{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[{"file_id":"1dneD0o5zf7OC4I-K4vr9XBmmX-E7IRtq","timestamp":1676999972013}],"authorship_tag":"ABX9TyM/qhS/P5919I+J5ijHQezT"},"kernelspec":{"name":"ir","display_name":"R"},"language_info":{"name":"R"},"gpuClass":"standard"},"cells":[{"cell_type":"markdown","source":["## **MULTIVARIATE ANALYSIS**\n","\n","Recopilación de Toni Monleón. Sólo con propósito docente.\n","\n","\n","\n","This lecture and examples are inspired in: https://little-book-of-r-for-multivariate-analysis.readthedocs.io/en/latest/src/multivariateanalysis.html\n","\n","\n","\n","\n","See bibliography in: https://uw.pressbooks.pub/appliedmultivariatestatistics/\n","\n","See Modern Statistics for Modern Biology (Multivariate analysis): https://www.huber.embl.de/msmb/07-chap.html\n","\n","----------\n"],"metadata":{"id":"2MyqVXWrweNk"}},{"cell_type":"code","source":["#Because in the multivariate analysis we have to work with numerous libraries, we will load them, while highlighting the theoretical explanation.\n","#run the libraries\n","install.packages(\"titanic\")\n","install.packages(\"cowplot\")\n","\n","install.packages(\"mice\")\n","\n","install.packages(\"car\")\n","\n","\n","install.packages(\"ggcorrplot\")\n","\n","install.packages(\"corrr\")\n","\n","\n","install.packages(\"tidyverse\")\n","install.packages(\"RNHANES\")\n","install.packages(\"ggplot2\")\n","\n","\n","install.packages(\"factoextra\")\n","\n","install.packages(\"dendextend\")\n","\n","install.packages(\"pheatmap\")\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"YGKoTehFkKkn","executionInfo":{"status":"ok","timestamp":1717494785265,"user_tz":-120,"elapsed":1146263,"user":{"displayName":"toni Monleon","userId":"05269440626861479105"}},"outputId":"c9bf928a-1916-423d-be34-05151b9df9cc"},"execution_count":2,"outputs":[{"output_type":"stream","name":"stderr","text":["Installing package into ‘/usr/local/lib/R/site-library’\n","(as ‘lib’ is unspecified)\n","\n","Installing package into ‘/usr/local/lib/R/site-library’\n","(as ‘lib’ is unspecified)\n","\n","Installing package into ‘/usr/local/lib/R/site-library’\n","(as ‘lib’ is unspecified)\n","\n","also installing the dependencies ‘minqa’, ‘nloptr’, ‘ucminf’, ‘numDeriv’, ‘iterators’, ‘lme4’, ‘ordinal’, ‘foreach’, ‘shape’, ‘RcppEigen’, ‘pan’, ‘jomo’, ‘glmnet’, ‘mitml’, ‘Rcpp’\n","\n","\n","Installing package into ‘/usr/local/lib/R/site-library’\n","(as ‘lib’ is unspecified)\n","\n","also installing the dependencies ‘SparseM’, ‘MatrixModels’, ‘carData’, ‘abind’, ‘pbkrtest’, ‘quantreg’\n","\n","\n","Installing package into ‘/usr/local/lib/R/site-library’\n","(as ‘lib’ is unspecified)\n","\n","also installing the dependencies ‘plyr’, ‘reshape2’\n","\n","\n","Installing package into ‘/usr/local/lib/R/site-library’\n","(as ‘lib’ is unspecified)\n","\n","also installing the dependencies ‘permute’, ‘ca’, ‘gclus’, ‘qap’, ‘registry’, ‘TSP’, ‘vegan’, ‘ggrepel’, ‘seriation’\n","\n","\n","Installing package into ‘/usr/local/lib/R/site-library’\n","(as ‘lib’ is unspecified)\n","\n","Installing package into ‘/usr/local/lib/R/site-library’\n","(as ‘lib’ is unspecified)\n","\n","also installing the dependencies ‘mitools’, ‘RcppArmadillo’, ‘survey’\n","\n","\n","Installing package into ‘/usr/local/lib/R/site-library’\n","(as ‘lib’ is unspecified)\n","\n","Installing package into ‘/usr/local/lib/R/site-library’\n","(as ‘lib’ is unspecified)\n","\n","also installing the dependencies ‘later’, ‘lazyeval’, ‘htmlwidgets’, ‘httpuv’, ‘crosstalk’, ‘promises’, ‘estimability’, ‘mvtnorm’, ‘corrplot’, ‘viridis’, ‘DT’, ‘ellipse’, ‘emmeans’, ‘flashClust’, ‘leaps’, ‘multcompView’, ‘scatterplot3d’, ‘ggsci’, ‘ggsignif’, ‘gridExtra’, ‘polynom’, ‘rstatix’, ‘dendextend’, ‘FactoMineR’, ‘ggpubr’\n","\n","\n","Installing package into ‘/usr/local/lib/R/site-library’\n","(as ‘lib’ is unspecified)\n","\n","Installing package into ‘/usr/local/lib/R/site-library’\n","(as ‘lib’ is unspecified)\n","\n"]}]},{"cell_type":"markdown","source":["The multivariate statistical analysis appears in many situations and is a very broad subject, in which new methods are emerging every time, like in bioinformatics:\n","\n","\n","https://www.sciencedirect.com/topics/neuroscience/multivariate-statistics\n","\n","\n","![](https://ars.els-cdn.com/content/image/3-s2.0-B9780128122938000013-f01-06-9780128122938.jpg)\n","\n","\n","In the examples in this booklet, I will be using data sets from the UCI Machine Learning Repository, http://archive.ics.uci.edu/ml.\n","\n","There is a pdf version of this booklet available at https://media.readthedocs.org/pdf/little-book-of-r-for-multivariate-analysis/latest/little-book-of-r-for-multivariate-analysis.pdf.\n","\n","\n","\n","\n","\n","![](https://fastercapital.com/i/Multivariate-analysis--Extending-Two-Way-ANOVA-to-Complex-Data-Sets--Advantages-of-Multivariate-Analysis.webp)"],"metadata":{"id":"zCp6FyIw4KVS"}},{"cell_type":"markdown","source":["\n","## What is a Multivariate analysis\n","\n","In data science in general, many variables of many different types appear, but what is multivariate data analysis? and what are its techniques?\n","\n","![](https://pbs.twimg.com/media/GJLIcKRWsAI4z3f.jpg)\n","\n","\n","\n","Multivariate statistics is a subdivision of statistics encompassing the simultaneous observation and analysis of more than one outcome variable, i.e., multivariate random variables.\n","\n","Multivariate statistics concerns understanding the different aims and background of each of the different forms of multivariate analysis, and how they relate to each other.\n","\n","The practical application of multivariate statistics to a particular problem may involve several types of univariate and multivariate analyses in order to understand the relationships between variables and their relevance to the problem being studied (See Wikipedia: https://en.wikipedia.org/wiki/Multivariate_statistics).\n","\n","\n","The statistical data analysis appears whenever we have many variables and data, and includes different steps and procedures:\n"],"metadata":{"id":"R7evEvDf2jG5"}},{"cell_type":"markdown","source":["## Types of multivariate analysis\n","\n","Multivariate analysis (MVA) is based on the principles of multivariate statistics. Typically, MVA is used to address situations where multiple measurements are made on each experimental unit and the relations among these measurements and their structures are important. A modern, overlapping categorization of MVA includes:\n","\n","* Normal and general multivariate models and distribution theory\n","* The study and measurement of relationships\n","* Probability computations of multidimensional regions\n","* The exploration of data structures and patterns.\n","* Other like:\n"," * Advanced data exploration and sidualization techniques\n"," * Missing value imputation techniques\n","\n","Simple scheme based on the number of dependent and independent variables:\n","\n","![](https://miro.medium.com/v2/resize:fit:1100/format:webp/1*vCYcPVR12KL-URGdwNY0WQ.png)\n","\n"],"metadata":{"id":"79cYEZUpvasa"}},{"cell_type":"markdown","source":["# Types of MVA analysis (Classification)\n","\n","In general, MVA techniques can be divided into: 1) predictive models, 2) visualization techniques, 3) identification techniques; but this is very generic and there are many techniques and applications that are currently linked to machine learning and techniques based on artificial intelligence.\n","\n","---\n","\n","\n","\n","\n","\n","\n","![](https://uw.pressbooks.pub/app/uploads/sites/641/2023/05/cover-683x1024.jpg)\n","\n","Many different techniques are used in MVA, each with its own type of analysis (see Wikipedia):\n","\n","* **Multivariate analysis of variance (MANOVA)** extends the analysis of variance to cover cases where there is more than one dependent variable to be analyzed simultaneously; see also Multivariate analysis of covariance (MANCOVA).\n","* **Multivariate regression** attempts to determine a formula that can describe how elements in a vector of variables respond simultaneously to changes in others. For linear relations, regression analyses here are based on forms of the general linear model. Some suggest that multivariate regression is distinct from multivariable regression, however, that is debated and not consistently true across scientific fields.\n","* **Principal components analysis (PCA)** creates a new set of orthogonal variables that contain the same information as the original set. It rotates the axes of variation to give a new set of orthogonal axes, ordered so that they summarize decreasing proportions of the variation.\n","* **Factor analysis** is similar to PCA but allows the user to extract a specified number of synthetic variables, fewer than the original set, leaving the remaining unexplained variation as error. The extracted variables are known as latent variables or factors; each one may be supposed to account for covariation in a group of observed variables.\n","* **Canonical correlation analysis** finds linear relationships among two sets of variables; it is the generalised (i.e. canonical) version of bivariate[3] correlation.\n","* **Redundancy analysis (RDA)** is similar to canonical correlation analysis but allows the user to derive a specified number of synthetic variables from one set of (independent) variables that explain as much variance as possible in another (independent) set. It is a multivariate analogue of regression.\n","* **Correspondence analysis (CA)**, or reciprocal averaging, finds (like PCA) a set of synthetic variables that summarise the original set. The underlying model assumes chi-squared dissimilarities among records (cases).\n","* **Canonical (or \"constrained\")** correspondence analysis (CCA) for summarising the joint variation in two sets of variables (like redundancy analysis); combination of correspondence analysis and multivariate regression analysis. The underlying model assumes chi-squared dissimilarities among records (cases).\n","* **Multidimensional scaling** comprises various algorithms to determine a set of synthetic variables that best represent the pairwise distances between records. The original method is principal coordinates analysis (PCoA; based on PCA).\n","* **Discriminant analysis**, or canonical variate analysis, attempts to establish whether a set of variables can be used to distinguish between two or more groups of cases.\n","* **Linear discriminant analysis (LDA)** computes a linear predictor from two sets of normally distributed data to allow for classification of new observations.\n","* **Clustering systems** assign objects into groups (called clusters) so that objects (cases) from the same cluster are more similar to each other than objects from different clusters.\n","* **Recursive partitioning** creates a decision tree that attempts to correctly classify members of the population based on a dichotomous dependent variable.\n","Artificial neural networks extend regression and clustering methods to non-linear multivariate models.\n","* **Statistical graphics** such as tours, parallel coordinate plots, scatterplot matrices can be used to explore multivariate data.\n","* **Simultaneous equations models** involve more than one regression equation, with different dependent variables, estimated together.\n","* **Vector autoregression** involves simultaneous regressions of various time series variables on their own and each other's lagged values.\n","* **Principal response curves analysis (PRC)** is a method based on RDA that allows the user to focus on treatment effects over time by correcting for changes in control treatments over time.\n","* **Iconography of correlations** consists in replacing a correlation matrix by a diagram where the “remarkable” correlations are represented by a solid line (positive correlation), or a dotted line (negative correlation)."],"metadata":{"id":"mqwkaxp9xLwi"}},{"cell_type":"markdown","source":["\n","It is not obvious or easy to know what type of technique or method to apply at any given time.\n","\n","![](https://www.ctanujit.org/uploads/2/5/3/9/25393293/ddb165d603c561e2eb4eecda30374285-removebg-preview_orig.png)\n","\n","\n"],"metadata":{"id":"FRBjEhx3UTD3"}},{"cell_type":"markdown","source":["# Chosing the Statistical Technique\n","\n","Choosing the right multivariate analysis technique is crucial to getting accurate and relevant results from your data set. Here are some key factors to consider:\n","\n","* **The nature of your variables**: Determine whether your variables are categorical or numerical. This will help you narrow down the techniques that are appropriate for your data. For instance, principal component analysis (PCA) is suitable for numerical variables, while correspondence analysis is designed for categorical variables.\n","\n","* **The objective of your analysis**: Are you interested in exploring the relationships between variables, identifying patterns and clusters in the data, or predicting an outcome? Different techniques are better suited for different objectives. For example, correlation analysis is useful for exploring relationships, while k-means clustering is effective for identifying groups in the data.\n","\n","* **The amount of data**: The amount of data you have available can also influence your choice of technique. Some techniques, such as PCA, require a large amount of data to work effectively, while others, such as linear regression, can be used with smaller datasets.\n","\n","Here's a table summarizing some common multivariate analysis techniques and their suitability based on variable type and analysis objective:\n","\n","* **Principal Component Analysis (PCA)**: This technique is suitable for numerical variables. It helps reduce the dimensionality of data by creating new composite variables (principal components) that capture most of the variance in the original data. This is useful for exploratory data analysis and visualization when dealing with many variables.\n","* **Correlation Analysis**: This technique is used with numerical variables. It measures the strength and direction of the linear relationship between two variables. This helps understand how changes in one variable might be associated with changes in another.\n","* **Linear Regression**: This technique is used for predictive modeling. It can handle a mix of variable types, with a numerical outcome variable and numerical or categorical predictor variables. Linear regression builds a model to predict the outcome variable based on the values of the predictor variables.\n","* **Correspondence Analysis**: This technique is designed for categorical\n","* **K-means Clustering**: This technique works with numerical variables. It groups data points into a predefined number of clusters based on their similarity. This is useful for identifying distinct groups or segments within the data.\n","variables. It helps explore relationships and identify patterns among categorical variables by creating a low-dimensional representation of the data that highlights these relationships.\n","\n","\n","# Summary of Multivariate Analysis Techniques: decide on which techniques to use, some schemes and ideas\n","\n","\n","\n","Remember that these are just general guidelines, and the specific technique you choose will depend on the specific characteristics of your data and your research question. It's always a good idea to consult with a statistician or data scientist if you are unsure which technique is best for your needs.\n","\n","\n","\n","![](https://qph.cf2.quoracdn.net/main-qimg-8fb49483af028e7b724a4e163acbfa8a-lq)\n","\n","\n","There are numerous techniques and methods of multivariate analysis:\n","\n","![](https://www.researchgate.net/publication/229555724/figure/fig3/AS:300858223939585@1448741598069/Multivariate-data-analysis-methods-used-in-modelling-hyperspectral-data-MLR-Multiple.png)\n","\n"],"metadata":{"id":"UtL8BcvFb0Z3"}},{"cell_type":"markdown","source":["\n","-------------------\n","--------------------\n"],"metadata":{"id":"CUwiq2L9n5lM"}},{"cell_type":"markdown","source":["# Multivariate Analysis and AI\n","\n","\n","![](https://assets.spe.org/dims4/default/18239e8/2147483647/strip/true/crop/1024x746+0+0/resize/800x583!/quality/90/?url=http%3A%2F%2Fspe-brightspot.s3.us-east-2.amazonaws.com%2F0a%2Ff1%2F45e8926dce176bdcd006b5b6e44b%2Fstatvsaiml-fig4.jpg)\n","\n","AI Integration: Empowering Multivariate Analysis\n","\n","The integration of AI has significantly enhanced multivariate analysis capabilities. AI algorithms can handle vast amounts of data, identify complex patterns, and adapt to non-linear relationships, making them well-suited for real-world applications.\n","\n","Multivariate data analysis techniques are clearly divided into supervised and unsupervised. Within the supervised we will address classification and regression problems, everything will depend on our objectives and the type of data we have (or can obtain)\n","\n","Multivariate analysis has undergone a remarkable transformation, integrating powerful AI-based techniques to address complex data challenges. This evolution has led to the emergence of two primary approaches: **supervised learning** and **unsupervised learning**.\n","\n","* **Supervised Learning**: Guided by Labeled Data\n","\n"," * Supervised learning algorithms leverage labeled data, where each data point is associated with a known outcome or target variable. These algorithms learn from these labeled examples to establish a mapping function that can predict the outcome for new, unlabeled data.\n","\n"," * Common supervised learning techniques include:\n","\n"," * Classification: Assigning data points to predefined categories, such as predicting whether an email is spam or not.\n"," * Regression: Estimating continuous numerical values, such as forecasting house prices based on various features.\n","* **Unsupervised Learning**: Unraveling Hidden Patterns\n","\n"," * In contrast, unsupervised learning deals with unlabeled data, where the true structure or patterns are unknown. The goal is to discover hidden relationships or groupings within the data without explicit guidance.\n","\n"," * Unsupervised learning techniques include:\n","\n"," * Clustering: Identifying groups of similar data points, such as segmenting customers based on their purchasing behavior.\n"," * Dimensionality Reduction: Reducing the number of variables while preserving the essential information, making data analysis more manageable.\n","\n","\n","\n","\n","\n","\n","![](https://cdn-images-1.medium.com/max/1200/1*U5GzWYAm8t1urqdDxpiOFw.jpeg)\n","\n","\n","In this notebook, we will first perform a regression example (supervised regression method), then we will use an unsupervised method, and finally a supervised method of classification."],"metadata":{"id":"EBPvC7LjgWik"}},{"cell_type":"markdown","source":["# Missing data & Imputation techniques\n","\n","Real-world data is often messy and full of missing values. As a result, data scientists spend the majority of their time cleaning and preparing the data, and have less time to focus on predictive modeling and machine learning. If there's one thing all data preparation steps share, then it's dealing with missing data. Today we'll make this process a bit easier for you by introducing 3 ways for data imputation in R. (See https://www.appsilon.com/post/imputation-in-r)\n","\n","Multivariate Analysis for Data Imputation: Unveiling Hidden Information\n","Missing data, a common challenge in data analysis, can hinder accurate and reliable conclusions. Multivariate analysis techniques, traditionally used to explore relationships and patterns in data, have emerged as valuable tools for addressing missing data issues, particularly through imputation methods.\n","\n","**Imputation: Filling the Gaps in Data**\n","\n","Data imputation aims to replace missing values with plausible estimates, restoring the completeness of the dataset. Multivariate analysis methods can effectively handle missing data due to their ability to consider the relationships and interdependencies among variables.\n","\n","* **Multivariate Techniques for Imputation**: Several multivariate analysis techniques are employed for data imputation:\n","\n"," * **Multiple Imputation (MI)**: MI generates multiple imputed datasets by considering the relationships between variables. This approach accounts for the uncertainty in the imputed values, providing more robust results.\n","\n"," * **Joint Modeling**: Joint modeling techniques, such as copula models, treat all variables simultaneously, incorporating their interdependencies to impute missing values. This method is particularly useful for non-linear relationships between variables.\n","\n"," * **Dimensionality Reduction**: Dimensionality reduction techniques, like Principal Component Analysis (PCA), can be used to reduce the number of variables, making imputation more computationally efficient and potentially improving accuracy.\n","\n"," In the simplest words, imputation represents a process of replacing missing or NA values of your dataset with values that can be processed, analyzed, or passed into a machine learning model. There are numerous ways to perform imputation in R programming language, and choosing the best one usually boils down to domain knowledge. Let' s see a simple example:"],"metadata":{"id":"VDgRn1KCy0Uq"}},{"cell_type":"code","source":["#case example with Titanic data (see in: https://www.appsilon.com/post/imputation-in-r)\n","\n","library(ggplot2)\n","library(dplyr)\n","#install.packages(\"titanic\")\n","library(titanic)\n","#install.packages(\"cowplot\")\n","library(cowplot)\n","\n","\n","#You can see some of the possible missing values below:\n","head(titanic_train)\n","print(\"Age-----------------------------\")\n","titanic_train$Age"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":807},"id":"0piYRi5mA4TC","executionInfo":{"status":"ok","timestamp":1717433037261,"user_tz":-120,"elapsed":14616,"user":{"displayName":"toni Monleon","userId":"05269440626861479105"}},"outputId":"894e0e6c-5a7f-4dfa-905f-0f0ac9e05fef"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["\n","Attaching package: ‘dplyr’\n","\n","\n","The following objects are masked from ‘package:stats’:\n","\n"," filter, lag\n","\n","\n","The following objects are masked from ‘package:base’:\n","\n"," intersect, setdiff, setequal, union\n","\n","\n","Installing package into ‘/usr/local/lib/R/site-library’\n","(as ‘lib’ is unspecified)\n","\n","Installing package into ‘/usr/local/lib/R/site-library’\n","(as ‘lib’ is unspecified)\n","\n"]},{"output_type":"display_data","data":{"text/html":["\n","\n","\n","\t\n","\t\n","\n","\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\n","
A data.frame: 6 × 12
PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarked
<int><int><int><chr><chr><dbl><int><int><chr><dbl><chr><chr>
1103Braund, Mr. Owen Harris male 2210A/5 21171 7.2500 S
2211Cumings, Mrs. John Bradley (Florence Briggs Thayer)female3810PC 17599 71.2833C85 C
3313Heikkinen, Miss. Laina female2600STON/O2. 3101282 7.9250 S
4411Futrelle, Mrs. Jacques Heath (Lily May Peel) female3510113803 53.1000C123S
5503Allen, Mr. William Henry male 3500373450 8.0500 S
6603Moran, Mr. James male NA00330877 8.4583 Q
\n"],"text/markdown":"\nA data.frame: 6 × 12\n\n| | PassengerId <int> | Survived <int> | Pclass <int> | Name <chr> | Sex <chr> | Age <dbl> | SibSp <int> | Parch <int> | Ticket <chr> | Fare <dbl> | Cabin <chr> | Embarked <chr> |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | 1 | 0 | 3 | Braund, Mr. Owen Harris | male | 22 | 1 | 0 | A/5 21171 | 7.2500 | | S |\n| 2 | 2 | 1 | 1 | Cumings, Mrs. John Bradley (Florence Briggs Thayer) | female | 38 | 1 | 0 | PC 17599 | 71.2833 | C85 | C |\n| 3 | 3 | 1 | 3 | Heikkinen, Miss. Laina | female | 26 | 0 | 0 | STON/O2. 3101282 | 7.9250 | | S |\n| 4 | 4 | 1 | 1 | Futrelle, Mrs. Jacques Heath (Lily May Peel) | female | 35 | 1 | 0 | 113803 | 53.1000 | C123 | S |\n| 5 | 5 | 0 | 3 | Allen, Mr. William Henry | male | 35 | 0 | 0 | 373450 | 8.0500 | | S |\n| 6 | 6 | 0 | 3 | Moran, Mr. James | male | NA | 0 | 0 | 330877 | 8.4583 | | Q |\n\n","text/latex":"A data.frame: 6 × 12\n\\begin{tabular}{r|llllllllllll}\n & PassengerId & Survived & Pclass & Name & Sex & Age & SibSp & Parch & Ticket & Fare & Cabin & Embarked\\\\\n & & & & & & & & & & & & \\\\\n\\hline\n\t1 & 1 & 0 & 3 & Braund, Mr. Owen Harris & male & 22 & 1 & 0 & A/5 21171 & 7.2500 & & S\\\\\n\t2 & 2 & 1 & 1 & Cumings, Mrs. John Bradley (Florence Briggs Thayer) & female & 38 & 1 & 0 & PC 17599 & 71.2833 & C85 & C\\\\\n\t3 & 3 & 1 & 3 & Heikkinen, Miss. Laina & female & 26 & 0 & 0 & STON/O2. 3101282 & 7.9250 & & S\\\\\n\t4 & 4 & 1 & 1 & Futrelle, Mrs. Jacques Heath (Lily May Peel) & female & 35 & 1 & 0 & 113803 & 53.1000 & C123 & S\\\\\n\t5 & 5 & 0 & 3 & Allen, Mr. William Henry & male & 35 & 0 & 0 & 373450 & 8.0500 & & S\\\\\n\t6 & 6 & 0 & 3 & Moran, Mr. James & male & NA & 0 & 0 & 330877 & 8.4583 & & Q\\\\\n\\end{tabular}\n","text/plain":[" PassengerId Survived Pclass\n","1 1 0 3 \n","2 2 1 1 \n","3 3 1 3 \n","4 4 1 1 \n","5 5 0 3 \n","6 6 0 3 \n"," Name Sex Age SibSp Parch\n","1 Braund, Mr. Owen Harris male 22 1 0 \n","2 Cumings, Mrs. John Bradley (Florence Briggs Thayer) female 38 1 0 \n","3 Heikkinen, Miss. Laina female 26 0 0 \n","4 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35 1 0 \n","5 Allen, Mr. William Henry male 35 0 0 \n","6 Moran, Mr. James male NA 0 0 \n"," Ticket Fare Cabin Embarked\n","1 A/5 21171 7.2500 S \n","2 PC 17599 71.2833 C85 C \n","3 STON/O2. 3101282 7.9250 S \n","4 113803 53.1000 C123 S \n","5 373450 8.0500 S \n","6 330877 8.4583 Q "]},"metadata":{}},{"output_type":"stream","name":"stdout","text":["[1] \"Age-----------------------------\"\n"]},{"output_type":"display_data","data":{"text/html":["\n","
  1. 22
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  17. 2
  18. <NA>
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  151. 51
  152. 22
  153. 55.5
  154. 40.5
  155. <NA>
  156. 51
  157. 16
  158. 30
  159. <NA>
  160. <NA>
  161. 44
  162. 40
  163. 26
  164. 17
  165. 1
  166. 9
  167. <NA>
  168. 45
  169. <NA>
  170. 28
  171. 61
  172. 4
  173. 1
  174. 21
  175. 56
  176. 18
  177. <NA>
  178. 50
  179. 30
  180. 36
  181. <NA>
  182. <NA>
  183. 9
  184. 1
  185. 4
  186. <NA>
  187. <NA>
  188. 45
  189. 40
  190. 36
  191. 32
  192. 19
  193. 19
  194. 3
  195. 44
  196. 58
  197. <NA>
  198. 42
  199. <NA>
  200. 24
  201. ⋯
  202. 4
  203. <NA>
  204. 25
  205. 60
  206. 52
  207. 44
  208. <NA>
  209. 49
  210. 42
  211. 18
  212. 35
  213. 18
  214. 25
  215. 26
  216. 39
  217. 45
  218. 42
  219. 22
  220. <NA>
  221. 24
  222. <NA>
  223. 48
  224. 29
  225. 52
  226. 19
  227. 38
  228. 27
  229. <NA>
  230. 33
  231. 6
  232. 17
  233. 34
  234. 50
  235. 27
  236. 20
  237. 30
  238. <NA>
  239. 25
  240. 25
  241. 29
  242. 11
  243. <NA>
  244. 23
  245. 23
  246. 28.5
  247. 48
  248. 35
  249. <NA>
  250. <NA>
  251. <NA>
  252. 36
  253. 21
  254. 24
  255. 31
  256. 70
  257. 16
  258. 30
  259. 19
  260. 31
  261. 4
  262. 6
  263. 33
  264. 23
  265. 48
  266. 0.67
  267. 28
  268. 18
  269. 34
  270. 33
  271. <NA>
  272. 41
  273. 20
  274. 36
  275. 16
  276. 51
  277. <NA>
  278. 30.5
  279. <NA>
  280. 32
  281. 24
  282. 48
  283. 57
  284. <NA>
  285. 54
  286. 18
  287. <NA>
  288. 5
  289. <NA>
  290. 43
  291. 13
  292. 17
  293. 29
  294. <NA>
  295. 25
  296. 25
  297. 18
  298. 8
  299. 1
  300. 46
  301. <NA>
  302. 16
  303. <NA>
  304. <NA>
  305. 25
  306. 39
  307. 49
  308. 31
  309. 30
  310. 30
  311. 34
  312. 31
  313. 11
  314. 0.42
  315. 27
  316. 31
  317. 39
  318. 18
  319. 39
  320. 33
  321. 26
  322. 39
  323. 35
  324. 6
  325. 30.5
  326. <NA>
  327. 23
  328. 31
  329. 43
  330. 10
  331. 52
  332. 27
  333. 38
  334. 27
  335. 2
  336. <NA>
  337. <NA>
  338. 1
  339. <NA>
  340. 62
  341. 15
  342. 0.83
  343. <NA>
  344. 23
  345. 18
  346. 39
  347. 21
  348. <NA>
  349. 32
  350. <NA>
  351. 20
  352. 16
  353. 30
  354. 34.5
  355. 17
  356. 42
  357. <NA>
  358. 35
  359. 28
  360. <NA>
  361. 4
  362. 74
  363. 9
  364. 16
  365. 44
  366. 18
  367. 45
  368. 51
  369. 24
  370. <NA>
  371. 41
  372. 21
  373. 48
  374. <NA>
  375. 24
  376. 42
  377. 27
  378. 31
  379. <NA>
  380. 4
  381. 26
  382. 47
  383. 33
  384. 47
  385. 28
  386. 15
  387. 20
  388. 19
  389. <NA>
  390. 56
  391. 25
  392. 33
  393. 22
  394. 28
  395. 25
  396. 39
  397. 27
  398. 19
  399. <NA>
  400. 26
  401. 32
\n"],"text/markdown":"1. 22\n2. 38\n3. 26\n4. 35\n5. 35\n6. <NA>\n7. 54\n8. 2\n9. 27\n10. 14\n11. 4\n12. 58\n13. 20\n14. 39\n15. 14\n16. 55\n17. 2\n18. <NA>\n19. 31\n20. <NA>\n21. 35\n22. 34\n23. 15\n24. 28\n25. 8\n26. 38\n27. <NA>\n28. 19\n29. <NA>\n30. <NA>\n31. 40\n32. <NA>\n33. <NA>\n34. 66\n35. 28\n36. 42\n37. <NA>\n38. 21\n39. 18\n40. 14\n41. 40\n42. 27\n43. <NA>\n44. 3\n45. 19\n46. <NA>\n47. <NA>\n48. <NA>\n49. <NA>\n50. 18\n51. 7\n52. 21\n53. 49\n54. 29\n55. 65\n56. <NA>\n57. 21\n58. 28.5\n59. 5\n60. 11\n61. 22\n62. 38\n63. 45\n64. 4\n65. <NA>\n66. <NA>\n67. 29\n68. 19\n69. 17\n70. 26\n71. 32\n72. 16\n73. 21\n74. 26\n75. 32\n76. 25\n77. <NA>\n78. <NA>\n79. 0.83\n80. 30\n81. 22\n82. 29\n83. <NA>\n84. 28\n85. 17\n86. 33\n87. 16\n88. <NA>\n89. 23\n90. 24\n91. 29\n92. 20\n93. 46\n94. 26\n95. 59\n96. <NA>\n97. 71\n98. 23\n99. 34\n100. 34\n101. 28\n102. <NA>\n103. 21\n104. 33\n105. 37\n106. 28\n107. 21\n108. <NA>\n109. 38\n110. <NA>\n111. 47\n112. 14.5\n113. 22\n114. 20\n115. 17\n116. 21\n117. 70.5\n118. 29\n119. 24\n120. 2\n121. 21\n122. <NA>\n123. 32.5\n124. 32.5\n125. 54\n126. 12\n127. <NA>\n128. 24\n129. <NA>\n130. 45\n131. 33\n132. 20\n133. 47\n134. 29\n135. 25\n136. 23\n137. 19\n138. 37\n139. 16\n140. 24\n141. <NA>\n142. 22\n143. 24\n144. 19\n145. 18\n146. 19\n147. 27\n148. 9\n149. 36.5\n150. 42\n151. 51\n152. 22\n153. 55.5\n154. 40.5\n155. <NA>\n156. 51\n157. 16\n158. 30\n159. <NA>\n160. <NA>\n161. 44\n162. 40\n163. 26\n164. 17\n165. 1\n166. 9\n167. <NA>\n168. 45\n169. <NA>\n170. 28\n171. 61\n172. 4\n173. 1\n174. 21\n175. 56\n176. 18\n177. <NA>\n178. 50\n179. 30\n180. 36\n181. <NA>\n182. <NA>\n183. 9\n184. 1\n185. 4\n186. <NA>\n187. <NA>\n188. 45\n189. 40\n190. 36\n191. 32\n192. 19\n193. 19\n194. 3\n195. 44\n196. 58\n197. <NA>\n198. 42\n199. <NA>\n200. 24\n201. ⋯\n202. 4\n203. <NA>\n204. 25\n205. 60\n206. 52\n207. 44\n208. <NA>\n209. 49\n210. 42\n211. 18\n212. 35\n213. 18\n214. 25\n215. 26\n216. 39\n217. 45\n218. 42\n219. 22\n220. <NA>\n221. 24\n222. <NA>\n223. 48\n224. 29\n225. 52\n226. 19\n227. 38\n228. 27\n229. <NA>\n230. 33\n231. 6\n232. 17\n233. 34\n234. 50\n235. 27\n236. 20\n237. 30\n238. <NA>\n239. 25\n240. 25\n241. 29\n242. 11\n243. <NA>\n244. 23\n245. 23\n246. 28.5\n247. 48\n248. 35\n249. <NA>\n250. <NA>\n251. <NA>\n252. 36\n253. 21\n254. 24\n255. 31\n256. 70\n257. 16\n258. 30\n259. 19\n260. 31\n261. 4\n262. 6\n263. 33\n264. 23\n265. 48\n266. 0.67\n267. 28\n268. 18\n269. 34\n270. 33\n271. <NA>\n272. 41\n273. 20\n274. 36\n275. 16\n276. 51\n277. <NA>\n278. 30.5\n279. <NA>\n280. 32\n281. 24\n282. 48\n283. 57\n284. <NA>\n285. 54\n286. 18\n287. <NA>\n288. 5\n289. <NA>\n290. 43\n291. 13\n292. 17\n293. 29\n294. <NA>\n295. 25\n296. 25\n297. 18\n298. 8\n299. 1\n300. 46\n301. <NA>\n302. 16\n303. <NA>\n304. <NA>\n305. 25\n306. 39\n307. 49\n308. 31\n309. 30\n310. 30\n311. 34\n312. 31\n313. 11\n314. 0.42\n315. 27\n316. 31\n317. 39\n318. 18\n319. 39\n320. 33\n321. 26\n322. 39\n323. 35\n324. 6\n325. 30.5\n326. <NA>\n327. 23\n328. 31\n329. 43\n330. 10\n331. 52\n332. 27\n333. 38\n334. 27\n335. 2\n336. <NA>\n337. <NA>\n338. 1\n339. <NA>\n340. 62\n341. 15\n342. 0.83\n343. <NA>\n344. 23\n345. 18\n346. 39\n347. 21\n348. <NA>\n349. 32\n350. <NA>\n351. 20\n352. 16\n353. 30\n354. 34.5\n355. 17\n356. 42\n357. <NA>\n358. 35\n359. 28\n360. <NA>\n361. 4\n362. 74\n363. 9\n364. 16\n365. 44\n366. 18\n367. 45\n368. 51\n369. 24\n370. <NA>\n371. 41\n372. 21\n373. 48\n374. <NA>\n375. 24\n376. 42\n377. 27\n378. 31\n379. <NA>\n380. 4\n381. 26\n382. 47\n383. 33\n384. 47\n385. 28\n386. 15\n387. 20\n388. 19\n389. <NA>\n390. 56\n391. 25\n392. 33\n393. 22\n394. 28\n395. 25\n396. 39\n397. 27\n398. 19\n399. <NA>\n400. 26\n401. 32\n\n\n","text/latex":"\\begin{enumerate*}\n\\item 22\n\\item 38\n\\item 26\n\\item 35\n\\item 35\n\\item \n\\item 54\n\\item 2\n\\item 27\n\\item 14\n\\item 4\n\\item 58\n\\item 20\n\\item 39\n\\item 14\n\\item 55\n\\item 2\n\\item \n\\item 31\n\\item \n\\item 35\n\\item 34\n\\item 15\n\\item 28\n\\item 8\n\\item 38\n\\item \n\\item 19\n\\item \n\\item \n\\item 40\n\\item \n\\item \n\\item 66\n\\item 28\n\\item 42\n\\item \n\\item 21\n\\item 18\n\\item 14\n\\item 40\n\\item 27\n\\item \n\\item 3\n\\item 19\n\\item \n\\item \n\\item \n\\item \n\\item 18\n\\item 7\n\\item 21\n\\item 49\n\\item 29\n\\item 65\n\\item \n\\item 21\n\\item 28.5\n\\item 5\n\\item 11\n\\item 22\n\\item 38\n\\item 45\n\\item 4\n\\item \n\\item \n\\item 29\n\\item 19\n\\item 17\n\\item 26\n\\item 32\n\\item 16\n\\item 21\n\\item 26\n\\item 32\n\\item 25\n\\item \n\\item \n\\item 0.83\n\\item 30\n\\item 22\n\\item 29\n\\item \n\\item 28\n\\item 17\n\\item 33\n\\item 16\n\\item \n\\item 23\n\\item 24\n\\item 29\n\\item 20\n\\item 46\n\\item 26\n\\item 59\n\\item \n\\item 71\n\\item 23\n\\item 34\n\\item 34\n\\item 28\n\\item \n\\item 21\n\\item 33\n\\item 37\n\\item 28\n\\item 21\n\\item \n\\item 38\n\\item \n\\item 47\n\\item 14.5\n\\item 22\n\\item 20\n\\item 17\n\\item 21\n\\item 70.5\n\\item 29\n\\item 24\n\\item 2\n\\item 21\n\\item \n\\item 32.5\n\\item 32.5\n\\item 54\n\\item 12\n\\item \n\\item 24\n\\item \n\\item 45\n\\item 33\n\\item 20\n\\item 47\n\\item 29\n\\item 25\n\\item 23\n\\item 19\n\\item 37\n\\item 16\n\\item 24\n\\item \n\\item 22\n\\item 24\n\\item 19\n\\item 18\n\\item 19\n\\item 27\n\\item 9\n\\item 36.5\n\\item 42\n\\item 51\n\\item 22\n\\item 55.5\n\\item 40.5\n\\item \n\\item 51\n\\item 16\n\\item 30\n\\item \n\\item \n\\item 44\n\\item 40\n\\item 26\n\\item 17\n\\item 1\n\\item 9\n\\item \n\\item 45\n\\item \n\\item 28\n\\item 61\n\\item 4\n\\item 1\n\\item 21\n\\item 56\n\\item 18\n\\item \n\\item 50\n\\item 30\n\\item 36\n\\item \n\\item \n\\item 9\n\\item 1\n\\item 4\n\\item \n\\item \n\\item 45\n\\item 40\n\\item 36\n\\item 32\n\\item 19\n\\item 19\n\\item 3\n\\item 44\n\\item 58\n\\item \n\\item 42\n\\item \n\\item 24\n\\item ⋯\n\\item 4\n\\item \n\\item 25\n\\item 60\n\\item 52\n\\item 44\n\\item \n\\item 49\n\\item 42\n\\item 18\n\\item 35\n\\item 18\n\\item 25\n\\item 26\n\\item 39\n\\item 45\n\\item 42\n\\item 22\n\\item \n\\item 24\n\\item \n\\item 48\n\\item 29\n\\item 52\n\\item 19\n\\item 38\n\\item 27\n\\item \n\\item 33\n\\item 6\n\\item 17\n\\item 34\n\\item 50\n\\item 27\n\\item 20\n\\item 30\n\\item \n\\item 25\n\\item 25\n\\item 29\n\\item 11\n\\item \n\\item 23\n\\item 23\n\\item 28.5\n\\item 48\n\\item 35\n\\item \n\\item \n\\item \n\\item 36\n\\item 21\n\\item 24\n\\item 31\n\\item 70\n\\item 16\n\\item 30\n\\item 19\n\\item 31\n\\item 4\n\\item 6\n\\item 33\n\\item 23\n\\item 48\n\\item 0.67\n\\item 28\n\\item 18\n\\item 34\n\\item 33\n\\item \n\\item 41\n\\item 20\n\\item 36\n\\item 16\n\\item 51\n\\item \n\\item 30.5\n\\item \n\\item 32\n\\item 24\n\\item 48\n\\item 57\n\\item \n\\item 54\n\\item 18\n\\item \n\\item 5\n\\item \n\\item 43\n\\item 13\n\\item 17\n\\item 29\n\\item \n\\item 25\n\\item 25\n\\item 18\n\\item 8\n\\item 1\n\\item 46\n\\item \n\\item 16\n\\item \n\\item \n\\item 25\n\\item 39\n\\item 49\n\\item 31\n\\item 30\n\\item 30\n\\item 34\n\\item 31\n\\item 11\n\\item 0.42\n\\item 27\n\\item 31\n\\item 39\n\\item 18\n\\item 39\n\\item 33\n\\item 26\n\\item 39\n\\item 35\n\\item 6\n\\item 30.5\n\\item \n\\item 23\n\\item 31\n\\item 43\n\\item 10\n\\item 52\n\\item 27\n\\item 38\n\\item 27\n\\item 2\n\\item \n\\item \n\\item 1\n\\item \n\\item 62\n\\item 15\n\\item 0.83\n\\item \n\\item 23\n\\item 18\n\\item 39\n\\item 21\n\\item \n\\item 32\n\\item \n\\item 20\n\\item 16\n\\item 30\n\\item 34.5\n\\item 17\n\\item 42\n\\item \n\\item 35\n\\item 28\n\\item \n\\item 4\n\\item 74\n\\item 9\n\\item 16\n\\item 44\n\\item 18\n\\item 45\n\\item 51\n\\item 24\n\\item \n\\item 41\n\\item 21\n\\item 48\n\\item \n\\item 24\n\\item 42\n\\item 27\n\\item 31\n\\item \n\\item 4\n\\item 26\n\\item 47\n\\item 33\n\\item 47\n\\item 28\n\\item 15\n\\item 20\n\\item 19\n\\item \n\\item 56\n\\item 25\n\\item 33\n\\item 22\n\\item 28\n\\item 25\n\\item 39\n\\item 27\n\\item 19\n\\item \n\\item 26\n\\item 32\n\\end{enumerate*}\n","text/plain":[" [1] 22.00 38.00 26.00 35.00 35.00 NA 54.00 2.00 27.00 14.00 4.00 58.00\n"," [13] 20.00 39.00 14.00 55.00 2.00 NA 31.00 NA 35.00 34.00 15.00 28.00\n"," [25] 8.00 38.00 NA 19.00 NA NA 40.00 NA NA 66.00 28.00 42.00\n"," [37] NA 21.00 18.00 14.00 40.00 27.00 NA 3.00 19.00 NA NA NA\n"," [49] NA 18.00 7.00 21.00 49.00 29.00 65.00 NA 21.00 28.50 5.00 11.00\n"," [61] 22.00 38.00 45.00 4.00 NA NA 29.00 19.00 17.00 26.00 32.00 16.00\n"," [73] 21.00 26.00 32.00 25.00 NA NA 0.83 30.00 22.00 29.00 NA 28.00\n"," [85] 17.00 33.00 16.00 NA 23.00 24.00 29.00 20.00 46.00 26.00 59.00 NA\n"," [97] 71.00 23.00 34.00 34.00 28.00 NA 21.00 33.00 37.00 28.00 21.00 NA\n","[109] 38.00 NA 47.00 14.50 22.00 20.00 17.00 21.00 70.50 29.00 24.00 2.00\n","[121] 21.00 NA 32.50 32.50 54.00 12.00 NA 24.00 NA 45.00 33.00 20.00\n","[133] 47.00 29.00 25.00 23.00 19.00 37.00 16.00 24.00 NA 22.00 24.00 19.00\n","[145] 18.00 19.00 27.00 9.00 36.50 42.00 51.00 22.00 55.50 40.50 NA 51.00\n","[157] 16.00 30.00 NA NA 44.00 40.00 26.00 17.00 1.00 9.00 NA 45.00\n","[169] NA 28.00 61.00 4.00 1.00 21.00 56.00 18.00 NA 50.00 30.00 36.00\n","[181] NA NA 9.00 1.00 4.00 NA NA 45.00 40.00 36.00 32.00 19.00\n","[193] 19.00 3.00 44.00 58.00 NA 42.00 NA 24.00 28.00 NA 34.00 45.50\n","[205] 18.00 2.00 32.00 26.00 16.00 40.00 24.00 35.00 22.00 30.00 NA 31.00\n","[217] 27.00 42.00 32.00 30.00 16.00 27.00 51.00 NA 38.00 22.00 19.00 20.50\n","[229] 18.00 NA 35.00 29.00 59.00 5.00 24.00 NA 44.00 8.00 19.00 33.00\n","[241] NA NA 29.00 22.00 30.00 44.00 25.00 24.00 37.00 54.00 NA 29.00\n","[253] 62.00 30.00 41.00 29.00 NA 30.00 35.00 50.00 NA 3.00 52.00 40.00\n","[265] NA 36.00 16.00 25.00 58.00 35.00 NA 25.00 41.00 37.00 NA 63.00\n","[277] 45.00 NA 7.00 35.00 65.00 28.00 16.00 19.00 NA 33.00 30.00 22.00\n","[289] 42.00 22.00 26.00 19.00 36.00 24.00 24.00 NA 23.50 2.00 NA 50.00\n","[301] NA NA 19.00 NA NA 0.92 NA 17.00 30.00 30.00 24.00 18.00\n","[313] 26.00 28.00 43.00 26.00 24.00 54.00 31.00 40.00 22.00 27.00 30.00 22.00\n","[325] NA 36.00 61.00 36.00 31.00 16.00 NA 45.50 38.00 16.00 NA NA\n","[337] 29.00 41.00 45.00 45.00 2.00 24.00 28.00 25.00 36.00 24.00 40.00 NA\n","[349] 3.00 42.00 23.00 NA 15.00 25.00 NA 28.00 22.00 38.00 NA NA\n","[361] 40.00 29.00 45.00 35.00 NA 30.00 60.00 NA NA 24.00 25.00 18.00\n","[373] 19.00 22.00 3.00 NA 22.00 27.00 20.00 19.00 42.00 1.00 32.00 35.00\n","[385] NA 18.00 1.00 36.00 NA 17.00 36.00 21.00 28.00 23.00 24.00 22.00\n","[397] 31.00 46.00 23.00 28.00 39.00 26.00 21.00 28.00 20.00 34.00 51.00 3.00\n","[409] 21.00 NA NA NA 33.00 NA 44.00 NA 34.00 18.00 30.00 10.00\n","[421] NA 21.00 29.00 28.00 18.00 NA 28.00 19.00 NA 32.00 28.00 NA\n","[433] 42.00 17.00 50.00 14.00 21.00 24.00 64.00 31.00 45.00 20.00 25.00 28.00\n","[445] NA 4.00 13.00 34.00 5.00 52.00 36.00 NA 30.00 49.00 NA 29.00\n","[457] 65.00 NA 50.00 NA 48.00 34.00 47.00 48.00 NA 38.00 NA 56.00\n","[469] NA 0.75 NA 38.00 33.00 23.00 22.00 NA 34.00 29.00 22.00 2.00\n","[481] 9.00 NA 50.00 63.00 25.00 NA 35.00 58.00 30.00 9.00 NA 21.00\n","[493] 55.00 71.00 21.00 NA 54.00 NA 25.00 24.00 17.00 21.00 NA 37.00\n","[505] 16.00 18.00 33.00 NA 28.00 26.00 29.00 NA 36.00 54.00 24.00 47.00\n","[517] 34.00 NA 36.00 32.00 30.00 22.00 NA 44.00 NA 40.50 50.00 NA\n","[529] 39.00 23.00 2.00 NA 17.00 NA 30.00 7.00 45.00 30.00 NA 22.00\n","[541] 36.00 9.00 11.00 32.00 50.00 64.00 19.00 NA 33.00 8.00 17.00 27.00\n","[553] NA 22.00 22.00 62.00 48.00 NA 39.00 36.00 NA 40.00 28.00 NA\n","[565] NA 24.00 19.00 29.00 NA 32.00 62.00 53.00 36.00 NA 16.00 19.00\n","[577] 34.00 39.00 NA 32.00 25.00 39.00 54.00 36.00 NA 18.00 47.00 60.00\n","[589] 22.00 NA 35.00 52.00 47.00 NA 37.00 36.00 NA 49.00 NA 49.00\n","[601] 24.00 NA NA 44.00 35.00 36.00 30.00 27.00 22.00 40.00 39.00 NA\n","[613] NA NA 35.00 24.00 34.00 26.00 4.00 26.00 27.00 42.00 20.00 21.00\n","[625] 21.00 61.00 57.00 21.00 26.00 NA 80.00 51.00 32.00 NA 9.00 28.00\n","[637] 32.00 31.00 41.00 NA 20.00 24.00 2.00 NA 0.75 48.00 19.00 56.00\n","[649] NA 23.00 NA 18.00 21.00 NA 18.00 24.00 NA 32.00 23.00 58.00\n","[661] 50.00 40.00 47.00 36.00 20.00 32.00 25.00 NA 43.00 NA 40.00 31.00\n","[673] 70.00 31.00 NA 18.00 24.50 18.00 43.00 36.00 NA 27.00 20.00 14.00\n","[685] 60.00 25.00 14.00 19.00 18.00 15.00 31.00 4.00 NA 25.00 60.00 52.00\n","[697] 44.00 NA 49.00 42.00 18.00 35.00 18.00 25.00 26.00 39.00 45.00 42.00\n","[709] 22.00 NA 24.00 NA 48.00 29.00 52.00 19.00 38.00 27.00 NA 33.00\n","[721] 6.00 17.00 34.00 50.00 27.00 20.00 30.00 NA 25.00 25.00 29.00 11.00\n","[733] NA 23.00 23.00 28.50 48.00 35.00 NA NA NA 36.00 21.00 24.00\n","[745] 31.00 70.00 16.00 30.00 19.00 31.00 4.00 6.00 33.00 23.00 48.00 0.67\n","[757] 28.00 18.00 34.00 33.00 NA 41.00 20.00 36.00 16.00 51.00 NA 30.50\n","[769] NA 32.00 24.00 48.00 57.00 NA 54.00 18.00 NA 5.00 NA 43.00\n","[781] 13.00 17.00 29.00 NA 25.00 25.00 18.00 8.00 1.00 46.00 NA 16.00\n","[793] NA NA 25.00 39.00 49.00 31.00 30.00 30.00 34.00 31.00 11.00 0.42\n","[805] 27.00 31.00 39.00 18.00 39.00 33.00 26.00 39.00 35.00 6.00 30.50 NA\n","[817] 23.00 31.00 43.00 10.00 52.00 27.00 38.00 27.00 2.00 NA NA 1.00\n","[829] NA 62.00 15.00 0.83 NA 23.00 18.00 39.00 21.00 NA 32.00 NA\n","[841] 20.00 16.00 30.00 34.50 17.00 42.00 NA 35.00 28.00 NA 4.00 74.00\n","[853] 9.00 16.00 44.00 18.00 45.00 51.00 24.00 NA 41.00 21.00 48.00 NA\n","[865] 24.00 42.00 27.00 31.00 NA 4.00 26.00 47.00 33.00 47.00 28.00 15.00\n","[877] 20.00 19.00 NA 56.00 25.00 33.00 22.00 28.00 25.00 39.00 27.00 19.00\n","[889] NA 26.00 32.00"]},"metadata":{}}]},{"cell_type":"markdown","source":["There's a fair amount of NA values, and it's our job to impute them. They're most likely missing because the creator of the dataset had no information on the person's age. If you were to build a machine learning model on this dataset, the best way to evaluate the imputation technique would be to measure classification metrics (accuracy, precision, recall, f1) after training the model."],"metadata":{"id":"vjMI5wm3DiYp"}},{"cell_type":"code","source":["#But before diving into the imputation, let's visualize the distribution of our variable:\n","\n","ggplot(titanic_train, aes(Age)) +\n"," geom_histogram(color = \"#000000\", fill = \"#0099F8\") +\n"," ggtitle(\"Variable distribution\") +\n"," theme_classic() +\n"," theme(plot.title = element_text(size = 18))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":489},"id":"Qfsw5WwjDjhB","executionInfo":{"status":"ok","timestamp":1717433962866,"user_tz":-120,"elapsed":871,"user":{"displayName":"toni Monleon","userId":"05269440626861479105"}},"outputId":"e8c293f2-b0c7-47fd-b06a-12b8a4def25a"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["\u001b[1m\u001b[22m`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.\n","Warning message:\n","“\u001b[1m\u001b[22mRemoved 177 rows containing non-finite values (`stat_bin()`).”\n"]},{"output_type":"display_data","data":{"text/plain":["plot without 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AIBLCDgAg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\nwAEHHHnkkemYCUiJsANortYuDyFMmDA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new40EyUlJYWFhccee2x2dnbr1q1POumkjRs3rly5sok/AYTdji1durRly5Z9+vRJfJuTk9Or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"},"metadata":{"image/png":{"width":420,"height":420}}}]},{"cell_type":"markdown","source":["So, why is this important? It's a good idea to compare variable distribution before and after imputation. You don't want the distribution to change significantly, and a histogram is a good way to check that."],"metadata":{"id":"2mg1ZGmcDu_C"}},{"cell_type":"markdown","source":["**Simple Value Imputation in R with Built-in Functions**\n","\n","You don't actually need an R package to impute missing values. You can do the whole thing manually, provided the imputation techniques are simple. We'll cover constant, mean, and median imputations in this section and compare the results.\n","\n","Zero: constant imputation, feel free to change the value.\n","Mean (average): average age after when all NA's are removed.\n","Median: median age after when all NA's are removed."],"metadata":{"id":"jxdHX-hID5zZ"}},{"cell_type":"code","source":["value_imputed <- data.frame(\n"," original = titanic_train$Age,\n"," imputed_zero = replace(titanic_train$Age, is.na(titanic_train$Age), 0),\n"," imputed_mean = replace(titanic_train$Age, is.na(titanic_train$Age), mean(titanic_train$Age, na.rm = TRUE)),\n"," imputed_median = replace(titanic_train$Age, is.na(titanic_train$Age), median(titanic_train$Age, na.rm = TRUE))\n",")\n","value_imputed"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"ugd_Xx2oDv2x","executionInfo":{"status":"ok","timestamp":1717433966139,"user_tz":-120,"elapsed":443,"user":{"displayName":"toni Monleon","userId":"05269440626861479105"}},"outputId":"3c784a80-ffb4-4352-b8de-d9abf8b065a8"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/html":["\n","\n","\n","\t\n","\t\n","\n","\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\n","
A data.frame: 891 × 4
originalimputed_zeroimputed_meanimputed_median
<dbl><dbl><dbl><dbl>
222222.0000022
383838.0000038
262626.0000026
353535.0000035
353535.0000035
NA 029.6991228
545454.0000054
2 2 2.00000 2
272727.0000027
141414.0000014
4 4 4.00000 4
585858.0000058
202020.0000020
393939.0000039
141414.0000014
555555.0000055
2 2 2.00000 2
NA 029.6991228
313131.0000031
NA 029.6991228
353535.0000035
343434.0000034
151515.0000015
282828.0000028
8 8 8.00000 8
383838.0000038
NA 029.6991228
191919.0000019
NA 029.6991228
NA 029.6991228
⋮⋮⋮⋮
212121.0000021
484848.0000048
NA 029.6991228
242424.0000024
424242.0000042
272727.0000027
313131.0000031
NA 029.6991228
4 4 4.00000 4
262626.0000026
474747.0000047
333333.0000033
474747.0000047
282828.0000028
151515.0000015
202020.0000020
191919.0000019
NA 029.6991228
565656.0000056
252525.0000025
333333.0000033
222222.0000022
282828.0000028
252525.0000025
393939.0000039
272727.0000027
191919.0000019
NA 029.6991228
262626.0000026
323232.0000032
\n"],"text/markdown":"\nA data.frame: 891 × 4\n\n| original <dbl> | imputed_zero <dbl> | imputed_mean <dbl> | imputed_median <dbl> |\n|---|---|---|---|\n| 22 | 22 | 22.00000 | 22 |\n| 38 | 38 | 38.00000 | 38 |\n| 26 | 26 | 26.00000 | 26 |\n| 35 | 35 | 35.00000 | 35 |\n| 35 | 35 | 35.00000 | 35 |\n| NA | 0 | 29.69912 | 28 |\n| 54 | 54 | 54.00000 | 54 |\n| 2 | 2 | 2.00000 | 2 |\n| 27 | 27 | 27.00000 | 27 |\n| 14 | 14 | 14.00000 | 14 |\n| 4 | 4 | 4.00000 | 4 |\n| 58 | 58 | 58.00000 | 58 |\n| 20 | 20 | 20.00000 | 20 |\n| 39 | 39 | 39.00000 | 39 |\n| 14 | 14 | 14.00000 | 14 |\n| 55 | 55 | 55.00000 | 55 |\n| 2 | 2 | 2.00000 | 2 |\n| NA | 0 | 29.69912 | 28 |\n| 31 | 31 | 31.00000 | 31 |\n| NA | 0 | 29.69912 | 28 |\n| 35 | 35 | 35.00000 | 35 |\n| 34 | 34 | 34.00000 | 34 |\n| 15 | 15 | 15.00000 | 15 |\n| 28 | 28 | 28.00000 | 28 |\n| 8 | 8 | 8.00000 | 8 |\n| 38 | 38 | 38.00000 | 38 |\n| NA | 0 | 29.69912 | 28 |\n| 19 | 19 | 19.00000 | 19 |\n| NA | 0 | 29.69912 | 28 |\n| NA | 0 | 29.69912 | 28 |\n| ⋮ | ⋮ | ⋮ | ⋮ |\n| 21 | 21 | 21.00000 | 21 |\n| 48 | 48 | 48.00000 | 48 |\n| NA | 0 | 29.69912 | 28 |\n| 24 | 24 | 24.00000 | 24 |\n| 42 | 42 | 42.00000 | 42 |\n| 27 | 27 | 27.00000 | 27 |\n| 31 | 31 | 31.00000 | 31 |\n| NA | 0 | 29.69912 | 28 |\n| 4 | 4 | 4.00000 | 4 |\n| 26 | 26 | 26.00000 | 26 |\n| 47 | 47 | 47.00000 | 47 |\n| 33 | 33 | 33.00000 | 33 |\n| 47 | 47 | 47.00000 | 47 |\n| 28 | 28 | 28.00000 | 28 |\n| 15 | 15 | 15.00000 | 15 |\n| 20 | 20 | 20.00000 | 20 |\n| 19 | 19 | 19.00000 | 19 |\n| NA | 0 | 29.69912 | 28 |\n| 56 | 56 | 56.00000 | 56 |\n| 25 | 25 | 25.00000 | 25 |\n| 33 | 33 | 33.00000 | 33 |\n| 22 | 22 | 22.00000 | 22 |\n| 28 | 28 | 28.00000 | 28 |\n| 25 | 25 | 25.00000 | 25 |\n| 39 | 39 | 39.00000 | 39 |\n| 27 | 27 | 27.00000 | 27 |\n| 19 | 19 | 19.00000 | 19 |\n| NA | 0 | 29.69912 | 28 |\n| 26 | 26 | 26.00000 | 26 |\n| 32 | 32 | 32.00000 | 32 |\n\n","text/latex":"A data.frame: 891 × 4\n\\begin{tabular}{llll}\n original & imputed\\_zero & imputed\\_mean & imputed\\_median\\\\\n & & & \\\\\n\\hline\n\t 22 & 22 & 22.00000 & 22\\\\\n\t 38 & 38 & 38.00000 & 38\\\\\n\t 26 & 26 & 26.00000 & 26\\\\\n\t 35 & 35 & 35.00000 & 35\\\\\n\t 35 & 35 & 35.00000 & 35\\\\\n\t NA & 0 & 29.69912 & 28\\\\\n\t 54 & 54 & 54.00000 & 54\\\\\n\t 2 & 2 & 2.00000 & 2\\\\\n\t 27 & 27 & 27.00000 & 27\\\\\n\t 14 & 14 & 14.00000 & 14\\\\\n\t 4 & 4 & 4.00000 & 4\\\\\n\t 58 & 58 & 58.00000 & 58\\\\\n\t 20 & 20 & 20.00000 & 20\\\\\n\t 39 & 39 & 39.00000 & 39\\\\\n\t 14 & 14 & 14.00000 & 14\\\\\n\t 55 & 55 & 55.00000 & 55\\\\\n\t 2 & 2 & 2.00000 & 2\\\\\n\t NA & 0 & 29.69912 & 28\\\\\n\t 31 & 31 & 31.00000 & 31\\\\\n\t NA & 0 & 29.69912 & 28\\\\\n\t 35 & 35 & 35.00000 & 35\\\\\n\t 34 & 34 & 34.00000 & 34\\\\\n\t 15 & 15 & 15.00000 & 15\\\\\n\t 28 & 28 & 28.00000 & 28\\\\\n\t 8 & 8 & 8.00000 & 8\\\\\n\t 38 & 38 & 38.00000 & 38\\\\\n\t NA & 0 & 29.69912 & 28\\\\\n\t 19 & 19 & 19.00000 & 19\\\\\n\t NA & 0 & 29.69912 & 28\\\\\n\t NA & 0 & 29.69912 & 28\\\\\n\t ⋮ & ⋮ & ⋮ & ⋮\\\\\n\t 21 & 21 & 21.00000 & 21\\\\\n\t 48 & 48 & 48.00000 & 48\\\\\n\t NA & 0 & 29.69912 & 28\\\\\n\t 24 & 24 & 24.00000 & 24\\\\\n\t 42 & 42 & 42.00000 & 42\\\\\n\t 27 & 27 & 27.00000 & 27\\\\\n\t 31 & 31 & 31.00000 & 31\\\\\n\t NA & 0 & 29.69912 & 28\\\\\n\t 4 & 4 & 4.00000 & 4\\\\\n\t 26 & 26 & 26.00000 & 26\\\\\n\t 47 & 47 & 47.00000 & 47\\\\\n\t 33 & 33 & 33.00000 & 33\\\\\n\t 47 & 47 & 47.00000 & 47\\\\\n\t 28 & 28 & 28.00000 & 28\\\\\n\t 15 & 15 & 15.00000 & 15\\\\\n\t 20 & 20 & 20.00000 & 20\\\\\n\t 19 & 19 & 19.00000 & 19\\\\\n\t NA & 0 & 29.69912 & 28\\\\\n\t 56 & 56 & 56.00000 & 56\\\\\n\t 25 & 25 & 25.00000 & 25\\\\\n\t 33 & 33 & 33.00000 & 33\\\\\n\t 22 & 22 & 22.00000 & 22\\\\\n\t 28 & 28 & 28.00000 & 28\\\\\n\t 25 & 25 & 25.00000 & 25\\\\\n\t 39 & 39 & 39.00000 & 39\\\\\n\t 27 & 27 & 27.00000 & 27\\\\\n\t 19 & 19 & 19.00000 & 19\\\\\n\t NA & 0 & 29.69912 & 28\\\\\n\t 26 & 26 & 26.00000 & 26\\\\\n\t 32 & 32 & 32.00000 & 32\\\\\n\\end{tabular}\n","text/plain":[" original imputed_zero imputed_mean imputed_median\n","1 22 22 22.00000 22 \n","2 38 38 38.00000 38 \n","3 26 26 26.00000 26 \n","4 35 35 35.00000 35 \n","5 35 35 35.00000 35 \n","6 NA 0 29.69912 28 \n","7 54 54 54.00000 54 \n","8 2 2 2.00000 2 \n","9 27 27 27.00000 27 \n","10 14 14 14.00000 14 \n","11 4 4 4.00000 4 \n","12 58 58 58.00000 58 \n","13 20 20 20.00000 20 \n","14 39 39 39.00000 39 \n","15 14 14 14.00000 14 \n","16 55 55 55.00000 55 \n","17 2 2 2.00000 2 \n","18 NA 0 29.69912 28 \n","19 31 31 31.00000 31 \n","20 NA 0 29.69912 28 \n","21 35 35 35.00000 35 \n","22 34 34 34.00000 34 \n","23 15 15 15.00000 15 \n","24 28 28 28.00000 28 \n","25 8 8 8.00000 8 \n","26 38 38 38.00000 38 \n","27 NA 0 29.69912 28 \n","28 19 19 19.00000 19 \n","29 NA 0 29.69912 28 \n","30 NA 0 29.69912 28 \n","⋮ ⋮ ⋮ ⋮ ⋮ \n","862 21 21 21.00000 21 \n","863 48 48 48.00000 48 \n","864 NA 0 29.69912 28 \n","865 24 24 24.00000 24 \n","866 42 42 42.00000 42 \n","867 27 27 27.00000 27 \n","868 31 31 31.00000 31 \n","869 NA 0 29.69912 28 \n","870 4 4 4.00000 4 \n","871 26 26 26.00000 26 \n","872 47 47 47.00000 47 \n","873 33 33 33.00000 33 \n","874 47 47 47.00000 47 \n","875 28 28 28.00000 28 \n","876 15 15 15.00000 15 \n","877 20 20 20.00000 20 \n","878 19 19 19.00000 19 \n","879 NA 0 29.69912 28 \n","880 56 56 56.00000 56 \n","881 25 25 25.00000 25 \n","882 33 33 33.00000 33 \n","883 22 22 22.00000 22 \n","884 28 28 28.00000 28 \n","885 25 25 25.00000 25 \n","886 39 39 39.00000 39 \n","887 27 27 27.00000 27 \n","888 19 19 19.00000 19 \n","889 NA 0 29.69912 28 \n","890 26 26 26.00000 26 \n","891 32 32 32.00000 32 "]},"metadata":{}}]},{"cell_type":"code","source":["#Let's take a look at the variable distribution changes introduced by imputation on a 2x2 grid of histograms:\n","\n","h1 <- ggplot(value_imputed, aes(x = original)) +\n"," geom_histogram(fill = \"#ad1538\", color = \"#000000\", position = \"identity\") +\n"," ggtitle(\"Original distribution\") +\n"," theme_classic()\n","h2 <- ggplot(value_imputed, aes(x = imputed_zero)) +\n"," geom_histogram(fill = \"#15ad4f\", color = \"#000000\", position = \"identity\") +\n"," ggtitle(\"Zero-imputed distribution\") +\n"," theme_classic()\n","h3 <- ggplot(value_imputed, aes(x = imputed_mean)) +\n"," geom_histogram(fill = \"#1543ad\", color = \"#000000\", position = \"identity\") +\n"," ggtitle(\"Mean-imputed distribution\") +\n"," theme_classic()\n","h4 <- ggplot(value_imputed, aes(x = imputed_median)) +\n"," geom_histogram(fill = \"#ad8415\", color = \"#000000\", position = \"identity\") +\n"," ggtitle(\"Median-imputed distribution\") +\n"," theme_classic()\n","\n","plot_grid(h1, h2, h3, h4, nrow = 2, ncol = 2)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":541},"id":"4dX-DSgXEIgY","executionInfo":{"status":"ok","timestamp":1717433972268,"user_tz":-120,"elapsed":1696,"user":{"displayName":"toni Monleon","userId":"05269440626861479105"}},"outputId":"8c656760-c677-46f7-d12a-3c310f1d96ff"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["\u001b[1m\u001b[22m`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.\n","Warning message:\n","“\u001b[1m\u001b[22mRemoved 177 rows containing non-finite values (`stat_bin()`).”\n","\u001b[1m\u001b[22m`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.\n","\u001b[1m\u001b[22m`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.\n","\u001b[1m\u001b[22m`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.\n"]},{"output_type":"display_data","data":{"text/plain":["plot without 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QU82syZM/fu3TtmzBjpUu22SB+iBQsWSAdRJHv37m3atKl0ZqL9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\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MCBer1earHZbLNnzxZCvPjii1LLZ599JoQYMmSIxWKRWmpqanr27CmE2LZtm9Sy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\nAAAAgBFo7AAAAAAY8X+dVuITE8TUHwAAAABJRU5ErkJggg=="},"metadata":{"image/png":{"width":420,"height":420}}}]},{"cell_type":"markdown","source":["All imputation methods severely impact the distribution. There are a lot of missing values, so setting a single constant value doesn't make much sense. Zero imputation is the worst, as it's highly unlikely for close to 200 passengers to have the age of zero.\n","\n","Maybe mode imputation would provide better results, but we'll leave that up to you."],"metadata":{"id":"WTsmJlTWEX-i"}},{"cell_type":"markdown","source":["**Impute Missing Values in R with MICE**\n","\n","MICE stands for Multivariate Imputation via Chained Equations, and it's one of the most common packages for R users. It assumes the missing values are missing at random (MAR).\n","\n","The basic idea behind the algorithm is to treat each variable that has missing values as a dependent variable in regression and treat the others as independent (predictors)."],"metadata":{"id":"O5rgI-BvEc7S"}},{"cell_type":"code","source":["#install.packages(\"mice\")\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"q3ZlqoesEYyB","executionInfo":{"status":"ok","timestamp":1717434352967,"user_tz":-120,"elapsed":375046,"user":{"displayName":"toni Monleon","userId":"05269440626861479105"}},"outputId":"931e445b-5c7d-4586-996c-7170166fcbcb"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["Installing package into ‘/usr/local/lib/R/site-library’\n","(as ‘lib’ is unspecified)\n","\n","also installing the dependencies ‘minqa’, ‘nloptr’, ‘ucminf’, ‘numDeriv’, ‘iterators’, ‘lme4’, ‘ordinal’, ‘foreach’, ‘shape’, ‘RcppEigen’, ‘pan’, ‘jomo’, ‘glmnet’, ‘mitml’, ‘Rcpp’\n","\n","\n","Warning message in install.packages(\"mice\"):\n","“installation of package ‘glmnet’ had non-zero exit status”\n","Warning message in install.packages(\"mice\"):\n","“installation of package ‘jomo’ had non-zero exit status”\n","Warning message in install.packages(\"mice\"):\n","“installation of package ‘mitml’ had non-zero exit status”\n","Warning message in install.packages(\"mice\"):\n","“installation of package ‘mice’ had non-zero exit status”\n"]}]},{"cell_type":"code","source":["library(mice)\n","\n","titanic_numeric <- titanic_train %>%\n"," select(Survived, Pclass, SibSp, Parch, Age) #only select these variables\n","\n","#df dimension\n","print(\"Dimension----------------\")\n","dim(titanic_numeric)\n","\n","\n","\n","#To calculate the number of missing values in every column. We use colSums() function. This returns the count of missing values w.r.t each column.\n","print(\"Missing data----------------\")\n","colSums(is.na(titanic_numeric))\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":106},"id":"ygcdZmjHHPFq","executionInfo":{"status":"error","timestamp":1717434358215,"user_tz":-120,"elapsed":420,"user":{"displayName":"toni Monleon","userId":"05269440626861479105"}},"outputId":"5b122747-44a0-4303-c748-e74d1dc0fdfb"},"execution_count":null,"outputs":[{"output_type":"error","ename":"ERROR","evalue":"Error in library(mice): there is no package called ‘mice’\n","traceback":["Error in library(mice): there is no package called ‘mice’\nTraceback:\n","1. library(mice)"]}]},{"cell_type":"code","source":["#The md.pattern() function gives us a visual representation of missing values:\n","print(\"missing data with md.pattern----------------\")\n","md.pattern(titanic_numeric)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":598},"id":"NQS4G4R4Ixpn","executionInfo":{"status":"ok","timestamp":1717351688055,"user_tz":-120,"elapsed":291,"user":{"displayName":"toni Monleon","userId":"05269440626861479105"}},"outputId":"cd17d3d2-78c0-4dc9-950d-b8f51f017a06"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["[1] \"missing data with md.pattern----------------\"\n"]},{"output_type":"display_data","data":{"text/html":["\n","\n","\n","\t\n","\n","\n","\t\n","\t\n","\t\n","\n","
A matrix: 3 × 6 of type dbl
SurvivedPclassSibSpParchAge
7141111 1 0
1771111 0 1
0000177177
\n"],"text/markdown":"\nA matrix: 3 × 6 of type dbl\n\n| | Survived | Pclass | SibSp | Parch | Age | |\n|---|---|---|---|---|---|---|\n| 714 | 1 | 1 | 1 | 1 | 1 | 0 |\n| 177 | 1 | 1 | 1 | 1 | 0 | 1 |\n| | 0 | 0 | 0 | 0 | 177 | 177 |\n\n","text/latex":"A matrix: 3 × 6 of type dbl\n\\begin{tabular}{r|llllll}\n & Survived & Pclass & SibSp & Parch & Age & \\\\\n\\hline\n\t714 & 1 & 1 & 1 & 1 & 1 & 0\\\\\n\t177 & 1 & 1 & 1 & 1 & 0 & 1\\\\\n\t & 0 & 0 & 0 & 0 & 177 & 177\\\\\n\\end{tabular}\n","text/plain":[" Survived Pclass SibSp Parch Age \n","714 1 1 1 1 1 0\n","177 1 1 1 1 0 1\n"," 0 0 0 0 177 177"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["plot without 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dAEAQwg4AIAhhBwAQhLADAAhC2AEABCHsAACCEHYA\nAEEIOwCAIIQdAEAQwg4AIAhhBwAQhLADAAhC2AEABCHsAACCEHYAAEEIOwCAIIQdAEAQwg4A\nIAhhBwAQhLADAAhC2AEABCHsAACCEHYAAEEIOwCAIIQdAEAQwg4AIAhhBwAQhLADAAhC2AEA\nBCHsAACCEHYAAEEIOwCAIIQdAEAQwg4AIAhhBwAQhLADAAhC2AEABCHsAACCEHYAAEEIOwCA\nIIQdAEAQwg4AIAhhBwAQhLADAAhC2AEABCHsAACCEHYAAEEIOwCAIIQdAEAQwg4AIAhhBwAQ\nhLADAAhC2AEABCHsAACCEHYAAEEIOwCAIIQdAEAQwg4AIAhhBwAQhLADAAhC2AEABCHsAACC\nEHYAAEEIOwCAIIQdAEAQwg4AIAhhBwAQhLADAAhC2AEABCHsAACCEHYAAEEIOwCAIIQdAEAQ\nwg4AIAhhBwAQhLADAAhC2AEABCHsAACCEHYAAEEIOwCAIIQdAEAQwg4AIAhhBwAQhLADAAhC\n2AEABCHsAACCEHYAAEEIOwCAIIQdAEAQwg4AIAhhBwAQhLADAAhC2AEABCHsAACCEHYAAEEI\nOwCAIIQdAEAQwg4AIAhhBwAQhLADAAhC2AEABCHsAACCEHYAAEEIOwCAIIQdAEAQwg4AIAhh\nBwAQhLADAAhC2AEABCHsAACCEHYAAEEIOwCAIIQdAEAQwg4AIAhhBwAQhLADAAhC2AEABCHs\nAACCEHYAAEEIOwCAIIQdAEAQwg4AIAhhBwAQhLADAAhC2AEABCHsAACCEHYAAEEIOwCAIIQd\nAEAQwg4AIAhhBwAQhLADAAhC2AEABCHsAACCEHYAAEEIOwCAIIQdAEAQwg4AIAhhBwAQhLAD\nAAji/wE6+L3DTW/SXAAAAABJRU5ErkJggg=="},"metadata":{"image/png":{"width":420,"height":420}}}]},{"cell_type":"markdown","source":["Onto the imputation now. We'll use the following MICE imputation methods:\n","pmm: Predictive mean matching.\n","cart: Classification and regression trees.\n","laso.norm: Lasso linear regression."],"metadata":{"id":"EyEVVfVYE-4D"}},{"cell_type":"code","source":["#Once again, the results will be stored in a data.frame:\n","mice_imputed <- data.frame(\n"," original = titanic_train$Age,\n"," imputed_pmm = complete(mice(titanic_numeric, method = \"pmm\"))$Age,\n"," imputed_cart = complete(mice(titanic_numeric, method = \"cart\"))$Age,\n"," imputed_lasso = complete(mice(titanic_numeric, method = \"lasso.norm\"))$Age\n",")\n","mice_imputed"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"vpYlmS8PE9gi","executionInfo":{"status":"ok","timestamp":1717351712569,"user_tz":-120,"elapsed":3095,"user":{"displayName":"toni Monleon","userId":"05269440626861479105"}},"outputId":"92fe864c-2425-4881-a1d2-470cd0b71f34"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["\n"," iter imp variable\n"," 1 1 Age\n"," 1 2 Age\n"," 1 3 Age\n"," 1 4 Age\n"," 1 5 Age\n"," 2 1 Age\n"," 2 2 Age\n"," 2 3 Age\n"," 2 4 Age\n"," 2 5 Age\n"," 3 1 Age\n"," 3 2 Age\n"," 3 3 Age\n"," 3 4 Age\n"," 3 5 Age\n"," 4 1 Age\n"," 4 2 Age\n"," 4 3 Age\n"," 4 4 Age\n"," 4 5 Age\n"," 5 1 Age\n"," 5 2 Age\n"," 5 3 Age\n"," 5 4 Age\n"," 5 5 Age\n","\n"," iter imp variable\n"," 1 1 Age\n"," 1 2 Age\n"," 1 3 Age\n"," 1 4 Age\n"," 1 5 Age\n"," 2 1 Age\n"," 2 2 Age\n"," 2 3 Age\n"," 2 4 Age\n"," 2 5 Age\n"," 3 1 Age\n"," 3 2 Age\n"," 3 3 Age\n"," 3 4 Age\n"," 3 5 Age\n"," 4 1 Age\n"," 4 2 Age\n"," 4 3 Age\n"," 4 4 Age\n"," 4 5 Age\n"," 5 1 Age\n"," 5 2 Age\n"," 5 3 Age\n"," 5 4 Age\n"," 5 5 Age\n","\n"," iter imp variable\n"," 1 1 Age\n"," 1 2 Age\n"," 1 3 Age\n"," 1 4 Age\n"," 1 5 Age\n"," 2 1 Age\n"," 2 2 Age\n"," 2 3 Age\n"," 2 4 Age\n"," 2 5 Age\n"," 3 1 Age\n"," 3 2 Age\n"," 3 3 Age\n"," 3 4 Age\n"," 3 5 Age\n"," 4 1 Age\n"," 4 2 Age\n"," 4 3 Age\n"," 4 4 Age\n"," 4 5 Age\n"," 5 1 Age\n"," 5 2 Age\n"," 5 3 Age\n"," 5 4 Age\n"," 5 5 Age\n"]},{"output_type":"display_data","data":{"text/html":["\n","\n","\n","\t\n","\t\n","\n","\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\n","
A data.frame: 891 × 4
originalimputed_pmmimputed_cartimputed_lasso
<dbl><dbl><dbl><dbl>
22222222.00000
38383838.00000
26262626.00000
35353535.00000
35353535.00000
NA362629.05269
54545454.00000
2 2 2 2.00000
27272727.00000
14141414.00000
4 4 4 4.00000
58585858.00000
20202020.00000
39393939.00000
14141414.00000
55555555.00000
2 2 2 2.00000
NA195023.35412
31313131.00000
NA412230.60267
35353535.00000
34343434.00000
15151515.00000
28282828.00000
8 8 8 8.00000
38383838.00000
NA192237.09124
19191919.00000
NA402125.69696
NA143226.76868
⋮⋮⋮⋮
21212121.000000
48484848.000000
NA 5 1 1.718252
24242424.000000
42424242.000000
27272727.000000
31313131.000000
NA142435.131428
4 4 4 4.000000
26262626.000000
47474747.000000
33333333.000000
47474747.000000
28282828.000000
15151515.000000
20202020.000000
19191919.000000
NA252335.448769
56565656.000000
25252525.000000
33333333.000000
22222222.000000
28282828.000000
25252525.000000
39393939.000000
27272727.000000
19191919.000000
NA481724.238496
26262626.000000
32323232.000000
\n"],"text/markdown":"\nA data.frame: 891 × 4\n\n| original <dbl> | imputed_pmm <dbl> | imputed_cart <dbl> | imputed_lasso <dbl> |\n|---|---|---|---|\n| 22 | 22 | 22 | 22.00000 |\n| 38 | 38 | 38 | 38.00000 |\n| 26 | 26 | 26 | 26.00000 |\n| 35 | 35 | 35 | 35.00000 |\n| 35 | 35 | 35 | 35.00000 |\n| NA | 36 | 26 | 29.05269 |\n| 54 | 54 | 54 | 54.00000 |\n| 2 | 2 | 2 | 2.00000 |\n| 27 | 27 | 27 | 27.00000 |\n| 14 | 14 | 14 | 14.00000 |\n| 4 | 4 | 4 | 4.00000 |\n| 58 | 58 | 58 | 58.00000 |\n| 20 | 20 | 20 | 20.00000 |\n| 39 | 39 | 39 | 39.00000 |\n| 14 | 14 | 14 | 14.00000 |\n| 55 | 55 | 55 | 55.00000 |\n| 2 | 2 | 2 | 2.00000 |\n| NA | 19 | 50 | 23.35412 |\n| 31 | 31 | 31 | 31.00000 |\n| NA | 41 | 22 | 30.60267 |\n| 35 | 35 | 35 | 35.00000 |\n| 34 | 34 | 34 | 34.00000 |\n| 15 | 15 | 15 | 15.00000 |\n| 28 | 28 | 28 | 28.00000 |\n| 8 | 8 | 8 | 8.00000 |\n| 38 | 38 | 38 | 38.00000 |\n| NA | 19 | 22 | 37.09124 |\n| 19 | 19 | 19 | 19.00000 |\n| NA | 40 | 21 | 25.69696 |\n| NA | 14 | 32 | 26.76868 |\n| ⋮ | ⋮ | ⋮ | ⋮ |\n| 21 | 21 | 21 | 21.000000 |\n| 48 | 48 | 48 | 48.000000 |\n| NA | 5 | 1 | 1.718252 |\n| 24 | 24 | 24 | 24.000000 |\n| 42 | 42 | 42 | 42.000000 |\n| 27 | 27 | 27 | 27.000000 |\n| 31 | 31 | 31 | 31.000000 |\n| NA | 14 | 24 | 35.131428 |\n| 4 | 4 | 4 | 4.000000 |\n| 26 | 26 | 26 | 26.000000 |\n| 47 | 47 | 47 | 47.000000 |\n| 33 | 33 | 33 | 33.000000 |\n| 47 | 47 | 47 | 47.000000 |\n| 28 | 28 | 28 | 28.000000 |\n| 15 | 15 | 15 | 15.000000 |\n| 20 | 20 | 20 | 20.000000 |\n| 19 | 19 | 19 | 19.000000 |\n| NA | 25 | 23 | 35.448769 |\n| 56 | 56 | 56 | 56.000000 |\n| 25 | 25 | 25 | 25.000000 |\n| 33 | 33 | 33 | 33.000000 |\n| 22 | 22 | 22 | 22.000000 |\n| 28 | 28 | 28 | 28.000000 |\n| 25 | 25 | 25 | 25.000000 |\n| 39 | 39 | 39 | 39.000000 |\n| 27 | 27 | 27 | 27.000000 |\n| 19 | 19 | 19 | 19.000000 |\n| NA | 48 | 17 | 24.238496 |\n| 26 | 26 | 26 | 26.000000 |\n| 32 | 32 | 32 | 32.000000 |\n\n","text/latex":"A data.frame: 891 × 4\n\\begin{tabular}{llll}\n original & imputed\\_pmm & imputed\\_cart & imputed\\_lasso\\\\\n & & & \\\\\n\\hline\n\t 22 & 22 & 22 & 22.00000\\\\\n\t 38 & 38 & 38 & 38.00000\\\\\n\t 26 & 26 & 26 & 26.00000\\\\\n\t 35 & 35 & 35 & 35.00000\\\\\n\t 35 & 35 & 35 & 35.00000\\\\\n\t NA & 36 & 26 & 29.05269\\\\\n\t 54 & 54 & 54 & 54.00000\\\\\n\t 2 & 2 & 2 & 2.00000\\\\\n\t 27 & 27 & 27 & 27.00000\\\\\n\t 14 & 14 & 14 & 14.00000\\\\\n\t 4 & 4 & 4 & 4.00000\\\\\n\t 58 & 58 & 58 & 58.00000\\\\\n\t 20 & 20 & 20 & 20.00000\\\\\n\t 39 & 39 & 39 & 39.00000\\\\\n\t 14 & 14 & 14 & 14.00000\\\\\n\t 55 & 55 & 55 & 55.00000\\\\\n\t 2 & 2 & 2 & 2.00000\\\\\n\t NA & 19 & 50 & 23.35412\\\\\n\t 31 & 31 & 31 & 31.00000\\\\\n\t NA & 41 & 22 & 30.60267\\\\\n\t 35 & 35 & 35 & 35.00000\\\\\n\t 34 & 34 & 34 & 34.00000\\\\\n\t 15 & 15 & 15 & 15.00000\\\\\n\t 28 & 28 & 28 & 28.00000\\\\\n\t 8 & 8 & 8 & 8.00000\\\\\n\t 38 & 38 & 38 & 38.00000\\\\\n\t NA & 19 & 22 & 37.09124\\\\\n\t 19 & 19 & 19 & 19.00000\\\\\n\t NA & 40 & 21 & 25.69696\\\\\n\t NA & 14 & 32 & 26.76868\\\\\n\t ⋮ & ⋮ & ⋮ & ⋮\\\\\n\t 21 & 21 & 21 & 21.000000\\\\\n\t 48 & 48 & 48 & 48.000000\\\\\n\t NA & 5 & 1 & 1.718252\\\\\n\t 24 & 24 & 24 & 24.000000\\\\\n\t 42 & 42 & 42 & 42.000000\\\\\n\t 27 & 27 & 27 & 27.000000\\\\\n\t 31 & 31 & 31 & 31.000000\\\\\n\t NA & 14 & 24 & 35.131428\\\\\n\t 4 & 4 & 4 & 4.000000\\\\\n\t 26 & 26 & 26 & 26.000000\\\\\n\t 47 & 47 & 47 & 47.000000\\\\\n\t 33 & 33 & 33 & 33.000000\\\\\n\t 47 & 47 & 47 & 47.000000\\\\\n\t 28 & 28 & 28 & 28.000000\\\\\n\t 15 & 15 & 15 & 15.000000\\\\\n\t 20 & 20 & 20 & 20.000000\\\\\n\t 19 & 19 & 19 & 19.000000\\\\\n\t NA & 25 & 23 & 35.448769\\\\\n\t 56 & 56 & 56 & 56.000000\\\\\n\t 25 & 25 & 25 & 25.000000\\\\\n\t 33 & 33 & 33 & 33.000000\\\\\n\t 22 & 22 & 22 & 22.000000\\\\\n\t 28 & 28 & 28 & 28.000000\\\\\n\t 25 & 25 & 25 & 25.000000\\\\\n\t 39 & 39 & 39 & 39.000000\\\\\n\t 27 & 27 & 27 & 27.000000\\\\\n\t 19 & 19 & 19 & 19.000000\\\\\n\t NA & 48 & 17 & 24.238496\\\\\n\t 26 & 26 & 26 & 26.000000\\\\\n\t 32 & 32 & 32 & 32.000000\\\\\n\\end{tabular}\n","text/plain":[" original imputed_pmm imputed_cart imputed_lasso\n","1 22 22 22 22.00000 \n","2 38 38 38 38.00000 \n","3 26 26 26 26.00000 \n","4 35 35 35 35.00000 \n","5 35 35 35 35.00000 \n","6 NA 36 26 29.05269 \n","7 54 54 54 54.00000 \n","8 2 2 2 2.00000 \n","9 27 27 27 27.00000 \n","10 14 14 14 14.00000 \n","11 4 4 4 4.00000 \n","12 58 58 58 58.00000 \n","13 20 20 20 20.00000 \n","14 39 39 39 39.00000 \n","15 14 14 14 14.00000 \n","16 55 55 55 55.00000 \n","17 2 2 2 2.00000 \n","18 NA 19 50 23.35412 \n","19 31 31 31 31.00000 \n","20 NA 41 22 30.60267 \n","21 35 35 35 35.00000 \n","22 34 34 34 34.00000 \n","23 15 15 15 15.00000 \n","24 28 28 28 28.00000 \n","25 8 8 8 8.00000 \n","26 38 38 38 38.00000 \n","27 NA 19 22 37.09124 \n","28 19 19 19 19.00000 \n","29 NA 40 21 25.69696 \n","30 NA 14 32 26.76868 \n","⋮ ⋮ ⋮ ⋮ ⋮ \n","862 21 21 21 21.000000 \n","863 48 48 48 48.000000 \n","864 NA 5 1 1.718252 \n","865 24 24 24 24.000000 \n","866 42 42 42 42.000000 \n","867 27 27 27 27.000000 \n","868 31 31 31 31.000000 \n","869 NA 14 24 35.131428 \n","870 4 4 4 4.000000 \n","871 26 26 26 26.000000 \n","872 47 47 47 47.000000 \n","873 33 33 33 33.000000 \n","874 47 47 47 47.000000 \n","875 28 28 28 28.000000 \n","876 15 15 15 15.000000 \n","877 20 20 20 20.000000 \n","878 19 19 19 19.000000 \n","879 NA 25 23 35.448769 \n","880 56 56 56 56.000000 \n","881 25 25 25 25.000000 \n","882 33 33 33 33.000000 \n","883 22 22 22 22.000000 \n","884 28 28 28 28.000000 \n","885 25 25 25 25.000000 \n","886 39 39 39 39.000000 \n","887 27 27 27 27.000000 \n","888 19 19 19 19.000000 \n","889 NA 48 17 24.238496 \n","890 26 26 26 26.000000 \n","891 32 32 32 32.000000 "]},"metadata":{}}]},{"cell_type":"code","source":["#Let's take a look at the variable distribution changes introduced by imputation on a 2x2 grid of histograms:\n","\n","h1 <- ggplot(mice_imputed, aes(x = original)) +\n"," geom_histogram(fill = \"#ad1538\", color = \"#000000\", position = \"identity\") +\n"," ggtitle(\"Original distribution\") +\n"," theme_classic()\n","h2 <- ggplot(mice_imputed, aes(x = imputed_pmm)) +\n"," geom_histogram(fill = \"#15ad4f\", color = \"#000000\", position = \"identity\") +\n"," ggtitle(\"pmm\") +\n"," theme_classic()\n","h3 <- ggplot(mice_imputed, aes(x = imputed_cart)) +\n"," geom_histogram(fill = \"#1543ad\", color = \"#000000\", position = \"identity\") +\n"," ggtitle(\"cart\") +\n"," theme_classic()\n","h4 <- ggplot(mice_imputed, aes(x = imputed_lasso)) +\n"," geom_histogram(fill = \"#ad8415\", color = \"#000000\", position = \"identity\") +\n"," ggtitle(\"lasso.norm\") +\n"," theme_classic()\n","\n","plot_grid(h1, h2, h3, h4, nrow = 2, ncol = 2)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":541},"id":"IPnboJjMFZIZ","executionInfo":{"status":"ok","timestamp":1717351733508,"user_tz":-120,"elapsed":1539,"user":{"displayName":"toni Monleon","userId":"05269440626861479105"}},"outputId":"b56b6ac1-6020-4eda-d7a0-898f8c1abb91"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["\u001b[1m\u001b[22m`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.\n","Warning message:\n","“\u001b[1m\u001b[22mRemoved 177 rows containing non-finite values (`stat_bin()`).”\n","\u001b[1m\u001b[22m`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.\n","\u001b[1m\u001b[22m`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.\n","\u001b[1m\u001b[22m`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.\n"]},{"output_type":"display_data","data":{"text/plain":["plot without 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AuJZdQWr7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\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GLUqFE6nU5KaUIIDw+PkSNH3r7uzZs3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"},"metadata":{"image/png":{"width":420,"height":420}}}]},{"cell_type":"markdown","source":["More possibilities in: https://www.appsilon.com/post/imputation-in-r with R missForest Package"],"metadata":{"id":"QoQdzgRiFLLr"}},{"cell_type":"markdown","source":["**EXERCISE**\n","\n","We propose do a complete study of missings and use imputation in the dataframe airquality. Use different methods and ideas proposed above:\n","\n"],"metadata":{"id":"DcYtuz_-DW7u"}},{"cell_type":"code","source":["data(airquality)\n","head(airquality)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":286},"id":"m13hgAsdEMEe","executionInfo":{"status":"ok","timestamp":1717434395653,"user_tz":-120,"elapsed":395,"user":{"displayName":"toni Monleon","userId":"05269440626861479105"}},"outputId":"3cae7bcb-d825-430d-a24b-4534b040d879"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/html":["\n","\n","\n","\t\n","\t\n","\n","\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\n","
A data.frame: 6 × 6
OzoneSolar.RWindTempMonthDay
<int><int><dbl><int><int><int>
141190 7.46751
236118 8.07252
31214912.67453
41831311.56254
5NA NA14.35655
628 NA14.96656
\n"],"text/markdown":"\nA data.frame: 6 × 6\n\n| | Ozone <int> | Solar.R <int> | Wind <dbl> | Temp <int> | Month <int> | Day <int> |\n|---|---|---|---|---|---|---|\n| 1 | 41 | 190 | 7.4 | 67 | 5 | 1 |\n| 2 | 36 | 118 | 8.0 | 72 | 5 | 2 |\n| 3 | 12 | 149 | 12.6 | 74 | 5 | 3 |\n| 4 | 18 | 313 | 11.5 | 62 | 5 | 4 |\n| 5 | NA | NA | 14.3 | 56 | 5 | 5 |\n| 6 | 28 | NA | 14.9 | 66 | 5 | 6 |\n\n","text/latex":"A data.frame: 6 × 6\n\\begin{tabular}{r|llllll}\n & Ozone & Solar.R & Wind & Temp & Month & Day\\\\\n & & & & & & \\\\\n\\hline\n\t1 & 41 & 190 & 7.4 & 67 & 5 & 1\\\\\n\t2 & 36 & 118 & 8.0 & 72 & 5 & 2\\\\\n\t3 & 12 & 149 & 12.6 & 74 & 5 & 3\\\\\n\t4 & 18 & 313 & 11.5 & 62 & 5 & 4\\\\\n\t5 & NA & NA & 14.3 & 56 & 5 & 5\\\\\n\t6 & 28 & NA & 14.9 & 66 & 5 & 6\\\\\n\\end{tabular}\n","text/plain":[" Ozone Solar.R Wind Temp Month Day\n","1 41 190 7.4 67 5 1 \n","2 36 118 8.0 72 5 2 \n","3 12 149 12.6 74 5 3 \n","4 18 313 11.5 62 5 4 \n","5 NA NA 14.3 56 5 5 \n","6 28 NA 14.9 66 5 6 "]},"metadata":{}}]},{"cell_type":"markdown","source":["---\n"],"metadata":{"id":"YD4Cfm4ADg9G"}},{"cell_type":"markdown","source":["\n","# Case study: **Wine example**\n","\n","\n","\n","![](https://www.benvenutolimos.com/blog/wp-content/uploads/2017/12/18523855_l.jpg)\n","\n","\n","Reading Multivariate Analysis Data into R.\n","\n","The first thing that you will want to do to analyse your multivariate data will be to read it into R, and to plot the data. You can read data into R using the read.table() function.\n","\n","For example, the file http://archive.ics.uci.edu/ml/machine-learning-databases/wine/wine.data contains data on concentrations of 13 different chemicals in wines grown in the same region in Italy that are derived from three different cultivars.\n","\n","\n","![](https://revistaelconocedor.com/wp-content/uploads/2016/02/italia.jpg)\n","\n","\n","The data set looks like this:"],"metadata":{"id":"i_Ng7U-amq5M"}},{"cell_type":"code","source":[" #Capture data from the repository:\n"," wine <- read.table(\"http://archive.ics.uci.edu/ml/machine-learning-databases/wine/wine.data\",\n"," sep=\",\")\n","\n","\n","\n","#defining the variable name, when it is convenient to do so, for example to interpret the variables\n","names(wine)<- c(\"groups\",\"Alcohol\",\"Malic acid\",\"Ash\",\"Alcalinity of ash\",\"Magnesium\",\"Total phenols\", \"Flavanoids\", \"Nonflavanoid phenols\", \"Proanthocyanins\",\"Color intensity\",\n"," \"Hue\", \"OD280/OD315 of diluted wines\",\"Proline\")\n","\n"," head(wine) #without variables definition\n"," tail(wine)\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":590},"id":"N201Ul5RrySN","executionInfo":{"status":"ok","timestamp":1717434487293,"user_tz":-120,"elapsed":789,"user":{"displayName":"toni Monleon","userId":"05269440626861479105"}},"outputId":"a55d6523-ff5d-4ea4-da81-4b25ee3efcd9"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/html":["\n","\n","\n","\t\n","\t\n","\n","\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\n","
A data.frame: 6 × 14
groupsAlcoholMalic acidAshAlcalinity of ashMagnesiumTotal phenolsFlavanoidsNonflavanoid phenolsProanthocyaninsColor intensityHueOD280/OD315 of diluted winesProline
<int><dbl><dbl><dbl><dbl><int><dbl><dbl><dbl><dbl><dbl><dbl><dbl><int>
1114.231.712.4315.61272.803.060.282.295.641.043.921065
2113.201.782.1411.21002.652.760.261.284.381.053.401050
3113.162.362.6718.61012.803.240.302.815.681.033.171185
4114.371.952.5016.81133.853.490.242.187.800.863.451480
5113.242.592.8721.01182.802.690.391.824.321.042.93 735
6114.201.762.4515.21123.273.390.341.976.751.052.851450
\n"],"text/markdown":"\nA data.frame: 6 × 14\n\n| | groups <int> | Alcohol <dbl> | Malic acid <dbl> | Ash <dbl> | Alcalinity of ash <dbl> | Magnesium <int> | Total phenols <dbl> | Flavanoids <dbl> | Nonflavanoid phenols <dbl> | Proanthocyanins <dbl> | Color intensity <dbl> | Hue <dbl> | OD280/OD315 of diluted wines <dbl> | Proline <int> |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | 1 | 14.23 | 1.71 | 2.43 | 15.6 | 127 | 2.80 | 3.06 | 0.28 | 2.29 | 5.64 | 1.04 | 3.92 | 1065 |\n| 2 | 1 | 13.20 | 1.78 | 2.14 | 11.2 | 100 | 2.65 | 2.76 | 0.26 | 1.28 | 4.38 | 1.05 | 3.40 | 1050 |\n| 3 | 1 | 13.16 | 2.36 | 2.67 | 18.6 | 101 | 2.80 | 3.24 | 0.30 | 2.81 | 5.68 | 1.03 | 3.17 | 1185 |\n| 4 | 1 | 14.37 | 1.95 | 2.50 | 16.8 | 113 | 3.85 | 3.49 | 0.24 | 2.18 | 7.80 | 0.86 | 3.45 | 1480 |\n| 5 | 1 | 13.24 | 2.59 | 2.87 | 21.0 | 118 | 2.80 | 2.69 | 0.39 | 1.82 | 4.32 | 1.04 | 2.93 | 735 |\n| 6 | 1 | 14.20 | 1.76 | 2.45 | 15.2 | 112 | 3.27 | 3.39 | 0.34 | 1.97 | 6.75 | 1.05 | 2.85 | 1450 |\n\n","text/latex":"A data.frame: 6 × 14\n\\begin{tabular}{r|llllllllllllll}\n & groups & Alcohol & Malic acid & Ash & Alcalinity of ash & Magnesium & Total phenols & Flavanoids & Nonflavanoid phenols & Proanthocyanins & Color intensity & Hue & OD280/OD315 of diluted wines & Proline\\\\\n & & & & & & & & & & & & & & \\\\\n\\hline\n\t1 & 1 & 14.23 & 1.71 & 2.43 & 15.6 & 127 & 2.80 & 3.06 & 0.28 & 2.29 & 5.64 & 1.04 & 3.92 & 1065\\\\\n\t2 & 1 & 13.20 & 1.78 & 2.14 & 11.2 & 100 & 2.65 & 2.76 & 0.26 & 1.28 & 4.38 & 1.05 & 3.40 & 1050\\\\\n\t3 & 1 & 13.16 & 2.36 & 2.67 & 18.6 & 101 & 2.80 & 3.24 & 0.30 & 2.81 & 5.68 & 1.03 & 3.17 & 1185\\\\\n\t4 & 1 & 14.37 & 1.95 & 2.50 & 16.8 & 113 & 3.85 & 3.49 & 0.24 & 2.18 & 7.80 & 0.86 & 3.45 & 1480\\\\\n\t5 & 1 & 13.24 & 2.59 & 2.87 & 21.0 & 118 & 2.80 & 2.69 & 0.39 & 1.82 & 4.32 & 1.04 & 2.93 & 735\\\\\n\t6 & 1 & 14.20 & 1.76 & 2.45 & 15.2 & 112 & 3.27 & 3.39 & 0.34 & 1.97 & 6.75 & 1.05 & 2.85 & 1450\\\\\n\\end{tabular}\n","text/plain":[" groups Alcohol Malic acid Ash Alcalinity of ash Magnesium Total phenols\n","1 1 14.23 1.71 2.43 15.6 127 2.80 \n","2 1 13.20 1.78 2.14 11.2 100 2.65 \n","3 1 13.16 2.36 2.67 18.6 101 2.80 \n","4 1 14.37 1.95 2.50 16.8 113 3.85 \n","5 1 13.24 2.59 2.87 21.0 118 2.80 \n","6 1 14.20 1.76 2.45 15.2 112 3.27 \n"," Flavanoids Nonflavanoid phenols Proanthocyanins Color intensity Hue \n","1 3.06 0.28 2.29 5.64 1.04\n","2 2.76 0.26 1.28 4.38 1.05\n","3 3.24 0.30 2.81 5.68 1.03\n","4 3.49 0.24 2.18 7.80 0.86\n","5 2.69 0.39 1.82 4.32 1.04\n","6 3.39 0.34 1.97 6.75 1.05\n"," OD280/OD315 of diluted wines Proline\n","1 3.92 1065 \n","2 3.40 1050 \n","3 3.17 1185 \n","4 3.45 1480 \n","5 2.93 735 \n","6 2.85 1450 "]},"metadata":{}},{"output_type":"display_data","data":{"text/html":["\n","\n","\n","\t\n","\t\n","\n","\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\n","
A data.frame: 6 × 14
groupsAlcoholMalic acidAshAlcalinity of ashMagnesiumTotal phenolsFlavanoidsNonflavanoid phenolsProanthocyaninsColor intensityHueOD280/OD315 of diluted winesProline
<int><dbl><dbl><dbl><dbl><int><dbl><dbl><dbl><dbl><dbl><dbl><dbl><int>
173314.162.512.4820.0 911.680.700.441.24 9.70.621.71660
174313.715.652.4520.5 951.680.610.521.06 7.70.641.74740
175313.403.912.4823.01021.800.750.431.41 7.30.701.56750
176313.274.282.2620.01201.590.690.431.3510.20.591.56835
177313.172.592.3720.01201.650.680.531.46 9.30.601.62840
178314.134.102.7424.5 962.050.760.561.35 9.20.611.60560
\n"],"text/markdown":"\nA data.frame: 6 × 14\n\n| | groups <int> | Alcohol <dbl> | Malic acid <dbl> | Ash <dbl> | Alcalinity of ash <dbl> | Magnesium <int> | Total phenols <dbl> | Flavanoids <dbl> | Nonflavanoid phenols <dbl> | Proanthocyanins <dbl> | Color intensity <dbl> | Hue <dbl> | OD280/OD315 of diluted wines <dbl> | Proline <int> |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 173 | 3 | 14.16 | 2.51 | 2.48 | 20.0 | 91 | 1.68 | 0.70 | 0.44 | 1.24 | 9.7 | 0.62 | 1.71 | 660 |\n| 174 | 3 | 13.71 | 5.65 | 2.45 | 20.5 | 95 | 1.68 | 0.61 | 0.52 | 1.06 | 7.7 | 0.64 | 1.74 | 740 |\n| 175 | 3 | 13.40 | 3.91 | 2.48 | 23.0 | 102 | 1.80 | 0.75 | 0.43 | 1.41 | 7.3 | 0.70 | 1.56 | 750 |\n| 176 | 3 | 13.27 | 4.28 | 2.26 | 20.0 | 120 | 1.59 | 0.69 | 0.43 | 1.35 | 10.2 | 0.59 | 1.56 | 835 |\n| 177 | 3 | 13.17 | 2.59 | 2.37 | 20.0 | 120 | 1.65 | 0.68 | 0.53 | 1.46 | 9.3 | 0.60 | 1.62 | 840 |\n| 178 | 3 | 14.13 | 4.10 | 2.74 | 24.5 | 96 | 2.05 | 0.76 | 0.56 | 1.35 | 9.2 | 0.61 | 1.60 | 560 |\n\n","text/latex":"A data.frame: 6 × 14\n\\begin{tabular}{r|llllllllllllll}\n & groups & Alcohol & Malic acid & Ash & Alcalinity of ash & Magnesium & Total phenols & Flavanoids & Nonflavanoid phenols & Proanthocyanins & Color intensity & Hue & OD280/OD315 of diluted wines & Proline\\\\\n & & & & & & & & & & & & & & \\\\\n\\hline\n\t173 & 3 & 14.16 & 2.51 & 2.48 & 20.0 & 91 & 1.68 & 0.70 & 0.44 & 1.24 & 9.7 & 0.62 & 1.71 & 660\\\\\n\t174 & 3 & 13.71 & 5.65 & 2.45 & 20.5 & 95 & 1.68 & 0.61 & 0.52 & 1.06 & 7.7 & 0.64 & 1.74 & 740\\\\\n\t175 & 3 & 13.40 & 3.91 & 2.48 & 23.0 & 102 & 1.80 & 0.75 & 0.43 & 1.41 & 7.3 & 0.70 & 1.56 & 750\\\\\n\t176 & 3 & 13.27 & 4.28 & 2.26 & 20.0 & 120 & 1.59 & 0.69 & 0.43 & 1.35 & 10.2 & 0.59 & 1.56 & 835\\\\\n\t177 & 3 & 13.17 & 2.59 & 2.37 & 20.0 & 120 & 1.65 & 0.68 & 0.53 & 1.46 & 9.3 & 0.60 & 1.62 & 840\\\\\n\t178 & 3 & 14.13 & 4.10 & 2.74 & 24.5 & 96 & 2.05 & 0.76 & 0.56 & 1.35 & 9.2 & 0.61 & 1.60 & 560\\\\\n\\end{tabular}\n","text/plain":[" groups Alcohol Malic acid Ash Alcalinity of ash Magnesium Total phenols\n","173 3 14.16 2.51 2.48 20.0 91 1.68 \n","174 3 13.71 5.65 2.45 20.5 95 1.68 \n","175 3 13.40 3.91 2.48 23.0 102 1.80 \n","176 3 13.27 4.28 2.26 20.0 120 1.59 \n","177 3 13.17 2.59 2.37 20.0 120 1.65 \n","178 3 14.13 4.10 2.74 24.5 96 2.05 \n"," Flavanoids Nonflavanoid phenols Proanthocyanins Color intensity Hue \n","173 0.70 0.44 1.24 9.7 0.62\n","174 0.61 0.52 1.06 7.7 0.64\n","175 0.75 0.43 1.41 7.3 0.70\n","176 0.69 0.43 1.35 10.2 0.59\n","177 0.68 0.53 1.46 9.3 0.60\n","178 0.76 0.56 1.35 9.2 0.61\n"," OD280/OD315 of diluted wines Proline\n","173 1.71 660 \n","174 1.74 740 \n","175 1.56 750 \n","176 1.56 835 \n","177 1.62 840 \n","178 1.60 560 "]},"metadata":{}}]},{"cell_type":"code","source":["#describe structure and data type\n","str(wine)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":111},"id":"g37EXEgeLzMF","executionInfo":{"status":"error","timestamp":1717486726439,"user_tz":-120,"elapsed":400,"user":{"displayName":"toni Monleon","userId":"05269440626861479105"}},"outputId":"fd0db036-dbe4-43bc-9988-4663575bc07f"},"execution_count":1,"outputs":[{"output_type":"error","ename":"ERROR","evalue":"Error in eval(expr, envir, enclos): object 'wine' not found\n","traceback":["Error in eval(expr, envir, enclos): object 'wine' not found\nTraceback:\n","1. str(wine)"]}]},{"cell_type":"markdown","source":[" Description :\n","\n","These data are the results of a chemical analysis of wines grown in the same region in Italy but derived from three different cultivars. The analysis determined the quantities of 13 constituents found in each of the three types of wines.\n","\n","This data frame contains 178 rows (wines), each corresponding to a different cultivar of wine produced in Piedmont (Italy), and 14 columns. The first column is the type of wine (Type), a factor variable with the following levels: Barolo (1), Grignolino (2), Barbera (3). The variables measured on the three types of wines are the following: Alcohol, Malic acid, Ash, Alcalinity, Magnesium, Phenols, Flavanoids, Nonflavanoids, Proanthocyanins, Color intensity, Hue, OD280.OD315Dilution, Proline. All variables but the label class are continuous\n","\n","\n"," (See variable definition in in https://search.r-project.org/CRAN/refmans/HDclassif/html/wine.html). This dataset is from the UCI machine learning repository, provided here : http://archive.ics.uci.edu/ml/datasets/Wine.\n","\n"," See more also in https://rpubs.com/joelrudinas03/WQD"],"metadata":{"id":"2PXBJvfiph6Y"}},{"cell_type":"code","source":["#other possibility is use directly the dataset contained in library(ContaminatedMixt)\n","#install.packages(\"HDclassif\")\n"],"metadata":{"id":"6O_Mo0iUpgof"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["#library(HDclassif)\n","#data(\"wine\")"],"metadata":{"id":"crtChemr5wtN"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["![](![image.png](data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAgQAAADbCAIAAACdlx4SAAAgAElEQVR4nOzdeTwU//8A8PeylnVb9xk5KiH3TYUiKUqlIh2iW/ed7vv4VLpRfSodilJJh5KIXJGjdCiR+z53Hcv+/pg+89nfYlsk9fm+ng9/rJn3vN/vec/svGbe855ZAoPBQAAAAP63cQ10BQAAAAw8CAYAAAAgGAAAAIBgAAAAAEEwAAAAgCAYAAAAQBAMAAAAIAgGAAAAEAQDAAAACIIBAAAABMEAAAAAgmAAAAAAQTAAAACAIBgAAABAEAwAAAAgCAYAAABQ/wUDOp0eeOWWltVENSP7wCu36HR6PxUEQB8xGIy7D6MtxrtLa1ruPHy6iUob6BoBMAB6HAxaWlqfvnjltXyLmpE9SU5HWtNyhs+a6LhElsP9k5iE9TuPfPz8taCoZP3OI09iEn5endmh0ZoXr9tJktMhyekY2k35kPv1pxfx9VvRmm2HsNXXspq45+i51ta2n14K+GUy3n5YvfVASnpWTW397r/OXroRPtA1AmAA9CwYfPqS7+a9ynHGgiu37hUUlSCEamrrwyKeOLj5eK/cWlFVjadMz8ppbKJinxubqOlZOT+x0gMo59MX94Xr/AOvYKv/8fPXopKy9vb2/i73Q+5XQ7spWJBbvG4njdbc3yX+73j34TO2NTHZ7z/9cc275+g5bN8gyekkJKcPdHXAH6kHwSDn05c5yzZFPo3tcu7VsIidh8/gl9h62sMEBfixz4IC/Hraw/pY0d9BE5V25NTFlPSsga4I+Jk0h6gqycvi/2oNVSeT+QawPgAMCCKH6VpaWi9eu40fB8VEhV2dxqooKWS++/Ag6gV2EXDuUoiZoe5M1/EIobGjzA9sXX3APwghtN53/thR5v1T/1/q89dvCSnfT7t0NDW83F01h6g1t7Rwc3MPbMVAX4wYPuTIzvU7Dp0qKi1bMm/m7OkuA10jAAYAp8HgW3Fp7KtU7LOSvOwF/z3WZoYIoY6Ojut3Ipdt2I3Fg6iY+An2o4QEBYhEovesqd6zpvZTvQdEY2NTbl4B9nnS+DGL5s4Y2PqAn4JAIDiPs3EeZzPQFQFgIHHaTVReUZWW+Q77bDfSzFB3+Pflubgcba1trU2xfz/nf6uurUMIVVXXjp+5EOvEHD9zYVV1LZaApXOzoqr66NlLtpPnSmtajp+58NylkC7HcnR0dMS+SsXuWqsZ2c9csCYmPrmJSuvpvWI6nZ6QnL7Sb7/x2GkkOR1LJ49tB05+yS9kMBjsF0xITifJ6YxymY1P2XHoFEsPPoPByPn0Ze32Q8Zjp1HUTZ1mLrpw7XYTlXYt7AG+ytfCHrDkXFVdGxQc6uK5VFrTUlrT0sVzaVBwaHllNUvR2tYTM999xKYEBYeKqBqzNCzWSompGfjaGY+dttJvf2JqRkdHB3OJLLcfCovLDp44jy3CYY9zeWU1Xmdsc9yJfIr3s+d8+mIx3h1f5ROBwczNe+/Rc4q6KTZLzcgeO8Ng2SsKikr2Hjtn6eRBUTe1n+Z97lJIfUNjlzWh0ZqjYhLmr/DTspqIJT569lJJWQVLCzPvioXFZQGXb9pOnotvke4GHbBsuIqq6oMnzls6eZDkdGwnzz194Tre+F+/FW07cNLSyQPbjZln9ajputxATVQavvMbj522cM32jLcfsCbFa77j0Cl88VEus1l2NnzP1LKaiA368F7ph2cCAIbTKwNBQQE1FSXsvLiqpralpY2fTMZmCQsJbF65cKmXO0KIyM1NERXhMM/g0PtPnsfj9+6iYhKiYhJi4lOO7dkoLSmOJ2ui0vz2+58MuopPKSgqCb3/xN3VqaaunsOyEEKl5ZUbd/11NSwCn5KclpmclnkiKHj3xuU+nlOJRE5bo7OOjo6Q8Icrtuyrqf1epScx8U9i4i+FhGsP0+hyEQaDcfdR9Ja9xz9+/jeMRT6NjXwau/944P6tqyePt+Pi4jRaV1RVb95z7G+mkTBvst+/yX5/6vy1OdNd9mxeISlO6bxUelbOdJ/VyWmZnK/m7QdPN+w8gm+1mtp6bHPYjTQ7vmeT+uBBw9QHb1+7xM17FXaxeDzgirmxnsGI4Qihr9+Kjpy5iI8s2LDc28rUgKUIlr3i+cuk5y+Trt1+cPrg1uFD1JhTvs/NW+W3/+mLV/gULPGp89cO7VjrMs6WQCCwZP6tsGSu76YXCSkcri/uQVTM3mPn8C0Vl/g6LvH1s7jEUwf8XqW+WbvtEMtuHHL3YcCRnUPUlHvUdJ3LzS8s9lyy4f7j59i/BUUlb7Lf34l86r938zRnB05qTqfTAy7f2rLvON7sNbX1l0LuhkVE9X23B/8lnB5rFOVkhg/9/lW8+zB68bqd8Ulp2BkNNze3rtbQkeZGI82NLEz0hQQFOMwzKDiUeRQHJiziyd/X7+Djc+h0+n7/QOZIgLsaFtHd3ezOyiqqlqzfxRwJcI1N1BVb9h0PuNKXQUERT14sWb8LjwS4VylvAi7f7JyewWDcuBM5z3czcyTAFRSV+KzaGhL+kMNzt7KKqoVrdvzdzZjIv2+EL1yzo6yiqvOs1xlvOY8EDAYjJPyhz6qtnbcaQujpi1dzlm369CUfITTa0njjch98Xfb7B1ZUVdNozUfPXn6V8gab7uXuOmPy+M7H6y73ilcpbxav2/n1WxE+JefTF6/lW5gjAa6gqMRr+Zabdx91nvU+N68XkQAhdOve485b6v7j51PmrWCOBMwVPnTyPH6Zy3nTsYiKScAjAa6mtv7QyfMfP3eRvrPYV6nMkQDX2ETd7x/4KjWDk0zA/wJOg4GYqPCUCfb4AKGwiCejJ80RUTW2nTx3z9Fz2e8/sfRFcJ7tfI8pbi7j8JwRQg+evigsKcM+P3+ZfOr8NXyWkrysj+c0d1cnMVFhzktpaWk9cvoi/qWyNjO8F3z6a9rTiyf26mh+P20/c/FG0utuD4uGulol2bH3r57Gp6z3nV+SHVuSHbtvy8rq2roTQcH4901QgH+iw+jlPrM0VJW7yQ9l5XzadywAX2TMKPML/nuCju2yMNHHpjQ2UY+cvvjxcz5WdELkNa2h6tis2W7Oea+jSrJjg08fFBMVptPpJ89fZT5kjLY02eDrPdrSBJ9y//Hzk+evdvfon4aq8qpFc/ZsWrFgthsfH293dU7LfOe3zx+rs6AA/6YVC97FR6Q+DfXxnIYlSEnPOnn+Go3WTCQSPd2cJ9iPxqbffRh9OeTu7cinV27exaYY6Wn7+swS4Cd3V5aFif6qRXOM9LTxKa9S3py/GoatQk1t/c5Dp/HhDK5OY5/f+ftLatSRHeuwcUGNTVT/wOAuD6/on71oz6YVqxbNYd7x2MP21fkeU5j3veS0zIKiEkEBfjeXcSyznsTEf8jN62nTsWkNfAdACGW++/goOo6Pj3fflpUl2bHrfefjs+5fPY3tmZMcbdvb26NfJuHlnj++uyk/LSHyGtawpeWVdx9Gw1MyANODK0RXpzEtra1rtx9iPv/Frpd3HDrlaGe9wdfbxECn87led4z0tM8d2a41VJ3BYMTEJ3uv/H7elJ3zqbSscpCCXEtLa+SzWPyI6e7qtM9vlYyUBELoS37hyi37Hj6L46Sg3K8Fj/5JOdLc6O8Te+VlpbEMFeVk5vluLigqKSgquf3gqaGuFonE0zkHEolHnCIqJPDvRQ8fL684RRT7/CAqFj/fNNLTPr5no8GI4QQCYeuaxfv9Aw+dvMCSW3t7++2IqPf/HCk2LvfZ4DsfG844ydFu4+6j2MVE5ruPsa9Shqgpi1NEK6trubi+NywPDw9FVAQf/pjz6cvdyGfYZyV5Wbx/ieXe/t3IZ9MnObL0tCCEfDyn7duy8ofXc+3t7fcfx+Antge3rfFyd8W29YGtqwkEwrlLIQihOw+iZrs56+toSkuKr140JyP7PbbInqPnyHx8+FHJ19tjmPrgLgsSFOA/vGOd57SJRCKxpaX1aljEmm0HsQUfPo31mDJxiJpyeta7x89fYuk9pkw4tmejsJAgQmjpfHcJcbEl63c1NlFT0rMin8Yu95nFkv8E+9H+ezdhOwCGk6cKhqqpBPy109RwBEJo+iRHbJ/BZinJywYe3TnKwphAICyeN2PB6u1YlCotr3z/KU9fR7OnTcdS9K4NvisWePLykjavXIDvGwih3LyClpZWEWEhhBAf778hXEhAAN8zabTm6po67LOyoryRnjYPD9FgxHA3FwcRYUGEUGNTUxOV1uU+D/7X9OA5AyKR6DnNOelxiK/3LOZx2ZjIp7HOnktu3n3E+V0pL3dX7GSHQCBYmxl6TJ2ITW9souYXFiOESisqX2e8xSbqaGpsWO6DRQKE0OBBCltWLcT/ZS8hOR0/8k5zdmA+EJjo60xw+H4C+yY7p7K6hsPK41pb2/Bb6wihNUvmGupqYV91IUGB5T6eTmNHsSxSVlH1Muk19tlIT3vODBf8yC4kKLB0vvueTSuwP0kJyg87r5jXbp77ZJdxNtidBi4uLjdnhzVL5mKz3ufmdb45rKOpsWy+Byc9e8x1tjYzdLSzxqO+AD/Zc9pEbFuUlv+7ycyMdDcs98Y+NzZR8WcSl3jNdHUa011Bs6e7uLs6YR3ZvLwkd1cnfKxn5ruPHz/nMZ/tIoRmujphkQAhRCAQHGys8OEMb7JyGhqbmDOXkZJYscCTeQfg0PTJjiYGOthnC2M9fF9FCHlMnWhtZoi1htZQdS93V3xWXkEh6lXT4WysTD3dnHl5SQghIUGBRXOnD1VTwWYVFBbTmlvYV5uPj1dB7vvKZr//NHfZpnOXQjLefvD2mBp5/Vzk9XNnD2/v0UU2+A/r8b0jZUX5wzvW7vdbWVhS9irlTeTTWPw5g5ra+j1/nR2moYr3vbCH79YIIW5ubuZOFTq9HSFUVV37teB7N/EoCxMVJXnmxTVUlc2N9G4/iGJfSmtrG/NAoyXrdy1Zv6vLlB8/fy0tr5STkeKk8jhac3NhcSn22dRwBMuZnZQEZaS5UcSTGOaJpeWVeAe0maGuvMz/OzYNVVMZulQFcYZ57WSkJBxsrJjvBxKJRAcbq7N/h5SWVyKEPuR+ZekT0B6mIScjyUlBzHWOfZWqrG/XXUqsFBKJh0AgzJg8/nXG2/NXw/C542ytlsybyeampY2lCXbsw/DykmwsTfCuwi/5hY1N1NwvBXgCxxkLussKG9smyP9vR5CGqrKqsiK79eyGsqICfgRn2Vc1VJWZHzRh3qsxPW065imDBymICgvh/8pISigqyGKxn97e/sO+WQKBYD/a8sLV29h1yeuMt1i8ERTgd3Uas3nVQmVFefY5gP8dvXxRHZFIVFaUnzF5/JXTB1Kfho6ztcKmv8/Ne/4y6WdVrrm5BTuKIYTERIVZLmZJPDwUsR+PXGpvb6fSOHr1WGl5ZfOPTrU6o9PbG5q+n34KCQowH3ownYfxsF+vHmFeOykJikCn0gX4+aUkvleASqOxXGeQyXxEzp6YY64ze8ylCPCTZ02dyNwCM12d2F/PiYuJspnS2ERlbvAf1IRKa25uZZ7Cy0viJZG6S99Petd0P4u+jubhnetYbl81NlEvhdy1mzwPf3gIAI6uDJqbW9buOIz1bCKEnt2+yDwicPAghWXzPfDu+9y8gl4cUrvEx8crIyWBfZFqauux8018bmtbG94fygY3Nzc+ChYhtGjuDIXuewkkJboYf8kekciN30toaGxqpFLxHlsM8yubMOzXq0eY1668srqJyjpopIlKxZ9a4CeTe/2wNHOdzYx07Udb8BC7rrOivAxeSn1DY1BwKHMLBAWHWpros+moqaphHaHPPEVQgJ+5wWWkJNynTOhuNDMvLw/WMz6wetd0PwuBQHAZZ2tjafLwWVx45LPol4n4Pb+CohK//f6XTu6D6wOAOAwGfHy8zAfQ1xlvLU30Ob9R3GviFFFlJXnsWxQTn5RXUMQ8cPvj56/4yyHYIJF4FOVl8H9N9HWwF2b8LGQ+PgW57/knpmakZb4bpCCHzy2vrO48llFGSkJDVRlbr1epb4pKy1SUFPC573Pz7j/6PjRo+FA1RztrNqWTSDx4m5SWVz6KjtPR1MA7Yeh0+qPoOPy0dIiacq+jDvO2EBIQ8PGcJkERY78Ig8G4dCM8OPQ+88QXCSnHA67s2uDL3BfELPpl0thRFvjclpbWaKZrzcGDFJgbvLGJOmHsKHNjPTbV6O75r1+mF0330wkLCbq5jHNzGdfa2paUlrnveAA2KvdVypusdx8hGADEeTeRlakBPgjvZNDVmPhk/EZxdU3djTuReEo1FSU2wxN7REZSAntYCSGU+e7j/uMB+HHtS37h7r/Ocnj1bW6ki3dNhIRHMi+V8+kL/rSn86wlnR8U+CESiYf5PsHhUxdT32RjjdPQ2HQ84DLLDQOEkARFDB80mZKe9deZS/iA9CYq7dT5a5v3HsP+ausaOpdIozXTmToTzI318H7qC1dvhz+MxrqSOzo6wh9GX7h6G5s1VE2F/UGTPeZt8SQm/vHzeHwHoNPpe4/9+wgxPsA/LvH18YAr2GdBAX68sygoOPT2g6juBhpcuhEeGvEEWwVsNBH+TmkjPW2tYeokEo+FyfcVaWyiXrl1j/mp9ecvk5R0bbCaLNu452ddpPZFL5quL6hMg6Ny8wpMHaZjmR87d5nBYJBIPFamBh5T/r0B3tDIejUJ/jdxegNZT3vY9EmOQcGhCKGCohL7ad6OdtamhiNq6xoinsTg98eU5GUt/xkp33e8vCRHW+tLN8KxG9RXwyLiEl872Fo1NVEjn8VyfuDWGqo+afyYMxevI4QePovzWrFl4Ww3PW3N1IzsA/5B+Gse7KzNREWE2ObUNXNjXTMjXexxqpT0rLFT59tYmagoKTx8FtflM2UkEs+MyeMfPInB7gSeuxTyJf+byzhbMpnv+u0HUf/89sNIc6OR5obYZxkpcTWVQVhV7z6KFhYWcrSz+lpQNNlpzBBVZWdH2/f+QQihgqKSmQvWjLY0MdHXSUrLZL5/4+xoO6T75x5+iJeXNHWC/f1Hz7Fbkav89uflF7o42iKErty8F3jlFpbMzEjXWF8bIVRWUXU84Ao+nnLHuqWKCrLzfDc3NlEbm6gnz1/T1R7W5ejSxibq3GWbgoJDTfR14hJfM78j1sbKRFFOBiFkaaLvPM7m7sNohND5q2GNTdT5HlOUleSfxSb+deZvLNgLCvCPGWnOx8c74D9W09Om6wXmIRt++/2Ly8oF+Ml8vLyjLIy1hqpjo932HQ/gJ/M5j7P9+PlrcOg9LLGgAH/v7qiD/x5OgwGZzLfcx/Pth1z8CVLsxQksyRbMdhsxfMhPrN9oS+MlXjOxt58ihAqKSrp8oJc9Mplv4Ry31DfZ2JEFe2EAS5oJ9qOnuYzrXd+XsqL84rkzst59xIJWYxP13iPWp0ZZaA9T37jCBxsR32WVlORl/VYvwvvWhYUELYy/D51qbKKeuXj9zMXrY0aZT5lgTyQSl3q5v/vwGX/uDHsrA8vaLfVy7+OLB4z1tRfMdtu89xjCHvs6fHrn4dPMCQQF+BfOdhukIEen089fDcXr4+o01mPqRH4y33yPKcfOXUYIpaRn+QdcObxjXXfPncUnpcUnpTFPMTPS9XJ3xVZBUpyybL5HemYOdngNCX8YEv6QJQf3KRPwMaYDjvOm613+2poa+OnI64y381f4IYT+PrFPgJ/s5jLu9oOoxiZqTW390g27l27YzbygyzjbYRpdP/AB/tf0YDTREDXl4NMH3VzGdTlXTFT40Pa1S73YjRrsBSKRuMHXe+l8986zJo8fM4bjN2MPUx987dyh7t5M6TzO5q9d65lfiNRTrk5jju3Z2HnItsGI4bPdnDunJxAI0yc5XvDf0+VTygYjhl85fYD5Lj2BQJjmMs7VaWyXpUtLip89vG1ON+9enjPd5ezhbX1ZOwyRSFy50DPo2K4uR6aLiQof27Nx6kR7AoEQ+TTu8KmL2PShaiobV/iIiQrz8pKW+8waaW6ETT9/NezC1bDOnUXzPaZ0forFzEj39MGtzF3b1maGN88fNTPS7bKqC2a77Vi3lM0Tzr8Y503Xu/wHKcgtnjujy8xHWxp3uWcihMyMdNf7euNPaYD/cT07cCvKy1w6uW/JvJkhdx9GxSRgfSBWpgbjbK2muYzr/B3+KQT4yYe3r3UZZ3vpRjh2M9ZYX9vHc5qRnvba7Yc4z2eQgtz1c4eT07Ju3X8cn5T2Jvu9hqqyqcGI6ZMcrUwNurufySEikThr6kQjPe0LV8NeJKTk5hUY6WlPHm/nMXXi3YfRl0Ludl4EG+ZhZWJwJ/JpxJOYV6lvhAQEjPW1ncfZOo0d1flAJi0pHnRs1ygLo8s376WkZ2moKhuO0MKrLSlOOXt4+7yZriF3H2Jrp6s11MJE3815nLG+NucvvPvhanpOc3awsbr3KBqrc1sb3UhPe8xIM3wHyPn05cCJIPyhMF+fWdrDvr9HQV5Wet1Sr9cZb/F32I3QGoq9Cx3nMWXChuXewbfuRT6Ne/chF2vGGZPHdz5m6etoProR8DIpLST8YeLrjI+fv/bHKv8snDRdrxEIhGnODkPVB58MCr73+HlbG93cSE9JQQb9s2damhicuXgd67cUFOA3N9Jzn+I00cHm94mXYMARftvX2DIYjPqGRuzpM4SQkKAAy0iYmtr6Ocs2YkNarc0ML5/a39PnxcBvYs/Rc/hLmGPCL/XlRjcAoHd+r7MnZh0dHYdOXZDVssb+Hjx9wZIg+/0n/KeVFeVkOH9bKgAAABa/bzDg5ubGB+QhhA6fupj9/hP+b1FJ2bFzl/FBolZmhhAMAACg137r37VgHkGYkp5lO3mum4vjmJFm7z/lXQoJx0dtjjQ3sh9tMaA1BQCAP9tvHQwkxSm+3rNyPn7Bjvs1tfVn/75x9u8bzGlYhmACAADohd+3mwhjZWoQfOZgd69k6DwEEwAAQC/8vqOJmLW0tMYlvr5xJzLz3QdsSKiu1tAJ9qO7HIIJ/jiNTdSWlu+vF+08bAwA8Av8GcEAAABAv/rdu4kAAAD8AhAMAAAAQDAAAAAAwQAAAACCYAAAAABBMAAAAIAgGAAAAECcv46io72V3kLt6GhHCJ5LAACA3wSBi4ubyMvPxd2nX2RBHD501kFvbW3u8S/FAwAA+DVIfMJcxD7FA466idpaqX0pAwAAQL/q+1Gao2DA6GjvYzEAAAD6T9+P0hzeQIb7BAAA8Dvr61EaRhMBAACAYAAAAACCAQAAAATBAAAAAIJgAAAAAEEwAAAAgCAYAAAAQBAMAAAAIAgGv6FvxWUnL4bmFRQPdEVQc3PLldCH7z/nc/ICKwDAHw2CwW+EwWBExSbNXbWbh8gtQREd6OogHh6iqIjQ4g0HA4LD6XT6QFcHANCPBjgYJKZmTfJaX1Vd112Cquq6SV7rhTVshDVsDpy6wnnOLS2tAcF3Bhk7L/f7i0Zr/hmV5UivK4wQ+vCl4NTfYQc2LVkwa5KQIH8/1ZBz3NzcE8ZYXj+zKzYx/VFM0kBXBwDQjzj9PYOBIiYqdOHIlo6OjpN/3+J8qarqus0HzvKT+Vb7zPycX9SLcjs6OhqbaMJCAj1dsHcVRgi1t7eH3o+2szTS1x7S00L7lbQExdvd+cKNCCvjESLCggNdHQBAv+jTlUFVdZ3vliM7/goycZz3OCZpzoqdnr47yiqrsbm5eYWevjvk9MabOc0PjYjG+xkqqmt8txyR0xs/f83e4ooq5gyT099NmL1GWMPGfubyF6/SGAwGFxeXmKiQOEWEj5eX84rVNzY5jbE8uGWJtKR4T1eKwWBk5uTOX7P3YfSrni6LEGJT4Y+fv63afnzltmOOHiujX6aOme67fs/JJirte50bqDmf8iyMRxAIBHyRu09ifdbua2j8/nLaB0/jmf/9WZ7FpUycs7aotAIhVFPbMGfFzpv3njLfJ9BQHUSn00v/2bIAgP+evnYTNTRRJ461XjJ36r3HL/xWzBMREkh9k4MQqqlt2H38wmgLg4xnwcd2rDh9+fbL5AyEUHt7+7nL4QihxIjzW1fOe535Hs8qv7Bk74m/F86alJd4Z+dq76OBN3Jyv/auVipKck52FkRiz657sDDgtXrPtsOBnlPGuY4f1bvS2Whoalo4e7K1qd7L5IyTe9cUFpd//PINm9XR0UEi8QiQyczpRwxTK62o/lpYghBqbW2LS84wNdBi34OExVorF5/7US8ZDMbHz9/mrtzFpiMOIWRpPEJr6OC7j2I7OjrCH78QFhQYb2fBHJN4eXhoLa01bDMBAPzRfkI3kQCZrKGiUFJeqaqsoCAn3dhEQwgVlpa3trY5jbGQpIhJUsTsR5okpb8bZW7Q2ET7+LlggeckJXkZhJCjjfm7j3lYPp+/Fg2Sl7GxMCCT+URFBHWHq2e9+6yprtL3GnLi/ef8g6eu1NQ1rPSebmk8gourX+6m0OntEqIiKopyCCElWSkJimhLcwub9HLSkpoaKilv3mkPVa2sqfuSX+g+2Z59EdFxrwUF+f13r9lz/IKYiJCgAH9HRwf71eHlJc1zm7Bml7+CvFTks/itq+YL8JPZpAcA/Pf01w3kpkYqraWVq1P+dHp7A5XK/c9ZJzfT6WdFVe3FkAjpEY7CGjZiw8b8de56P9WtSxnZnwT5ySf3rLE21WM+dCamZmF3g/G/nt4W7gsSiWestXHi6+yGRur7T19FhYWUFWTZL/K1sERESFBPS2Pz8nlbDwU4z10zYYyVqMgP+vrVVBQ8JjssXLff0dZCa8jgn7cGAIA/w+91A9nD1X7z8nl8pO+/5MlP7sF9gj4aZ2NW19AwZf7GMSNNvN0nKspJY9P1dYbmJd5hTsnLy/PLaoUQGqah0thE/ZxflJCa9cM+IoTQkjmu2Ac9LY0n14/T6e18fD9uxvb29qKSckF+ckVVTXt7e0972AAAf7r+ujIQEBSAVzQAACAASURBVOQn85I6UAc+hcjNhRAiErmF+Pnb/7k52c50l1JSXJSbi1tMRFCcIkIREyYSufupbl0SFhLw8Zj0+PpxBVmJaT6btx4KLC6rRAiRSDziFBHmP0GBfhn0ycXFRaO11NY3sEyXkaToamlEPov/+LnASFfzh/kICvDjNSQSiZxEAoRQxrvc56/S/j6+NfH12/iUTJa5LW1tZF6SGEWEk6wAAH+i/goGCjJSJBJPRFR8RXVN0uvsxy+SDHSGIoQEBcgaqkohd6MKikoLikrDHjzHF1FVlv9WUv48Pq2qui457e2MxX45ufn4XBkp8cLisuKyyqrqupraBvbPQNHp9JrahqrqOmpzcxu9rbquoaa2oaOjg80iGOaQkPzmbR8aoGcVRggJC/FrDVWNS0pvb/9/v2VKIBBsLAxvRTwTEiQPVpLrS5W6U1Pb4H8+xGOyg7mhzrwZE85fu19RXcOcIC3rA5FIlJGg9EfpAIDfQX8FAzFRoS3L5z2Pfz3C1mPFtmOLPSdbGo9ACHFzcy/wdEEImTp5bdx7esRwdXyRQQqym5bNOXvljorppK1HAn293PS0NPC5jrZmBALB0GG2iukkJ89VX/JL2JT+Jb/EyXOViumk5X5/BYc9HmbtNm/17ppa1pPu7mAhwcV+ZG/WvFcVRghxc3NPmWDz7GXqy+QMltc/qA5S0FRXMdbX6o/7ugwGI/zxC14SaexIE4SQrYWBmKhQcOgjPHq9/5z/V8D1aRPt4CEDAP7DCJy8dqa5sfIXVKWnPn7+Nnflzqz3n5knBh7a5OZsN1BV6rv4lMxV245Zm+muXugu889DEsVllau2Hdu62uuXja3CtLa2XQyJCAgOX7VghttEO7iRAMDvjE9Qoi+L/8Ff78GDZCMu/8XS+fMr7zn3Bwsjncjgo1FxyfS2fzuL0rM+iIkKD5KX+cWV6ejoEBcTCQ3cp6woy/zYAQDgv+cPvjL4H9HS0up36NzwIaqzpzoOdF0AAL+vPl4ZQDAAAID/gj4GA3iFNQAAAAgGAAAAIBgAAABAEAwAAAAgCAYAAAAQBAMAAAAIggEAAADEcTCAp08BAOB31tejNEfBgMD1S98mDQAAoEf6fpTmKBjwkPrlDf4AAAB+ir4fpTl6HQVCqKO9ld5C7ehoR4ij9AAAAPofgYuLm8jLz8VN6mtGHAYDAAAA/2EwmggAAAAEAwAAABAMAAAAIAgGAAAAEAQDAAAACIIBAAAABMEAAAAAgmAAAAAAQTAAAACAIBgAAABAEAwAAAAgCAYAAAAQBAMAAAAIIeIvKKOttbmF1thOb2V5QyqRh5ePX4jIw/sL6gAAAICNfn+FdVtrc1N9FZsEgiISEA8AAGBg9Xs3UQutgX2CZmp9f9cBAAAAe/0eDOhtrewTtNPb+rsO7DEYjLr6hpra+o6OjoGtSf+JeBIjrWl56OSFH14IXgt7QJLTefriVedZVdW142cunLtsU0NjU/9UEwAwYAb+BvKA/9Rac3PLxt1Hp3qtKC2vHNia9J/yyuqa2vrK6prW1gEOvQCA39OvuIEMBtyc6S6THO0E+MkkEs9A1wUA8Dsa+CuDn4jBYMS+Sp0ybzlF3VTLauLJoKs0WjM2a8/Rc5oWTpnvPmL/4j0eaRnvrCZ4BAWHxr5KVda3w9NU19Tt9w9UM7Inyek4zVz0OuMtfgWTlfPJe6WftKalmpH91v0nqmvqsOkJyekkOZ25yzbN8FmDVSAk/GFdfcP2g6ewxMcDrtDpdCxxR0fHs9hE+2neJDkdNSP7yzfvtrSw9qflFRRajHdfu/1QW9v3pZqotPkr/JxnLSktr6TT6SHhD/VtXElyOvbTvO89eu7g5oP14WA1WbJ+11SvFVifT2JqhrSm5aWQcCyf6po6v/3+0pqW0pqWS9bvCr3/hCSnE3jlFl50YUnZ+p1H1IzspTUtV287iK8jM05WAQDwp+A0GLxISDEaM5Ukp6Nu7PAm+31Pi6mta5izcldMQhr2b11Do9fqPfi/bLS2tlVV13b+a2yisqRkMBg37kS6eC7lIfJcOrlvygT7rQdOLFm/q76hkU3+gwfJ3ws+PdvN2cJEP+NFeNz94KFqKmUVVd6rtgZdCV3m7RF28TiRyD1mihfWjR77KnWS59K8gqLTB7cu8/a4Fhbh5r2qqKQMz/Duo+ghaipnD2+XkZKYtXi9trXz568FF/33mhnprt1+6PLNe1hVQ8IfTvVaIS8jdf/qadcJY1ds3rf76Fk8VGDkpKWM9bUTUt4UlpRiUz5//ZaQkm5ioCNBET1z8casxeuHD1ULu3jcytTAe5VfdFwi8+LXbz+Qk5GOvH5OXXUQ8/Sa2vol63cFXL65bL5H0NFd9Q2NMxesYWkWn1VbKyqrTx30WzRnxsVrt9dsO8jSjByuAgDgT8FpN9HqrQewU+b8wmLvlX4pUbd+uAgzEWFBE73hL1MyrExGcHNzf/1W0kilDVFT+uGCqW+yR7nM7jx93TKvHeuWcnNz41NKyysvhdx1tLM+unuDpDjFaewoBTnpxet2ThpvN8F+dHf5cxOJYiK8PDw83FxcIkKC4hRRhNCDqBfPXyZd8N/j7GBDIBD0dTTnLNt0LSxCR1PjcshdRQXZc0d2qCorIoQ0NVTdvFfdjoha5u2BZejl7rp+mReZzDdUfbDHwrXi4mLb1y1VVVYcMXxIaXnlw2exk8fb0ZpbsKoe2LZaUpxia2UqIiwYFBw6ydFOX0cTrxsvL2nC2FGXboS/TEpTUVJgMBjY4d7WyqyotPzW/cee05wPbltDERNxGjtKZZDC3GWbmFfNzWXc3s0rBPjJCKGi4n/D1cuk12ERT04f3DpnuguRSLSxMhUU4D9/NYx5WTznsaMssGa0G2luP9qic2uzXwUAwJ+C0ysDvIMFIZTx9kNPiyEQCOaG2lnvP5dX1SCE3rz9pD1UVUpc7IcLmhvrtRZndv7bvXE5cyRACOXmFUTHJTrajZQUp2Al2lqbGulpJ6S86dHpKpVGS07LHGVhbG6kRyAQEELystJRoecvnthbU9+QlvnOYbSlsqIclthQV8vW2jQpLRM/cdZQVSaT+RBC8jJSigqyygpyUhIUhBBFVER98KCa2noqrTkvvzA6LnGcnbUERQwhRCQSncaO5uPlfZ3xlqUywzRUjfV14pPSqDRabV1DfHKakZ62+uBBBYUliakZNlamFDERbGUtTfRNDUcwL6uvo4lFAmYMBiM9K0dHU8PK1JBIJCKEBPjJjnbWLMmYcx4zytzUcERWzsf29nY8AeerAAD4I3AaDJTkZfHPOpoavShp8CB5CTGRD7kFDU3U5PS3lkYjWI7mffStqBQhhB15McKCgqIiQqVlFbTmFs7zodFavhWXiggJkvlYH4Wrrq7Nfv+JIiaC15yPlyQpTiktr+zcbcXG12/FCKG5yzbxyo8gyemQ5HQM7abk5hXkFxYzH3ARQjJSEg42lrGvUnPzvuV8/PwsNtHK1EBMVLjzygry8wsJCvyw6ObmltLySlERYSFBfnyiuJgoS7LOOZeWVeA3YHq0CgCAPwKn3UTnj+/GeoqU5GWDju3uRUlCAvxmhtovUzIkxcXqG6mc9BEhhBKS0znsJlKUl0EIlVdW41MaqdSGxiYdzSGdD+tskMm8inIy34pLO4/CpFBEtYaqMxfR2tZWXVMnLyvd+RycDexQez3g8ChzY+bpRCI3F9f/C88EAsHCWO+vM38/i31VV984eJCChYk+gUDobmWlxCmILT4+XhkpieS0zIbGf6NXVU0tS7Kum5HM14tVAAD8ETj93o40N0p9GtpanJmb8lhXa2jvCjPR0/r8tTAqLmmIqhInfUQIIUNdrZLs2M5/a5fMYznoqKko2ViZhkc+ramtRwgxGIyomITsnE/mRrpEIlFFSSE3ryDj7fdb31/yv2UxdXwx4yeTjfV1omIS4pJeY1OKSsrGTPFatHaHuKiIvo5m5NPYvIJCbFZyWtaTmHhTAx1BAf4uc+uSqoqikZ52fHK6oAC/OEVUnCKalJY5a8n6+OR0rGOK2VD1wbZWpmf/Dgm8cstYX0dZQQ4hpKQga2o44vHzl3X1DdjKvkxKS0zN+GHRBAJBT3tY5ruPMfFJ2PioJiot8mksSzLmnKNiEhJTM7SHaTCH3h6tAgDg9/dLnzNQkpOSl5U6f+3+qb1rOOwjIpF4sJu6PyQjJTHbzXnJ+l3ui9b6eE57k/XeP/CKm8s4C2N9hJC5sa6Zke7yTXsLi0tlpaVOBgXjj5jx8fFqqCoHBYeGhD8kcnMb6+uMHzNygv3oeb6bc5Z+1lBVvnDtdl5+od/qRZISFE8353m+m6fOW7F2qVdpeeWJwGArUwPHMSN71OWlrCjv5e66ZtvB2tr6ma5Obz/knggMHjPK3NRgROfEQoICVmaGV8MiBAX4x9laYafnSvKyUyfYr952sK6+Yc6MSdk5n04EBXNYuqWJgavT2I27j37JL7Q00b9591FI+EOWNNdvP6DSaHNnTE5Oy/IPvOLl7upgY8n8eGCPVgEA8Pv7pcGAl5dkqj/8fW6+hipHfUQ9QiAQpk9ylJeV9g+8Ms938zAN1d0bl7tPcRIRFkIIKSvKH9y6ZuuBE9sOnBxtaeLrM+vi9Tv4gu5TnD7k5u06cmaE1lBzYz1pSfHAv3YGXLkZdCW0urbOfrRl8JmDJgY6CCFrM8M7l0/6B1z23bRHUpwyz33ynOmT5GSkelpVz2kTxUSFz/4d4jhjgYaqMpYPds+2MwtjfR1NDSEhQe1/7tYQCIRFc6dLSYof8A+a57vZxsrkxL4tLCOCuiMmKnzqgN/Rc5cCLt8Mu//ExdHu/PHdXsu3MKc5dcAv59OXub6bEEIL50xf6jWTIiZSVf1vb1JPVwEA8Jvr97eW1lYWMf976VZkSVnV2kUzmU+lRSXk+7UOAAAA2Pul9/rqGhrjEt+YG2r93HFEAAAA+uiXBoPPXwupzc2sfURwvxEAAAZav3cTNdZVsH+LNZGHJCgi2a91AAAAwF6/XxnwkoXYJ+DjF+7vOgAAAGCv368M0PffQG6g09sQ628gk/j4heE3LwEAYMD9imAAAADgNwdvDgAAAADBAAAAAAQDAAAACIIBAAAABMEAAAAA+t2CAY3WvHjdTuzHUlj+7FznFZeW/6yCIp7ESGtaHjp5gcFgXAt7gP1qfN+zxX6xmflHYH4NGq25qroW/wGGqura8TMXzl22qaGxqY85fysq9V7pR1E3neixuKKqmk1KbNthm6mlpXXz3mPSmpbYW7UZDMaTmHh9G1eSnM7lm3cRQm+y30/1WkGS01myflcTldbHSv5QP20XlmbvcspPQafTQ8If6tu4UtRNr4ZFsEnZ003P/EX4SZUFf6rfKxiQSDw+ntOiQs9HhZ4/sW8zQmjLqoXYvzvWLeXkl7w4VF5ZXVNbX1ld83O/t6lvsmW1rIND7//EPDlxJ/KZrJZ17KvUn5ttS0vrX2f/Ts/KOXNom5f7FC4Cp3sLvb29rLyqrY2OxY+v34p2HDo9fKha+OWTgwcp1NTW7zl6tolKC7t43H60JVf/v4+kn7ZL52bvpw2RnpWzdvshGyvT6wGHBynI/ngBjvXTFwH8iX7pK6x/iJubG//lHB4iESEkKy050tzopxc0Z7rLJEc7AX4yicTz0zP/z2hson78/FV7mIajnXWPIrEAP/n0wa2Htq8VERZECJWUVqSkZ82Z7oL92PKH3K95+YUOtlaOdtbwykJOfPpSUFpe6WBjaTfS7OfmDF8EgOv3K4OcT19Onr+W8+lL37Pq6Oh4FptoP82bJKejZmR/+ebdlpZW9M+l8VSvFQGXb2J9EbOXbiyrqMKW+vQl33uln7SmpfHYaSHhDzs6OhBCiakZ0pqWl0LCWYpgMBhpme9m+KyhqJtKa1ruOx7QXSdG52z3HD2H/ULnkvW7SHI6W/Ydb2qiLl6302zcjI27j6oZ2WMX701U2onAYC2riSQ5Hftp3q8z3mJX6AnJ6SQ5ne0HT/nt95fWtJTWtDx27jKdTsdLxLtW9G1cL1y7PX+Fn53rvC95BYvX7ZyzbCNCyHHGApKcTuCVW1j6Rir1+u0HnRuk8yrHvkqdMm85Rd1Uy2riyaCrWHdKQnK6rJZ1VEzC1bAIcQ2zzt10DAbj+cskbHNYjHcPexCFtS3m0KkLFuNnZr/PZWkWBzcfbeuJme8+Hjxxnqyoh7VJRVX11v0n1IzsSXI6Mxes+ZL//YfksB68zXuPjZ40R9PCKfPdx17sA523S+dfaabRmvEFLca7P4p+ia8LNstivDu2vZ6/TGIwGFifGHOz+27a67Nqa+cN0eXiP9xpmSvWeftiLT960hystsGh9xeu2c68gejt7fceP8c3zZOYeKzQzu3J8kWorqnb7x+IbQinmYvwnbPLLxH4r2H0J799x7WsJvgHXNGynOC373iPlo1PSuOR1Q64fBP7t6Oj41pYhJiaydxlmx5Fx63bcVhMzWTLvuNtbW2VVTWOMxbwyGo7z1pyJ/IpNmvR2h1UKq26pm7KvOUTPRY/i3118MR5MTWTe4+iWTK/GhrBI6sdFZPAYDBeJKSoGo51nrXk3qPoA/5BUsMs5i7bVFffwFK3LrNtaGx6FB3HI6t9IjC4sqqmtq6eSqUtWruDR1bb1GH6pZDwxNSM6prapRt2Sw2zOOAfdO9RtPOsJWJqJk+ex+NVElMzWbfj8J3Ip86zlvDIal8NjcBKzHj7wcDWFcvn4vU7BrauPLLatpPnFhWX1tbVB165xSOrHR75rLKqpqGxiU2DsKwI3qrTvVffexS97cBJrIXr6htaWlo/fckfO3W+x6J1+YXF1TV1bW1tzMtibWXqMD0k/GHA5ZvDLSd8r1JJGYPB2P3X2WHm4zPefmBplsqqmpT0LL3Rk9duP1ReWVVdU1dSVjF5ju9wywkBl2+GhD80dZiubTUx4+0HfNNIDbPYdeTMs9hXFZVVvdgHOm+Xjo4O5hVpbKIu3bBbTM1k24GTj6Lj5i7bxCOrHXjlVkdHB/Ose4+ip3uvxmexNHttXX3nDcFmcQ63UeeCGhqb7j6MFlMzcZyxIPT+42PnLjO3PJ4t1p6h9x/bTp6L72Os7VlVzfxFKC2vnDzHV9Vw7LFzl5l3zu6+ROA/ph+7iXI+fbnz4GlW3D2E0DJvD23riTNcnYapD+5dbqXllZdC7jraWR/YtlpSnGJrZSoiLBgUHDrJ0W6QghxCSEdTY5/fqmHqg8fZWLW2tT2Ofvkpr4CXRMrLL/Ryd7WxMjXU1RIWEmjv6OjuvKaJSrsccldRQfbIzvWqyopOY0fJy0nPXbZp0ni7CfajmVOWV1Z3zpafzCckIIAQ4uUlYT/ViZ1iy0hJ7N28YrSlCUIoOi7xauj9XRt8F8yeRiQSjfS0F6zeduHabWN9bSxnN5dxW9csFuAnjxg+pKKqJiomfoL9KF4S6XLIXYTQiX2bDXW1EEKDFGRnL92IEEIEgoiwED+ZjBAS4Cdj5WIny102iM4/v5XG0qpHd2+QFKc4jR2lICe9eN1ObJXFRISJRG4iN7eYiDBLN1FdfcPpC9cVFWSDju5SVVZECKmpKH2v0v8nKMDP0iyV1bVcXAQSiYciKsLNzX3h2u3nL5Mu+O9xdrAhEAjamhqzFq27cvPu3s0rsRyW+8xat9SLSCSWlFX0Yh/Q0dRgqQCLpNcZzBvFzFAXIXTjTqSjnfX7T1+YZ400NyLz8V6+eXfsaAsleVmWZkcIsUxh2dzMiwuQyZxsI0Kn7VtX33AtLMLMSPfUAT9lRXksE5aWl5GSOLhtzThbKwKBYGowYs6yTccCLuvraLK0J0LoI/qKL/Ug6gXzhtDX0ZyzbNO1sAhxMdEuv0QsP0IO/nT9uDmfxSb6eE7D/13g6daXETt5+YXRcYnj7KwlKGIIISKR6DR2NB8v7+uMt1gC7WEaCrLSCCFeXpKRrnZuXkF5RZWstMQQNZVTF64HXrlVU1fvPWuqyzjb7nbiwuKyN9k5Y0aaYffoCATCaAtjazPDpLRMlo6FHmWroao8RE0F+/wqNWPwIAUba1PsqygjJeE0dlTG2/f5hSVYAn0dTQF+MkJIRlJixPAh34pLGxqb6hoast9/MtbXGT5EDU9mbqTHvsW6bBCWNLl5BdFxiY52IyXFKdgq21qbGulpJ6S8Ye6h6qykvPLj568Ooy2VFeU4r1JnNFpz6ptsK1MDU4MRBAIBIaQxeJCDrVV6Vg4+eMlYTwdrrt7tA+wrwGAwXialDR6kYDfSHCtFWEjwgv+ep2EXZKUlO8+a4DA6MTXj3YfPP1y1LnNmWbwXFcZa3trUUFFOBpvSueUNdbUMdbWw9pSXlXYaOyr3S8G34lKW9mRGpdGS0zJHWRibG+nhC0aFnr94Yq/aYCXO93bw5+rHLWprbRpw5Sb+77lLIX25/fX1WzFCaO6yTbzyI7DBpoZ2U3LzCvILi9n0YAoLCW5ft9RYT3v9ziPqxg5O7ovYjE+tqq7JfPdx+8FT/Er6WBHK+naxr1KLisuo/39UYo+yxdFozUUlZZnvPo4Y6YIPmV2yftcPDwHVNfWVVdUUMRH8Lh+Jh+en/Nrwt6JShJCUBAWfIiwoKCoiVFpWQWtuYVel6trs958oYiL4HeDeVYlKa84vLH74LE5J1wZrELKi3sET54tLyyuZfnIZ07t9gL3m5pbS8kpREWEhQX5OZklLiCOEvhWV9C5nzhfvDictLyIkSOb792XAkuKUvILCyqoaNtnSaC3fiktZFsT0bm8Hf5x+7CYapj54kqOdttXEBbPdzl0OmTTertd9ROifA9b1gMOjzI2ZpxOJ3HQ66/1AZuqDB13w33O2dfvj5y9Xbtl/+uL1HeuWdplSUFBATUXJx3ParKkTmadzcXFhZ+u9yxZHIvFQxERMDUecObRNRlKCeRY/mS89K6e7BSliwhLilOqautbWNjKZGyHU2tZWXVPHvjhOKMrLIITKK/99gKCRSm1obNLRHELm42Uz3JBCEdUaqs68YO+qRCLxSIlTXJ3GHti2Gus5wQkJCmTnfGKe0ut9gA0+Pl4ZKYnktMyGRions6pqahFCivI/Ht/Zx8W7w0nLl1dVM2+7iqpqNRUlcYoo81IsyGReRTmZb8WlXW70Lvd2GAn2H9O/13o7N/jevHCMgdDN88d2bvDtS1aqKopGetrxyemCAvziFFFximhSWuasJevjk9PZLJWeleM0c1FUTAKJxGNnbTZ2tEXn03ycopzM8KFqCcnpXFxcWBH5hcXzlm8Oi3jSl2xx3NzcBiOGJ6ZmfPqSj+UvKMB/+uL15Zv3llWyuzIQERLSGqoeE5/8PjcPm5KW+S4hhd2Kc0hNRcnGyjQ88mlNbT1CiMFgRMUkZOd8MjfS7dyTwExWSkJDVTnyaWxeQWFfqiQowD9Ca2hSWkZ5RRXWJlxcXNsOntxx+HRjE+vRuXf7AHsEAsHSRP9LfuGj6DgGg4EQqm9onOe72XnWkrKKqs6z7jx4amGiP1RdpXc5c754d7CWfxr7qrC4DJvSueWjYhJS3mRjn4tKyiKexAxVHywvK80mW34y2VhfJyomIS7pNb7gmClei9bueJXyphd7O/jj9PtzBsPUB/flggCnrCjv5e66ZtvB2tr6ma5Obz/knggMHjPK3NRgBKP7hyeVFeWFBAW2HzrFy0v6/PXbk+fxi+ZO5yfzdZlYTFR4gaeb+6K1c303ebm7VtXUnQwKlpeVHjPKnKWHtLtsZWUkjfS0wx8+ExMV+VZU4vn/rzAQQrZWpnNnTFqwetvH3K/6OprhD59dC4s4vGOdgqx0cUm3l94kEo+nm3NMfNKsxeuX+8zi4eE5GRRcWl6poaqMJVAfrCQjJXE19H51bV1dfcMkRzsOW1VGSmK2m/OS9bvcF6318Zz2Juu9f+AVN5dxFsb67BcUERZaPG/GPN/NU+etWLvUq76h8XjAFeYqcYhAIExzdngSEz9n2ablPrPkZaWv3LyXkp518oCfmCjrT+D1bh9ACLFsFx/PacyXeiYGI9ynTFi7/dC3otKR5oY37z56EPXi1AE/aUlxIUEB5lnXwh48fv4S216IqdkbqdSq6tq5MyaxbIgZk8d3t3hdfWOPGgonIiw009VpzrKNU71WrFw4u6SsIvDKrc4tP3vpBr9Vi2RlJM9cvPG1oOjkAT9JcTH2OY8fM3KC/eh5vptzln7WUFW+cO12Xn6h3+pFQ9UHc/4lAn+uP+YuEIFA8Jw28fzx3UWl5Y4zFgReuTXPfbLf6kXs+6nFRIUPblszYviQeb6bz1y8vnS++3yPKWwub22tTW+dP0Ykcs9eunH3kTOjLEwObluLjVThJFtlRfl1S71KSiuWrN/ZRKVxcbE+WyssJLhvy6rVi+ZevnnXccaCT1/yTx/cOmOSI/vTcISQjqZG0LHdwzQGL1m/K/DKrfkeU9xcxuFz9bSH+a1eFJf4etPuowRCD36wiEAgTJ/kGH75JD+Zb57v5qgXCbs3Lt/vt4qT3n8rU4PAozvFKWKzFq+/dvvByoWzmavEOTkZqXOHdzjYWB3wD/JYtK6N3nb60FY7a1NCpyeTe7cPIIRYt8v/z1mAn3xw6+qT+7dExyW6zl1eXll97dzhqRPtCQQCPutV6pvZSze20dtuBB7xnDYR2154s6/ZepBGa2YwGCwbgszHy2bxXptgPyrs4nE+Pl6PResin8auW+bl7urEnMDVaeyBrWvCHkTN8FnT3tFx8oBfl+3JQlpSPPCvneuWeZ0PDvNeuVVYUDD4zEErU4OefonAHwp+6ewPUFld8/Z9rrKSPB6W6uobFqzezstL8t+7SUT4B78yDQAAP/THXBn8LyurqFq2Yff+44H449BYN7GmhqqgAOsYGAAA6IXf691EoEtD1VRm3aT8XAAAHDtJREFUT3fZuPtoSVmFl7vrl/zCwCu39LSHTZ/sCFfrAICfArqJ/gx0Ov3+k5izf4c8f5mkJC87fbKjj+c0pT6MUAQAAGYQDAAAAMA9AwAAABAMAAAAIAgGAAAAEAQDAAAACIIBAAAABMEAAAAAgmAAAAAAQTAAAACAIBgAAABAEAwAAAAgCAYAAAAQBAMAAADo17zCuq21uYXW2E5vZXkpHpGHl49fiMjD+wvqAAAAgI1+f2tpW2tzUz27X3sXFJGAeAAAAAOr37uJWmgN7BM0U+v7uw4AAADY6/dgQG9rZZ+gnd7W33XonYTkdJKcTuCVW/1XREdHR01tfV19Q39fn9Hp9JDwh/o2rhR106thEb3LhMFg1NU31NTWd3R0/Nzq9VpLS+vmvcekNS0TUzPYp6yqrh0/c+HcZZsaGps6z/0F2xqA39zA30D+X/51nZraeo/F61Zs3tfYRO3XgtKzctZuP2RjZXo94PAghV7+Plpzc8vG3Ueneq0oLa/8udXrNXp7e1l5VVsbvaKqeqDrAsCfDX4D+X/Cpy8FpeWVDjaWdiPNBrouP5MAP/n0wa2Htq8VERYc6LoA8Gcb+CuDn6ijo+NZbKL9NG+SnI6akf3lm3dbWloZDEZQcChJTufCtdtYsoTkdDUj+6UbducXFI2fuXCGz5q/b4SPnjSHJKdjP807LfNdl5l/Kyr1XulHUTdVM7Lfefj0mYvXSXI6T1+8otGaF6/baT1x1sXrd/BMPuR+/eGCCcnpslrWUTEJV8MixDXM7FznFZeWI4Sycj55r/ST1rRUM7Lfuv9EdU0dls+1sAckOZ3Ne4+NnjRH08Ip7tVrln4PLMHTF6+Yq41Vb86yjQghxxkLsM4QBoPx/GUSVluL8e7BofcXrtmOV4DBYMS+SnXxXIo14wH/oCYq7UPuV6sJHkHBobGvUpX17TQtnDLffUQIVdfUHTxxXstqIkXddIbPmjfZ77FysW6Z8TMXrtiyT1rTcsu+4+3t7XitUtKzlHRtTgQG41NKyioc3HwWrd3RRKV112KdGyHz3cdDpy5YjJ+Z/T4Xqzmb9UII0dvb7z1+ju0hFuPdn8TEd3lhWlFVvXX/CTUje5KczswFa77kF3K2AwLwB+M0GLxISDEaM5Ukp6Nu7IB/4TlHp9P3+P+9x/9vOp2OEHrz9qPLvHUfvhT8cMHW1raq6trOf537VRgMRkj4w6leK+RlpO5fPe06YeyKzft2Hz3b3t4+zdnBY8qEY2cvvf2QW1FVffTcJWkpiSVeMwUFBRBCYRFPTgYFz3ZzCTq2q76hccq85alvslky//qtyGPxulepGdvWLjmwbXVSWubyzfuYEySmZgRcvrlozvTdG5e/yc5ZvfVAZXUN+wUNdbXexUeMtjRxcxmXm/L41vljUhKU2FepkzyX5hUUnT64dZm3x7WwCDfvVUUlZXhBQcGhtlamJ/dvkZES56Tl+fh4921ZefrgVoTQrfPHSrJjZ0wef/9xjOvc5fxkvusBh6c5O+w/HohHSgaDceNOpIvnUjIfX/jlkz6e0/46+/eyDbulxEXvBZ+e7eZsYaKf8SI87n7wUDWVsooq71VbAy7f9J41NfDozoqqaptJc6JiEvDSo2ISPn3JP31w65QJ9sy1UlMZZGKgE5uYWlv3ffjA2/e50XGJlqYGFVXV7JuauRHkZCSZp7NZL0xI+MN9xwKmTBh7PeAwmY93uvdqltiJECqrqFq4ZsftB1HrfecHnzmYV1DkMmsJFvkA+A/jtJto9dYD2Pchv7DYe6VfSlTPbrURicRZrg5rdp3I/vBl8CD5oGv33Cc7aKgo/nDB1DfZo1xmd56+bpnXjnVLubm58Sml5ZWXQu462lkf2LZaUpxia2UqIiwYFBw6ydFOX0dzxcLZHgvXHjl1cbCy4rPYxAv+e4aqqWAn3TJSEjs3+I6ztSIQCEZ62rMWrbtw7fbwIWrMxd19GJ2XX3j28HYsmYWx/oLV2x4+i8MTyEhJ7N28YrSlCYPBIJF41u04/Cbrvd1IMzYLkkg8YiLCRCI3kZtbTERYSFCgiUq7HHJXUUH23JEdqsqKCCFNDVU371W3I6KWeXtgBS33mbVuqReRSKyqruWk5QkEgoiwED+ZjBAS4CeLU0Tr6huuhUWYGemeOuCnrCiPENLR1Ji9dCNLMx7dvUFSnDLO1ooiJrJ43c6Zrk4Wxno8PDzcXFwiQoLiFFGE0IOoF89fJl3w3+PsYEMgEEwNRsxZtunCtdvG+tpYbjqaGod3rBumPpilVmKiwuNsrReu2e7rPcvK1KClpfXB0xdGetrmRro/bGrmRmCeyH698M10cNsaLGestscCLuvraDKnYVkpbU2NWYvWXbl5d+/mlTw80K0K/rM4vTJgPjPKePuhFyUpyctMdx5z4UZE5LMELi4u+1EmBALhh0uZG+u1Fmd2/tu9cTlzJEAI5eUXRscljrOzlqCIIYSIRKLT2NF8vLyvM94ihLSHqfv6zAoOvb/z8Gkvd1f7URZ46Ya6Woa6Wti/qoMUR1mYZLz9UFrx7z1SKo2W8/EzczIZKQkbK1Pm0jVUlYeoqSCECASCsZ42QiivoJCTBZl9Ky5Ny3znMNpSWVEOr5uttWlSWmZ9QyM2xVhPh+Ug2FMl5ZUfP3+1NjVUlJPBpujraJob6WGfc/MKouMSHe1GSopTsNWZ7zGltTiz880GKo2WnJY5ysLY3EgPWzs5GanxY0amZb7LKyjC0mgP01CQle6yGiYGOkPVVJ6/TGIwGMVl5clpWZYm+mIiQpy0WJeNwH69MMw5y8tKO40dlful4FtxKZ6ARmtOfZNtZWpgajACS6YxeJCDrVV6Vg7cowb/bZwGAyX5f4eg6Ghq9K6wcaNM+fnJJy/cWuAxSVhQoHeZdOfrt2KE0Nxlm3jlR5DkdEhyOoZ2U3LzCvILi9vb2wkEgoONlYWJvqQ4ZcoEezKZD19QREiQzPf9qTcSiUdMVLimtq6h8d9uKBqt5VtxKXMyhBB2rGSvpwtWV9dmv/9EERPB4xwfL0lSnFJaXvkThxt1LoXEw0MRE8E+fysqRQhJSfRm7QgEgpSEeF5BYWVVzQ8XV1NWsh1pFvsqtaSs4lVKRs7Hz3bWZu3tjN419Q/XC9M5Z5baUmnN+YXFD5/FKenaYHsRWVHv4InzxaXllZxdigHwh+L0HPP88d1YT5GSvGzQsd29K6yJ1lxRWU1vb6+ureNwkYTkdA67ibDj1/WAw6PMjZlTEoncXFxc7e3tIeGR8UlpCKHAK7e0hqkL8JOxBOVV1a2t3591aG1tq6mtFxMVERLkb/znxiyZzKsoJ/OtuBRPhhDi5DyxpwtSKKJaQ9XLK/9N0NrWVl1TJy8rjde277orBfusKC+DEGKe253u1k5NRQnrRGKPl5dkbWZ46vy1mPiU6LhEK1MDbU2NXjf1D9cLw7ytmWtLozVjU0gkHilxiqvT2APbVguQ/1+bC/3s0xcAfiucXhmMNDdKfRraWpyZm/JYV2toL0piMBi3I2MUZKW2r54fcPVuWQVH33BDXa2S7NjOf2uXzOPi+n+VV1VRNNLTjk9OFxTgF6eIilNEk9IyZy1ZH5+cTiAQ4pPTz1y84es969QBv8s374aEP8QXjIpJSPnnjvHn/G8x8Uk6mhpSEv/enuUnk4dpqDInKy2vjI5L/GHle7qgopyMvo5m5NPYvILvw1eS07KexMSbGugICvB3ypxvkIJcxtsPRaXlCKGWltaUN1k/rBJCSFZKQkNV+Wnsq8Li7/el0zLfJaSkY5/VVJRsrEzDI5/W1NYjhLCxWEq6Ns9fJnVeO2N9naiYhLik19iUopKyiCcxRnragxTk/q+9O4+HcusDAP7MMFOW7IaxZ7uRGYWYMLYsZUmlSLLlxp1Ci7iWK2mnTShZ2pBbolVSSvuiW4oW3ZQiZBt7VJZ5/3hu886dYdKi5fb7fvwxzpxznnPOM5/5zXOe5QynJQY6ZJPJetGbdpw+f8Vw0kRJcdFPHuoP9gvFWjPa2nFqyrIsE1mCAvzaWuOKS0obm+jopwiLxUbFJkZv3jnS94IA8G19vRNipY8q8ouub4laIicteePOg8N552kes9jm/Tnh8bjh/MxEEERJXtbHzWlFVGxbW8c8J/uHfz9NSM20MjOk6Go3NNG3p2TIyxFp3nOlCRIlZY/idu3Xm6AlK01Ay3r6h0YupwkJCSbtPfjuXa+nywy2X+KO0yyOnCr09A8NonmrjJXfk3WE9ZoZLrgXFBTgV1dRyj15Nvt4QXtHp52VmYeL44LAiDkLlgb7+9Q3NiekZlIpurZWppwDxcc3eqoF9eDRfL+gqMCF82+V3N9/8NhwmiQsNGaek71XQNgcn6XLfvN81dCUmnG4vrFZXUUJQRBpgoSni+Pi39e40YJ9PZwflFckpGU6O07T1yGPHj1KXUUpLTPn0LHTvDw8+jpkOytTBxvzBYER5f7P1FWUkvYefFFdG7TIW1REiO0n+aCkJMWNDXTXxyWPUx07xYSC9vGTh5p7v5jQfU2UlkRbmxgTKSku+rSyCn0Xg8E4O049e/GaV0D4El93WaJURvaJv+7eT4yJFBURGk4zAPhBfaX7DDq6XqdlnZhla66sIDNqFH6+07SCizeL7z78gpvAYDAeztN3b19bW99o6+qXmnF4gdusyCCa0BiB9EPH/7p7P3jxAmVFOQF+vkBfdzweF7drPzoR5DJj2tY1obmnCn9dGiknI5WydbWBLpmtciV52cydsdNtzDfGp0bFJJpOnrQxcvlwWsW94KhR+EXerga6ZFpw9I3b93A4XpPJekfTEyeSNALD16FdSNoUNdQPbVtLaszKoNq6hkUhq3l4eEICfIY5Vg42Zrl7t48ePWo+LST/3OWQAB83J3vmMM6daXssPRGPwzn7LDt4ND9sie/asEABfj4MBuM2297HzWnNlqQj+ed4eXmkJMVTt64ODVx4ICdvUcjqsQqyWcmbbMyNhnNpAIIgPDw81maGggL8E8mayorywxmxT+4XysneOmblitxTha6+K/oHBhJjIi1NKGytlZEmJG+OnmpBjYlPm08L6e3r3blpJWc2AP5jRvyppW3NtR/MIyIhO6JtGAq9pc3DP5QgLha/IeKHmxEur6hsaWkjj/+F2fLUjMPbdu3fG7+eM5gBAAB3/6k7kH8qjysqzWd6ZeXmoeG8ta2j6EqxmrKiovyw5usBAIDVd3ATDRx9fxIzQ30ne+uwtdtqXzVS9LT/PHLqzIWrcevCJMVFv3XTAAA/nhGfJupqb+L+FGteHF5QWJJLhpHT9bo7KzcPh8PNnTGN9c6DH0VLa3tKRnZaRk517StzY4Nlv3lamlA+85Y0AMDPCVY6AwAAMPLBAPlnDeTOvr5ehH0NZPxofiGIBAAA8M19jWAAAADgOwdXEwEAAIBgAAAAAIIBAAAABIIBAAAABIIBAAAA5PsMBuu2JaPriqB/UprGkRvjh/MUzJHW0/OG3tLG+kD8z8dgMNo7OlvbOgYGBoZK+SJKyh5pUacvidjAfHb/MLH1+vqtu3gZcmrGx617yqmvr+/QsdM6Fk5iapQDuXlccn7UFt++fRexPk5K0/jm7dLPbCEAP5XvMRggCKI6VmFfwobCnN2FObvDl/r9mXvKP3QNc/H0b+Vo/nmilsnlG7e/YJ1v3rwNW7ttjs/S+sbmoVK+iLb2jrr6xrb2jp43bz+q4Ej0GkGQu/fLg1dtsqBS/kzZrChH/HCB4enr729opPf29sEqlQB8lO/30QVaGmro+pqmhpMkxcW8AsLsrc3nOdl963b9qMyNDZ7fKcTjcN/JgzcqKqvrG5unWhhzrq78OQT4+XbGrty0KlhYSPALVgvAf96IHxmUV1Qm7s4qr6j8nEp0tceTNdUfV1QODAw00VtWbkxQnWSDlyHP81tRWfXPomBZuafwMuSI9XHmM700jezLHj1hMBiXb9ye4eGPlyGrTrKJiU973d2DZn7d3ZOQmqlFnY6XIds4L7xT+hC9+Y7e0mY377c5PktT0rN1LJzwMmRP/7CGJnpPz5tFIau9AsIQBLF19Rtq1qKn5w2zoJGdW0HRVeZUT0tre2zCbi3qdDE1iqvvinsPHiMI8vfTF1SH+WmZOZdv3FbSscTLkOOS01lT0I4MVRx5P4WyKnZH5MZ4KU1jKU3juOT0vr4+zrY9eVY1ZZb3urjk/v7+obrJ2Z2hel37qnHQLTIYjJKyR66+K8TUKFKaxhu2pzDHnHu1zFkjdI+cKLgw1cXXOyC88/3io23tnZt37EFHwH3R71U1dUPt90079hjZzXvw+Clz5DfGp6IfGPt5NOa+BgD8C2MkRW7YrkV1iE/J0DJ2iNywfZil1m7dpWFoV/rwb2bKrZIyeW3zXfsO1jc2z/IKHG/skJKefejYacrUuSTqdDTngZw8HJFE0DBasyXp/OUbTc30rNw8UVWDuQuDThVeiolPI2gYeQeEt3d0dr3u9g9dS9AwiolPO1FQ5Oi+WFTV4OyFawwGo5neauvqhyOSHN0XH80/FxK9WVTVgBYc3d3d3dbekZpxGEckHcs/30xv7ex6zdZstFpRVYOomMSCoiveAeE4Iik14/DAwADabBU967jk9JyTZ6bM8ka32NvbW/uq4delf5jN8Hz05FkzvbWjs4st5e3bd0MVZzAY14pLcESSqKpBSPTmo/nnHN0X44ikAzl5nKP6uOK57hSniPVxfX19Q3ezh7XIwMAAZ6+5b/HS9b9U9Kwd3RefKChiHfMPVNvZFZ+SgSOS5tNCThQUrdmSRNAwwhFJXv5hHZ1d6BbR1p4oKIpLTlfRs7Z0WlBTVz/Ifqe3sH5+WIeObV8DAFiN4DRReUXl0VPn7l85gSBIwML5JJPprk72GmrKH1tPTV3D9pQMgoSYvg75VOGlC1eL98Svc5xqgcFgSJrq7rSQjOzj6yOWoZmX+LqH+Pvw8vK+amjaf+i4raXJtrWhkuJi06ZQxUSFF4Wsnudkj8ViDuScXBMa6OfpzMvLO2kiyS8oak/WEX0dEloJWVN9Q+RyDTXlaRbUd729Z4quVjx/SdZU5+fjQxBEgJ9v0JU4i++UslY7WW8CgiAHj+bbWpoUFF1lbTZFV9srIBzdoqiwEA6H48FihccIotXy8vCwpbD1mrU4ummXGdNWrlgkwM+nPf6XJnpr4cVrDjZmH1yuZ7BuVqNTcygMBiMsNGbQXg+6RSwWm37ouLwcccvq31WU5O2tzWRlpLwDwmfaWTrYmHOptqqm7vDJMx7OjrFRK8REhe2tzcYqynkHhLO21srMMG5dmJK8LIIgmuoqLguX7zt4LGKZH9t+Z+sj29DpkDW9AsKzcvMoeto/3HJGAIyoEZwmOn/5pq+HM/NfPw+Xc5duDLPs0+fVepaz0auJlPWs7j14HBm0SG2swu17D6gUXYquNroGobqy4tQp1Lv3y5lnC/UnktFvhKfPq4uu3LS1NJUUF0MQBIPB/Dp/9ru6MkvTyTdulyorylm8f9qzNEHC3tqs9OHjqppXaCUkDXU5ohSCIKNG4SdNID19Xt3IMYXChsFgXC0uUVaUszQ1RKsVGiO4J37dudw9IsJjbpWUmRnpG06aiDZbRppgZ2VaUvboefWHl4Hr7un5YHEdsia6aLO0pIT2+F9e1tUzZ1e4+IRuMg26xZq6hnsPyq1MJ6MnhDEYjLmRvslkveKSsv7+fi61Vde8unm71IJKERMVRgsaG+hQ9LRZ85hQ9ORlpNHX+jokazOj8ifPOjq7/kl5v99ZcQ6dLFGqMGf33oT1EAkAYDOCRwZTTCjOPksDFs5H/03efyh7T9wwy6qOVfhjOU2WSEAQBMfLq6GuIioiRG9pq6qpK7x4XWGCBVvm5pY2thpe1tYjCEKQEGNL7+l5U/uqoezRE23TGWxvNTbRZaUJw2whmzdv3tY3NosIC40R5OfY4tuXdfUEcTG+0f88nxWDwRAkxJ9X1zTTW3/593LtnLgX5/8+zgaj6C2tZY+elD16sip2B2u6vIx0d88bLt+/nDtLkJ+fLb+YqDAPDw/6Go/DiYkKP3n2out1N5f2cA4dAGAoIxgMNNSUZ9pakqjT/TxdktMPzbSz/Kg5IubVREx4PI4gLuZkbx0TFSTAx8f61hhBgQflFawp8rLSCII0NrNfX4jH48REhSl62kmboqQlJVjf4ucb3f2R1+AzjR49SpogcaukrLOL/euJj2+UvIz0y7p61hsUmugtqmMVBp1u+qjiH3vTwIgSFBRQHavg6+HsPmc6azoWi0UPI4bCubO6urs7u14TxP8fHljffdfb29LaLkuU4l7toEMHABjUyF5NtDo0MHtPHANBsnfHrQ4N/MzaBAX4tbXGFZeUNjbRxcVExMVEsFhsVGxi9OadnL8QVccqWFApx/LPtbZ1IAjCYDDSMnMUJlhcvnFbV3v8zdulFZVVaCWCAvw79/65JGJ9Q/NwJ0k4oTMblVU1BUVXGAwGgiAdnV0LAiMc3Rd3dL7W1yEXXrx+pfgOmrn2VUPe2YuTJpIU5T68XjE/H9/nFP+a5GWkx49TvX7rLhaLRce2qqZuwZKI3Lyz3AsqyBEpetpnLlxt7+hE3s+5sd01du7yjZq6BvT1rZL7Zy9e01RXERRgPw5jNejQWc32oQVHc17jBMBPbsTvM9BQU/6Ek8aDwmAwzo5Tz1685hUQvsTXXZYolZF94q+79xNjIkVFhNgySxMkPF0cF/++xo0W7Ovh/KC8IiEt09lxmr4Oub+/39t1pl9Q1JOnL3TImsdOn8/KzdscHSJHlGrv6OLSADVlBWmCxIGcky1t7e0dnT5uTljs/6Opga6222yH4FWbXtbWmxrqZR8vOFV4aUdMpJSkuJ2VqYON+YLAiHL/Z+oqSkl7D76org1a5I02W11FKS0z59Cx02MEBUga6vo6JGYKLw+Pvg6Ze/GRxtZrTXWVoXKKigj5ebi40YK9A8N93Jzore2JaZmyRCkrM0PWgeKkIEuc42ATFBXb3tHp5ToT3Vlsea4Vl7jRgpf95ln1si4hNZNK0Z07y5Y5cTQUtqHbk3XkeVVNZBCN+yEFAD+h7/ems0HJSBOSN0dvT8mIiU9raWu3oBrs3LTSwtgAPT3ICoPBzJ1pK0uU2pq0z9lnmbqKUtgSX2/Xmei3wIY/lqsoKaRnH/9jw3ZzY4OdsSsdp1p8cPXgiSSNyCBaTHzalZt3QpcsZPz7cnUBfr7YlUEkDbVd+w7Fp2aYGxtkJW+2NKFgMBgpSfHUravTMnPSs4830Vum25ivj1g6aSIJbbbbbPu/nz5fsyVJQ11lS3QIBoNhpmhrjTPUn8i9+Ejj3ms2U0woh3fHxadmePqHiYkIz7C1XOg+54NHMBgMhuY9lyApHhOftiAwwoJqkLDhj90HclnzRAUv5uHBrlgZ29LW7mRvtWKxz3AOjNChQ1eKbmlrtzE3zkyKNdAlD6fjAPxUYKUzAAAA3+uziQAAAHxNEAwAAABAMAAAAADBAAAAAALBAAAAAALBAAAAAALBAAAAAALBAAAAAALBAAAAAALBAAAAAALBAAAAAALBAAAAAALBAAAAAALBAAAAAALBAAAAAALBAAAAAIIg/wPoXtR3pEoTdgAAAABJRU5ErkJggg==)https://a-z-animals.com/media/2021/12/shutterstock_1934338307-1024x535.jpg)"],"metadata":{"id":"m0w2sW17y8nc"}},{"cell_type":"markdown","source":["#Plotting Multivariate Data\n","Once you have read a multivariate data set into R, the next step is usually to make a plot of the data. The idea is explore the data and visualize patterns or relationships between variables and data (groups?)\n","\n","See a good introduction in Multivariate Exploratory Methods in: https://bookdown.org/rdpeng/exdata/\n","\n","\n","\n","**A Matrix Scatterplot**\n","\n","One common way of plotting multivariate data is to make a “matrix scatterplot”, showing each pair of variables plotted against each other.\n","\n","One basic possibility is use pairs\n","\n"],"metadata":{"id":"-0l07YKjrTis"}},{"cell_type":"code","source":["# Basic Scatterplot Matrix\n","pairs(wine[2:6],\n"," main=\"Simple Scatterplot Matrix\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":437},"id":"VMDYjfTZv9JS","executionInfo":{"status":"ok","timestamp":1717434513493,"user_tz":-120,"elapsed":937,"user":{"displayName":"toni Monleon","userId":"05269440626861479105"}},"outputId":"14ae7b3f-b5e8-45e8-852e-c0411ca2b594"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["plot without 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PZRSSluQw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\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SwRJGOZ6mw47Kz1ovqUB6W0qGQKCzJt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aQZxkcUzyPKlEOGbVWBdWRHIfK/aDNxz02MAKDkJCm2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\n/KmDwKuIJsANvstxYLf9azrjiNEeysrAZwwhDVU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SyrLWHayEZ5LBxkhtyyo8Jbp92XRo29yfiQxWUaOZts1NIagMd+CsT0/ctAu9tEQm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\nrVVPN6Od/G75c1cAIb1pGrB07aL++vJsH+M0IW1XfYfL2CKrbIl0yfG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can use the “scatterplotMatrix()” function from the “car” R package to do this. To use this function, we first need to install the “car” R package (for instructions on how to install an R package, see How to install an R package).\n","\n","Once you have installed the “car” R package, you can load the “car” R package by typing:"],"metadata":{"id":"z9ojE0OEwABD"}},{"cell_type":"code","source":["\n"," #install.packages(\"car\")\n"," library(car)"],"metadata":{"id":"x4R14gamrShu","executionInfo":{"status":"ok","timestamp":1717434603333,"user_tz":-120,"elapsed":73501,"user":{"displayName":"toni Monleon","userId":"05269440626861479105"}},"colab":{"base_uri":"https://localhost:8080/"},"outputId":"1281c1b3-c26e-4707-df7e-5195d6b7adc0"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["Installing package into ‘/usr/local/lib/R/site-library’\n","(as ‘lib’ is unspecified)\n","\n","also installing the dependencies ‘SparseM’, ‘MatrixModels’, ‘carData’, ‘abind’, ‘pbkrtest’, ‘quantreg’\n","\n","\n","Loading required package: carData\n","\n","\n","Attaching package: ‘car’\n","\n","\n","The following object is masked from ‘package:dplyr’:\n","\n"," recode\n","\n","\n"]}]},{"cell_type":"markdown","source":["You can then use the “scatterplotMatrix()” function to plot the multivariate data in order to explore patters and correlations.\n","\n","To use the scatterplotMatrix() function, you need to give it as its input the variables that you want included in the plot. Say for example, that we just want to include the variables corresponding to the concentrations of the first five chemicals. These are stored in columns 2-6 of the variable “wine”. We can extract just these columns from the variable “wine” by typing:"],"metadata":{"id":"fmJpCO6HrSOj"}},{"cell_type":"code","source":["wineb<-wine\n","#remember variable definition #names(wine)<- c(\"groups\",\"Alcohol\",\"Malic acid\",\"Ash\",\"Alcalinity of ash\",\"Magnesium\",\"Total phenols\", \"Flavanoids\", \"Nonflavanoid phenols\",\"Proanthocyanins\",\"Color intensity\",\n","# \"Hue\", \"OD280/OD315 of diluted wines\",\"Proline\")\n","#names(wineb)<- c(\"groups\",\"Alcohol\",\"Malic acid\",\"Ash\",\"Alcalinity of ash\",\"Magnesium\",\"Total phenols\", \"Flavanoids\", \"Nonflavanoid phenols\",\"Proanthocyanins\",\"Color intensity\", \"Hue\", \"OD280/OD315 of diluted wines\",\"Proline\")\n","scatterplotMatrix(wineb[2:6])\n","\n","#This function present a lot of possibilities to catch patters, specially in regression: https://www.rdocumentation.org/packages/car/versions/2.1-6/topics/scatterplotMatrix\n","\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":437},"id":"snrXVhkarSH0","executionInfo":{"status":"ok","timestamp":1717434963905,"user_tz":-120,"elapsed":2048,"user":{"displayName":"toni Monleon","userId":"05269440626861479105"}},"outputId":"533ad2a6-64e2-4b08-ef9e-a35b6132361b"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["plot without title"],"image/png":"iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAIAAAByhViMAAAACXBIWXMAABJ0AAASdAHeZh94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\n8+f52FhDOjp89RULFshVd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I25saNQgL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\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WHqVJ48Ydu24n8mip/e5s28eqW80cyM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xIrNno6WFQoFCQVAQ/ftz9WqucPH69axd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\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OSYkHsPYmJYsYJ796hShQED/lMnePIk\nvXuLsUTp0vzyy6eWOxwWhoaG+MDnNw0a8PQpEREFsfKbVxw/TpMmtG9Pv36Eh+Pllbsp9msy\nM4mMpEoVlRmxElNTPDzo2FEsX1Srho8PN2+iqys0AXx8GDCA2rXp2JG7d1m8mK5dCQwUw2zl\nNJKyV1Rq+jx4IIz1Jk7k+nWmT+fhQ9q3Z/duIZwxejQzZ6qKikqV4ulTnj9XuWBlZnLjhmqN\nxdqaTZuAjy/yBjGC/Tv93uvXadqUP/+kRQuuX8fenkmThEDxjRs5puuUQYxSqOvpUySJGTNo\n317c8+Bg6tRh7Vr09ER5rHLn69extCQtjefP8fLi2DGh+gQ4O4saRDs7kpIYNoyFC8UQF7Cx\nyUvfwtKl8+xQr2nShB9+YPdu4Tx2+DBHjzJhAtOn5/25igKfbGDXpAnDh9OsGS4uZGZy/jwT\nJ/6T98i/0rw5zZurXurpve1nVaqUWIl4PSWupiaqLzMyiItDJiM9nQsXyMpCTU10l927c+kS\n9evnWPydOJHbt/nxR9asITOT+/epWZO1a0WQoVDwww/Mm8fz57x6xaRJTJ3KunVs2kRKSi6r\nUUlJjBvH06fUrSvU8rZu5cULHBxUOQfvir6+MHx8TXJyvnh0xsRw+zYXLjB/PmXLUqUKMhlb\nt+LuzvLleHuL2o435aOVKN+IV69YvhyFAgMDUfavJCUFmYwaNYiIwMYGR0euXCE9XbUwXbcu\nV64QHk5aGtWqqcQg9u1jyhShEeDpyY8/ivmAyEgyMoiP59w5bGx48gQNDZFf/OgRt26hpsaY\nMZibc+rUh96TcuWYNu0d9ldmz3h7s38/GhrMn0/nzly48KHNKFJcvEjVqv+UD5qHWFlRvjwn\nTnxMgd3YsTmMDapWZcKE3DX/MjI4dIg//iAzUzipAJqa2NkRGcnBg6plspgY7t7F0JDISFVJ\nVmQk9erh68u1azg6UqMGY8awcSNLlhARQVKSWHvl/3YyyjRWLS0uXeLgQaysaNiQsDBevEAu\np08fVqwQC7KAnh4PH6KrS0IC33zD6NFkZTF1qlDueIuPLqpT8teoLiODTZtYuJDz56lalbt3\nATFZ0LUrpUurXBbf0gdQvpwzR+gDhIaKBEeZjBs3KFmSmzdFgrKypOzhQx4/xtyczExWr6Zm\nTZo0YcAAXFxYuRJ1dS5fJjUVMzM2bqRZsxyTEUWcOnX44Qc8PXFyQi4nNJRvvxWTPp8kn2xg\nB/j50aEDBw+ipsbPP+dxsrNyJmbZMvr3R02No0c5fhxLS4YN49Ytfv0VPT1SUggLE2NNuZz6\n9TlzhtRUUYM5eTIpKTx7Rv/+ORyr0tLYuxczsxxrDeHhdOzIxYvExVG5Mj/9RN26WFgQHc39\n+1y9iqcnDg7s3JlLVdH48SgUhIair48kMXYs69YRG/tBHZ+JCXXrMnYsGzZgaEhMDLNn54ue\neFwckkR8PK1a8cUXXL6MJPHzz/j706iRUPV8szt70xo4Oxt9fdHleXgwaxaNG1OmDE+fMm4c\ndnaigC4hgevX0dLi+HE6d1adWkMjl8+MsTHz5+fi/Ks08/j5Z8aPZ/RoADs7njzBzo5NmwgK\n4tYtjhwhKeltTewCYPdutLX55Rex3D9rFgcO/K3Jx0fKuXMFWs3g6sqpU/nrcpG3hIWJrAkl\n3t6MGvV20n1kJNu3ExSUYwhUrhwdO9KsGePH07ChiOpeveL77wkOFqqZjo4sWYKZGTNmsH49\ntWrh7i5KLytUEEn6SvEOf39WrBDfVoUCBwfi4rh/n4wM5s5FTQ1NTX7/nRIl0NXl2TORt1q+\nPK9ece4czs7Mm0erVnzzDRs3snw5gKMjO3diZpbPd7AwePCAZctYvlxVT3DzJgMGsGQJY8Zg\na0uPHrx8KSSTnj4VQgdvZkDWqMEXXzBpEiNHoq7O3bts3IiTE+fP8+SJquc8dYrVq1m4kMxM\nEfkpC/JiY0VeXalSBAVRqZL4eIwZQ+vWhXFHPoBJk/DyEnInCxbkKOP49PiUAzvAxQUXl3w5\nsoUFs2Yxfjxr16JQEBcntCeUE2Y7dohqfD8/jh1jxw4kiaNHMTBAkhgzhthY/PwICcklIyEh\ngaws4uMxMyMxUXi/vnpF3br8+Sfp6cLibOVKFi3CwYF9+9ixQ0jd5lr8deQIw4YJsXiZjFGj\n8PPj2rV/SWj9V9ato00brKywsiI6moYNmTHj3WaS/hmlQY2y36lZU6T7AK1bs2ABERF07kzZ\nsoSGkpwsJlOVXmGWlsyeTd++vHzJ8+ci0vXzw8uLChUoX57YWMqWJSSEn37C05MyZUhJITub\nwMD3fzxUq4auLmlpxMURE4OODt7evHrF4MGYmAiV1/BwHB0LwZBK6Zv3ZhJnhQpi3P/JcO5c\nLmVA+Ue9erkE90UZc/McuQr37wsfaiAhgd272bIlhxaaoSFNmnDlCtHRbN3K4sXY2jJlivjt\n4sWcP09QEIMH0707u3czbhwrV9KrFwEBXLrE+vXUrElSEkOHCrcrICiIwED09DAz484dzM25\ne5cePZgzRwjazZ+PqSnLl3PkCGpqyOW8eCFMDgGZjAULqF6dsmXR0SEqSmS+5uoY+bFz6RLz\n5rFxoyr4trSkc2cWLmToUJFTpKND164sW4a1NTduoKNDYiL830hXklBXR0dH9PNXroiEucqV\nRWaLsqBeR4eMDDp2JCEBIyPq1CE8nNRUFi1i82Y6dODOHb7/nuPHqVcPAwNRFfuRUqtWHg//\njh49GhgYGBkZ+eLFCz09verVq/fu3bv2h6wM5hEf52x10aBlS/buZeJEhg5l1y5MTUUHlJJC\nXJyYvho9mrAw2rZFT4/q1Zk4kT176NlTaDOePZvLYZXit0CHDly5go2NOOySJTx7hqUlUVHI\n5QQHs2uXKNhWJvj/XWZGrvq9H75OYWvL5cts3szo0Rw8yP796Oh86DFfc+iQmOeXyWjblpQU\npkwhKwtjYyHGq8wIzsrCwABjY/FgUNrjLl0qVOuUKEtW9fQ4eJADBxg9mq1buXSJcuWEh+bE\niSxbxo0b/5J19M8oFPz0EyNH0ro1s2ZRvz6PHqGjk+POK38u+BUiBwcuX1a5AqSmcurUx7SM\n+K88ecKtW0Jhu2BwdeXWLRISCu6MH0jnzkyeLOrWr19nxAgqVSIlheHDadyY6dNFVCeX4+rK\nwoWcPMns2QQFsWIFvXuzbBlbtqiqEPbu5ZtvRF6Evj6+vpw4IfLe+H+GqySRkaGqEFL+VadO\nouqoVSsUCuLjRYwik2Fjw5o1+PhgYsLhwygU2Nujr0+JEmhooK7O6NFUr05cHJGR4tNbuvQn\nGNWdOYOXFzVrEhAgorp69Vi/npgYMXR5UzVQmWg4YAD6+gwdirY22toiqmvWjBUrOH2acuXQ\n1eXCBZKSSEwUU6ft2jFmjMh37NlT1K9kZHD2LI8fU78+kkSVKgBlyxIQQFJS7kLTnzNLly5t\n3769hoZGjx49Ro0a9dVXXwHu7u7r1q0r7KZ96jN2+U2JEqoZaRcXfv8db2+SkkTWgrk5e/ei\nULBnDyEh2NurEkHkckxMhBjba5KSmDdP5Leqqes81CQAACAASURBVBEdzejRmJlx7RqSxM2b\nyOXExoov8+LFLFhA/fq8fIm/P/Xq/W2CUZMmLFtGly4YG5OdzcyZlC5NxYp5cPmamvkyIb9l\nCz4+IqVXWRoC3LqFh4dw/QISE5HLRT2pnh7ly4t60uxsUTXG/5dlMzJExCmT0aBBjlVvoEoV\n0X99OD4+2NsTEEBCAgMGMHAgvXuzaBEtW4rUcj8/atQoBDebNm2oUoVGjRg+HE1Nli1DQ4Pu\n3VUlbx87oaFoaanqfwsA5QSt8hn8UTBlCrGx1K2Llhbp6ZQrR3w8aWkqu4gSJfDyol+/HDWk\namrUrZtLGu6TJ2I3Fxd++405c5AkEhNZvRp1dZyc6NMHdXUyMnBwQCYjMZHSpXnyhGrVqFOH\nyEhCQ1m/Hk9PLCyIiyM9nZgYvv6a9etFWUaJEowYQXY2Bw7w559YW2Nvz7JlLFgglno/SX75\nhW++ET+rq9OhA6NGqRYNlebmr6eKnz3jt9+oW5cWLVixgqAgsrLo0oVTp7h5k+bNKVmSrCxW\nriQjg5QU6tZVDf4DAtDXR1+fMWNYvVqonNaqxcWLzJjBwYPI5SQkiFGxpqaQRCnmTebPn3/4\n8GGHnBVb3bt379OnT/e35HMLnOLALs8YMYKzZ3F3p3p11NTYtk2k7QN792JlxcmTQu02IYGD\nB4mJUS2GZmWxaRPz5wv3WE9P0tOFcaqyNCwzE19fGjfGzo7bt7l5kzFjmDmTXbsIDyczU+yc\nK9On07AhFSrg4sKtW8TFERRUdDOLV6+mXz9Rq//ypSoMUi4mKheUU1OFeoJymfXevRz5Iq+L\n71q3JjKyQAOpmjVzhBeLFuHmhq0tTk5cucLz5+zfX3CNeY2GBiEhTJ7MnDlCHmXaNJWP+yfA\nqVPUqvW2ini+ogxfPqLATlOTRYuws2PVKm7dIiZGbFeuxtrZIUmsW4eNDV9++e9Hs7dn/37c\n3Rkzhq5d6dABdXX69SMpCWdnsrKYOZPMTOzsuHmT8eOpVInMTEqXZvt2PDw4fpwXL0RwFhOD\nmhouLjx9yuLFYnSq1HivUwd7e/r04d49fviBSZOE6fPEiQXkXl3wKEX+tLTo2ZOxY9/Ox5XJ\nRAIMMH060dGYmTF6NJqarF7NokVi3bxNGzw9+eEHTE2RyfjlFyGZ/uwZ1aqxcSO1arF9O927\n8913ZGUxfjzq6igUXLxIixaMGkVoqPDmVhIayv37ODkV6K0o+jx9+tTuL/VHzs7Occq07kKl\nOLB7f9LSUFdXSYLp6rJ1KyEhXLuGvj579rBjBxERnD5NaChr1zJ2LB07YmzM+fNitnzCBBYs\n4Pp1liwhPp5Jkxg6FA0NDA3FwZWGB7duYWKCr6/ohc3McHGhbVvWr+fmTby96do1dw9vJYaG\nnDvH778TEUGLFnz5ZdFdv1i4kJEjkST09Jg+neHD8fXlwgVKleLwYTQ1iYigZ0/CwsRaz/Xr\nfP01168LY0pDQ1HV5exMRAQ7dgh3xQJAmSH+lj6cpSUREWzYQFQUHh589VWhmU+bmLBwIQsX\nFs7Z85uTJ6lXr6BP6uLCmTMFfdL3Y/dufv6ZXbtU+QlK/1ClDuLmzVhbA2zdyuTJNGr077ZO\no0fTpQtJSdSpQ7VqxMTg5oabG40a4evL2bOcO0d2tnB2qVyZTp2EhqJMxurVlClDRgZ37mBl\nRa9edO+OmRk1a+LiQpcuPHnCihX07SumiwBra5VF/afNjz9Spw4uLn/r4uPmRlgYNjaULUvX\nrnh4iD7H2JhJk/D2xseHhATKlqVRIw4dQlcXX18OH+bwYZo3JyKCSpWYNYu+fTl+nMePCQoS\n03VPn1K2LPXqMWwYO3diaCjUWxMTWbGCAQPybGXjk6FixYpLliwZ9oYmpCRJc+fOrV4E0lyK\nA7v/xMuXnDhBQgI2Njg7s3cvvr6iXN/UlAULRH6PUvBMOYg/cYING9izhwoVCAqifHk2b+bb\nbzlxgqpV6diR2rUZOZK2bZHLGTCAsWOxsAB4+JC4OH79lQULOHMGHR1atiQi4m01RT09lX3t\nv6KhgY9Pnt2NfCIwUGTm6utTqhQjRwI4OHD3LleuULUqc+YQGsr48Zia4u5O375Mncrp02hp\n4ePDrl34+WFnh78/N29StSoxMR8kcPMfiY5m5Eh27yY7Gzc3Fi7MIeanUNC7d7634XMmM5PT\npwtBecHFRXiYFtnJbyXnz+fIl6hShZ496d6dFStYuJBWrURUB3TogL8/YWFC6+uvZGZy7x63\nbhEbS7t2nDlDWBiamlSpQlISmzczd67IoMjORiYjKQkjI+rWpVo1HByoUgU1NWbO5Px5nj5F\nXZ1795g6lY0bCQnh7FlmzSI4GF1d/Pzo04fTpwkPx8yMFi3yMn+3KKOtrcok+TvMzQF8fHL0\nM3fu4OvL+fNkZ3P4MCdPYm+PtTWursybh6UloaGUKoWZGVevMnw4Njb4+XHiBG5urFxJqVIM\nHsymTWzZQsmSrFtH48bMnMmff6Kvz9y5ovyrmDdZsmRJu3bt/Pz8qlatqlAoUlNTr169qlAo\nggpsOuHvKQ7sBA8fcvUqJUpgYvJ2CBUTQ//+PHqEhQX37mFvT3g4pqYMGUJ6OitW0KsXhw+L\npJPUVFau5OxZNDRo2pQuXZDLiYtj6lQiI7l3jwYNaNOG4GCmT8fUlJcvcXAgOppjx0QlhDI3\ntlEj+vYVDVi/njFjCvJmFA7K9O0SJXj+nGbNGDOGPn1ISUFdnQ0bxOPz+XNsbNi5k40b6d6d\n5GRkMrKzOXlSiDkdOkRMDGXL0rYtu3blUjWStzx/Lhx+HRxEA5o35/JlEaMXUwBcuMCLF7i5\nFfR5XVxITiYqKnc1uKKDuTkmJmRl8eWX9Oz5L1Obfw1Sk5M5e5bQUC5f5soVMjLQ18fGBktL\nGjZEoeDpUw4c4PlztLV59Yrp0/H0xMwMMzM2b2b4cFauzHHAxYvZsIGvvsLRkW++ISaGBQtw\ncSEujjlzxBlnzcLCgsRE4c1oZERQEDVq5Ol9+WhRKnf6+uLoSL9+lCnDxYvCaQOEHblMxuXL\nKBRs2sS33zJ1KlpaomZW2SV6eXH4MEZG7NwpDrtyJSEhjBlDp05iy9y5BX1pHxdOTk63b98+\ndOhQVFSUsip2/PjxjRo1kheBRIHiwA6gYkV27hTGUICxMSYmGBuLHw4dwsBAqMumpzNuHMCi\nRSIJzMaGYcOYPp2RI9HQoF8/MjJo1YrERObO5Y8/cHMT3qAKBU+ecPgwZ87QtSsDBzJ0KDIZ\ntWujUNC9O5GRTJqElRUVKzJvHkuXCnP6pUuFm9mnja8vdnbs3k1CAkuXcv48IBx+r1wRpgKJ\niZiZMXUqv/+OmhoKhZBFsLAgIYExY2jaVOzfvTslS/7tikZeERzMnTsYGoo0o3XrSElh7drP\nIhAvIhw8iINDIdhGlS5NqVKEhhb1wM7aWjji/NVHTpkx0q+f+Jps305KCjVqkJnJhQscP87x\n41y9io4O9evj5cXUqTg65jAGCAqiXTtMTGjQgJs3SU7mzBnRQwKlSvHkiVDHfZPJkylZkrAw\n8dLDg0aNhPf08+e4uJCYSEYGvXqxbRtt25KZSZcuXLlS1CdHC4Bff2XQIIBatYiOxtubOXOE\npgxgbc29e9jYEB2NhoaokvH1RUsLSWLuXCwsVB/X+PgcOTnKXvS1YN7nQ3Jy8u03nRwB0NbW\nLlWq1L/+rYaGRosWLVq8tuAoMhQHdgDTpzN9OsnJxMXx6BEJCcTF8fgxjx7x4AEJCZiYsGoV\nyckq6c7Xzl1KQkIICVG9VCpnymSEhxMTg54e3t5YW3PkCLGx3L3L/PmMHSv0JP39RXXtF18w\ncCDm5qxZg4cHhw5RpYpQp9u2raDuReGhrU3XrqxciasrwN69AF9/jbo6e/eKwK5KFVav5tQp\nNDWRydi5ky+/RFsbW1sOH0ZXl8hIdHWJikJLi9RUle9QPhESIpYClY46w4djbU1wcHFgV3Ac\nOCC8SgseZ2fOnKFnz8I5+3/n7yzVzcwoV442bahTh5QULl/Gw4Pp0zl5kvR0XFz48kuaN8fZ\nGXV1srNZs4YOHXjwAHt7Jk7E1ZUBAyhdmnv3xAH19AgKUkVyQUFUq5ZLUUtcHE5OJCYSEYGh\nIfXro6bGyZN07MhPP5GZSblydO7Md98xdChOTuzaxYYN3LxJpUr5do8+BjIzGTWKuXMZPpwO\nHahWjSFD+P57Iez88iV79rBkifAX0dSkZEm0tbGxoWpVEhK4f59t21R1JzVr8tNPvHghTBSv\nXePatQJV+X6TW7eYMIFTp9DXp0MHxozJXZM1z8nOzg4ICAgICPjrr8LDwx3e3aPw8uXLISEh\nY8eOzYvWvT/FgZ0KQ0MMDUWxvZLMTO7eZds29u5VZaLUqMHly8THo6GBkRFPnmBmRp8+6Omx\naBHZ2UgSGhoMGoSjI8OG8eIF48aJUWxMDDVrkpnJDz/wxx88ecL48UKu1sMDTU3CwmjRAldX\noqL47Tfu3aNNG3x83vlTnpn5sTocV6hAWBg//cTkyQC9ezN7NitW4OxM5cqsX09aGkBGBlZW\n9OtHhQo0aCAUAf39kcuJjKRBA5o1o2ZNbtzI34dBRkYOiVQTE7S0VCnqxeQ3aWkcP15ooqku\nLh+3gYdMhp8fK1dy/DhxcWRlcf48LVuyahXu7hgZ5dh5xgz8/Bg5kooV2b+fxo05coRHj0R2\no7LDmTaNkSPx8MDLi1On2LJFZXjNG52SuTnnzmFtzatXZGZia0t2thD1UDbg4EFRtV2jBra2\nXL/+toHh58n16zx/Ttu2Ki3uli3Zvx8TE1JSyMri7Fmh666sJKtaFVtbFi/m1Cmys6lVS6Vy\nJUn07cvy5dStS48ePH/OL7/g7S0G1QXMw4e4ulK9OpMmCc2vy5fZurUgTq2mpubj4zNp0qS3\ntsvl8rJly77HAaOjo7ds2VLogd1nP7WdG1lZ+PtjbY2mJi1aYGBAYKD4VXY2hoZIEnXq8Mcf\n/PQTlSsjl1O9OsuW4e1N3bqoq1O/Phs2MH68EO18PR4oV05YmuzfT2Ym7u4ifAGePSM9XbWi\nVLIk337LggX06/duUd2qVVSogKYm1tb4++dQs/woGDCAvXsZPZpu3QB27KBmTRo0YPBgGjRQ\n1SFqaPDwIWXLsn8/jx8Lb1agXz8WLKBLF77/HqB6dZo3Jzw8v1qr7Afbt+f4cUJD6d6d1NT3\ntN8t5j04fBhJKrREBRcX4Sb8MSKTcfEi9eoRGIi1NePGERbG/fusWkWnTm9HdRkZTJvGihVM\nnoyPD2vW0L07P/4oHEirVkVLC0tLlQ/yqlVkZXHypJhM3bePWrVQKDA1ZeRImjUjJYXSpQkI\nYMwYofHx1VdUqkRiIo8e4eLCxo1kZZGVRWIi4eHo6uaoFfgMOXFCKHoqZ5FevAB48gQ1NXR0\nyMxEX59evbh5EyAri4cPKVOG1avZtYvHj+nRg/BwKlTA0ZHGjTEwEJkqtWuzaRNHjjBmDBs2\nFM6lLV6MtTW7d9OzJyNHsn8/27eLVJwCwNDQ0OYvvF9UB7Rt2/bcuXN528L3Qfrk2LBhQ8mS\nJT/kCJMnS8bG0pIl0tGj0owZkoaGpKYmeXhIo0ZJtWpJRkaSv7+kpyeBBJKJibRnj+TtLQ0f\nLh0/LqmpSSVLSoMGSSDVry/p6krW1hJIM2dKkiQdOyaVKSPp6kq9ekk//ijp60uHDkmSJCUn\nS507SxUrSi9fftC1r1olKRTSrFnS0aPS4sWSsbE0bdoHHfBdGTduXMuWLf/7/u3atRsxYsRb\nG3/7TdxbkNzdpenTJblcUlOTQNxMmUz809KS2raV1NQkuVz68kvJ2lq6fFlKS5Ps7SUDA8nR\nUdq3T/riC8nMTHrwIE+v8/9ER0sKhWRjI8nlkkwmlS8vqatL587ly7nynPLly69ateo/7pyW\nlgacOnUqX5v0rgwaJLVoUWhnT06W1NSk48fz/USnTp0C0tLS/uP+q1atKl++/D/vExEhzZ0r\nnTz5n/qcy5clkBITVVs2bJBKlpTs7SWQOnaUjh6VpkyR1NQkLa2/Nl7S0JBGjJAOH5Z+/lky\nMpK0tSUjI0ldXXzH1dQkNTXp55+lqVMlDQ1JLpd+/lmysJDs7aVatSRNTUkul1av/o+XXviM\nGDGiXbt2/33/li1bjhs37p/3iYyUdHSkvn0le3vJzk4CydFR2rZNMjOTqlaVZDLJwEDVZypv\nqZubVKOG9OOPUna25OkpVaokzZ8vgWRoKKmpSYsWSXv3Sl5ekqWlFB//YRf8wXh6SqNH59hS\nvnwBveNqampDhw597z/fsmXLtGnT3uoVu3bt+sHt+lCKA7u3yc6WDA2ldetUW6ZNk8qVkxo3\nlipVktq2le7eFdvj4qSkJPGzs7Pk5ydJkjRwoOrbZWQkBQdL3t6SqakEkr6++H/wYMnDQ1JX\nlzp2lNTUJGNjSV1dqlhRCgt771YL7OykGTNUL9eskYyMpOzsDz3sfydPAruMDElLS/L3l0C6\ncEGqUUNq0ECSyyUQ/b5MJmlri5ssk0l16kgREVJ6utS2rQSSlpYEUs2aUmysJElSZqZUrVqO\n25K3bNsmmZhICoWkpyfp6kq//ppfJ8pzPvbALjtbKl1aWrq0MNvg4CDNnZvvZ8mPwO6dSEyU\nZLIcI5YZMyRnZ6l+fRFSyGTiq6euLr3VzC5dJOWTLiFBsrKSKlWSQHJwkLS0pBEjJB0d6Ycf\nJB0dqU8fKTpamjhRsrKStLQkAwNJLpe0tKQ2baSTJ/PwUvKd/AjsBg8WA5joaMnRUfWI8fKS\nli+XNDVVbwFIZmbSxIlShw4SSIMGSVeuSCBduyalpkoymWRoKLVpI/XqJUmS9OqVVLlyQXyA\n/5kBA6QOHVQvX7yQFApp376COPWHBHY//PCDqampl5eXmZnZhAkTXm/X+uvgpsApXop9m4cP\nSU7GxUW1xdSUmBhu3sTSkiNH8PIiKQnAwkK1YFGjhhDXmD1bZFzJZBw8yLVrhITw7bcYGWFk\nRNeuJCayZAnBwQwaxM2b3LzJqlUcOEBEBI6OH9Ty7Gxu3MjR8nr1ePqUIqCD/W5oatK5szCR\nTE/n+nXCwkQxv6kpXbogSSJB2NwcKyvOnBEaNCdPijdFWT2npwcgl+PszNWr+dXaL77g1i22\nb2fjRqKjVSI1xeQ3Z87w4AFt2xZmG1xcPgsPTRMTPDzo35+wMNLSCApi9my6d+fGDVasIDiY\n0aP59VdiYsjMFKuBr4mKEvlzM2ZgaUl4OHI5mZls2sTixaSmMns2qakcPoydHa9e8eQJ0dEE\nBrJnD4mJ/PlnIahPFzWiokTHXq4cR48C6OnRpw/PnjFoEAMGiAoebW1KlODJE7Ky2LIFe3tW\nreLaNYyNqVSJ4GDU1LC3x82NqCgAdXVq187HvvE/8tVXBAWxeDEpKcTE4OODlVXhZPu9E6tX\nrz516tSOHTsiIiJ27dq1sCjpvxcHdm9jYYGubo6srEmTMDDg9m0OH+bWLWSyXGoex4/n4kXc\n3Vm7FmXtsyRRqxbTprFqFenp2Npy/76QRFHSuTPh4Vha0q4dDRvmgSGSmhrly+do+aVL6Ot/\nlJpqS5aIPF9XV9LS0NYWFmEPH3L7NnI5ixYhk7FwIbGxwovdx4c2bYiN5aefMDTk1SuUCayS\nxOXLeeON+3cYGdGiBW3aYGaWj2cp5i1+/5369fNd0eafqVv3swjsgNWrsbSkZk10dOjcmYED\nGTIEGxvCw/HwwM+Pvn25cgW5nPLlc/yhra3olM6do107oqPJyiIhgblzRcW6hgYDB3LzJlOm\nMHcu5cphaYmXF82aiYLNYmxtRVUEiLLWFy+EM+/Fi3TvTmAgO3eiocHatezejZ8fR4/i50d6\nOrNnk5TEgAF8/TUdOnDrFhcvUqECQHY24eH52zf+Fxo2ZPlyJk3C0JDy5YmJ4Y8/PgJJ6tTU\nVFtbW8Dc3Dw4OHjx4sV7lVIORYDiwO5tlD4QQ4eyaRNRUcybR3w8w4aJgMzEhOHDc3H8LFeO\n8+cpXZply4TBjpUVc+dy4ADPnuHnx+DBKBRCIlJJYiJ6en+rRPB+DBzIpEmsWkVUFL//zvDh\nfPPNRyn+ZGDAzJkAa9fi5MSjR9SuTWgo2tqcPYuWFpGRDB+OlRUaGhgYEBvLtWtMmIC6Oi1a\niELjP//k0iUGDOD6dVGKUcwnQ2am0LktXOrW5d497t8v5GYUAGZm/Pknd+9y5gzx8cyciUzG\noEHMmcPSpURFsX07ffvy9ddvR2PffENAADNmoKHBhQt07kyrVly4QNmyZGaiUKCpSZMmREWh\nr8+rVzRpUkhXWITp149duxg/nogITp1CJkNPj9272bcPBwcOHqR2bdq0wdiYxESaN6dBAw4c\nwNgYuRxra3R02LCBESMYOZKMDDZvpkEDwsKED2/XroV9edCrF7GxnDvHjRucO8e7y4wUAlWr\nVl35f/Vtc3PzrVu39u7dOzg4uHBbpeTjlMTIa/bsYd06njyhRg2+/ZaZM5HL6dmTtDRRdf+m\nK5SmZu56FjY2rFkjfk5N5fvvGT+ejAxKlGD2bHr1Ys8efH1xcKB0ae7cYcIE2rbNY5W14cNJ\nT2fUKJKTUSgYOpSpU/Py+AXP5s1cv86wYQQG8uSJ2KitzaRJ9OpFx460aoW2Ni9fAiL41tMj\nOJgvviA2lho1qF6d4GChM1eQ7NpFYCBJSdSsybffYmJS0A34tAkOJjn5PznW5yt2dhgacuqU\nsI355LG2Vgk/Ad27k5LCjz8yZAhaWvTty+zZb/9J8+asW8d33xEbC+DuTmAgCgUyGTo6+PgQ\nHk6XLmRno6eHQlFoqoRFmdq12bqV4cPFs6l1a379ldcCuq9eia6vbVumTaNePTQ1SUpizBha\ntGDTJuLjGToUPz9mzKBUKaysGDAASaJmTUJCcryhr3n2jAULOH0afX06diyIj7eODk5O+X6W\nPGTu3LmtW7dWU1Pr3bs34OjouGPHjk6dOmUobfUKlY9wMievmTMHLy8hWbJzJ46OPH2Knx/P\nnvHgAcnJlCnD0qVi54wMli+nQYN/OaaODosX8/w5Dx7w+DFDhwIsXYqODuXKYWWFjY3wZc9b\nZDLGjiUpiQcPePaM2bPzYIW3cDlxgn37WLiQxERxMxs1IjWVn36iTBmysvj1V4Dy5bGyEml5\nQJkymJjQpQuJiVy6VAh+U7Nm0bYtGhpUry7ckB4/Lug2fNosW0bHjhgbF3Iz1NSoW5cTJwq5\nGYXI4MFCyP35c5YsyX3xtEsX7t3j4UMGDeLAAeztMTHhyBG6d2fVKhISGDWKLl3IyCAjo1gt\nKHc8Pbl1i4QEnj8nOJg3bRHc3AgNFWa7FSpQuTJ79vDTT8LxErCwYNMmnj8nLo7794mIIDmZ\nJ0+4cCH3u/3sGc7OrFuHgwN6evToUay4ngt169aNiYnx9vZ+vaVWrVoRERGbN28uxFYp+dxn\n7BIT8fVl/XoxIpk6lfr1mTqVxYtVwrOrV+PpyeHDVK3K8eNkZrJ+/X86uLp6Ds+WEiU4epQz\nZ7h1i4oVRUJxfiCT5TjvR4q5OdWqsXYtNWuKLcqLOnyY8+eJiqJcOerVEwvNMhlr1uDtzcGD\nVK7M8eNIEps3F8482aNHTJjApk188QXAlCm4ujJtGgsWFEJjPkmiotizp6gkt7m6UjSWXwqT\n/9LhlCzJ0qWMGMGFC5QoQf36Yn32yRNiY3n0iKwsJIn09Pxv7kdLrlm8jRvTuzdubrRujbY2\nGhrUrcv06bi65sjD0dJS5VsrV6L+jgULkCQuXhRhuo8PTZvSr1/hZ+MVNQwNDd/aolAoOhaB\n2fvPfcYuLAy5nHbtxEsNDTp1IjQ0xz5Nm3L1Kp6eaGszbBhXrvAfTORyRyajbl26dcvHqO6T\nwdqay5dVUd2bODnRrZtwInpN8+ZcuUKbNigUjBhBZCQlSxZYY3Nw8SIaGqpqTU1NOnZ8+0NV\nzIfg54ebW1H5EtWvz8WLQjC2mH+lYkXhVKZQcOECQ4Ywbx6GhtSuzenTmJsLF5li3omffmL7\ndsqVw9qaLVs4cgQ3t/fPrg4NxdtbNfnauDGWlsXvy8fE5z5jZ2TEy5ekpmJgILYkJeWyvlO2\nLD/++C+HkiRCQ7l7l0qVPlS4pJi/8vQpZ86QkUGdOn8bsZUr9+9vUwHw+kOl1Fvhbz5Uxbwf\nN24QGMiOHYXdjv+jXMw6fbo4OQxAkjh3jpgYbG3/3XjU0JDkZHr2FGodmZk8e1b8TXlPWrem\ndeu8OZSRkZD0UpKVRUpK0X1fHj0iNBQ1NerUEWVzxXzuM3YODpQpw/Dhwhfo7FmWLcPL652P\nExdH/fq4ujJkCDVq4OVFamqeN/bzZccOKlSgfXt69MDWVojYFVmqV8fKSvWhOnOGX355nw9V\nMbkyfjz16tGqVWG34//o6lKrFseOFXY7igDKFNi6dRkyBCcnWrbk2bN/2t/Li8BADh8GePmS\nb79FTy+HEmcxhYKXF+vXc/AgwKtXjB6NQlFE1QTXrMHWlq5d6dQJW1s2bizsBhUNPvfATkuL\nTZs4cAAzM6ytqVuX9u0ZNOidj9OvH5JEbCzx8URFERUlvEqL+XDu3cPHhyFDSE7m6VOWLmX4\ncJUrZRFEW5tNm9i7V3yoXF358ksGDCjsZn0SHDzItm3MnVvY7chJo0YiOvnMGTiQ1FTu3CE+\nnps3uXePUaP+af+uXfnmG5o1o3RpTE35/Xc2buQvaUvFFDSdOzNkCC1aYGWFqSnr17Nhw9v2\nwUWBy5cZMICZM0lOJiUFX1969eL69cJuVhHgc1+KBZydiYriyBEePcLJCXv7d/vzx4+5fZs9\nezh4UGQQV67MxImMH8/ixe/ZpHv3mDqVs2cxMaFFC1q1olIlFIr3PNpHx9atLF9OXBx2dnTr\nxtWrmJqq1lh79mTDBnbseLcRZGoqN29i6kOJ5wAAIABJREFUYkLp0vnQ4r/g4iI+VImJODlh\nZ1cQJ/3kSU1lwAD696d27cJuSk6aNGHhQtLSPosvaXIy0dEiFAMiIpgxg8hILC05dIjgYPEV\ns7Vl8mQGDmToUEqU+Fsd6blzGTCAs2cxMKBRI1VKTDF/JSODGzfQ1+evDvUvXuDvz+7dZGfj\n7s733/9TeURWFtHRZGZia6sSzH8LPz/69SM0FAMDGjYsotF2SAi1ajF4MIBMxnffERjIrl1U\nqlTYLStsPvcZOyU6OrRuTY8e7xbVpaTw1VeYm+PiwqtXrFxJdrb4lbk5iYmql+/Egwc4OXH1\nKt7e3LzJ2LHUqIGFhUrL49Nm7lx8fKhSBWdn/vgDLy/GjCEujgsXVPuYmb2besj8+VhY4OiI\ntTXNmws9rfxGVxcPD7p3L47q8owxY3j5klmzCrsdf8HNDUni+PHCbkc+k5XFqFGYmlKzJubm\ndOvG8ePUrk1qKn37Uro0L19y4IBq/4MHSUzE0ZHSpWnRggcPcj9spUp064aXV3FU908onT+q\nVRNqAG/OS2Vm0qoVa9fi7c0XX/D77zRrlrvYKnD6NA4OVKxI1arY2BAS8rdnrFhRvC9FM6oD\nHj9+u0z4XR8NnyrFgd37M2QIFy5w/DjJyZQowaZNKnHOLVtwcnrPoqTZs7G15cABQkKwsSEg\nAJkMX19GjWLnzjxsflEkIwNfX1asoHdvAgPx9cXTEycnMjLw9OTpU4AnTzh4EGfn/3rMLVsY\nN46lS3nyhPBwMjL48sv3jLmLKUS2b2fZMtasKYqPGT096tXLxZDmE2PWLNatIyiI5GSOHePc\nObp0oWNHtm9n6FBWrMDMjHnzyMwE2LCBX3+lfHmSkrh0iefP6doVSSrsa/g4OXSI/v2ZMoXE\nROH9+sUXvNbBDQoiIoKTJxk3jrFjOXWK6Ojcs80SEmjbFldX7t4lPp5u3ejUiWvXCvJS8hIn\nJ06cUJmh37tHaOg7PBo+YYoDu/ckI4Pff2fJElxdMTBg+XLS05k1i+nT8fAgIOD904DCwvDw\n4Pp1zp1jwwa6d8fIiMqV6dWLgIA8vYaix5UrZGTg5SVsQCdMoFMnHj3C25u4OHr2ZMIEnJwo\nVYqvv/6vxwwIYMAAevTA2BgHBwIDOXmSGzfy8zKKyWtu3KBXLyZMKLp+U+7u7NlT2I3IZwIC\nmDwZDw8MDKhfn8WLefAgRyXm/Pm8fEmbNkyfzogRSBJr12JkRPXqBARw9CjR0YXX+o+ZwEA6\ndWLIEExMqFSJjRu5cUOlPxIWRu3aKq0AU1NcXbl4MZfj7NmDpibLl2Ntjbk5s2bh4EAR0NN9\nTzp1wt4eZ2d8fRk3jjp1qFcPT8/CblYRoDiwe0/i43n5Eltb8bJ9e+bM4cULQkIoWZKLF99f\nZMvCggcPuHsXXV1KluTZM1JSKFmSChW4ezevml9EUepnKi9feW8fPKBkSTZvxsqKa9c4eZLe\nvTly5B08du/ezeEnZm2Nltanfyc/JVJSaNdOBPpFlpYtuXz5UzaNlSTu3cvxVapYEUnKMd9j\nb49MhoEBISG8fMm4cSqTnvLlUVcv/t69J6/7QyUGBpiZqW6mhQUPH+bYX9lt5nqccuWQy1Vb\nPurHiro6e/cyZAihoZw/z3ffsXPnR+mNnucU34P3xNoaQ0NREK7k/n1q1+bECVat+qC0qk6d\nWLOG+/d58YK9e+nTBxsbatbkwAGqVfvwhhdpSpWifn0GDsTammPH2L+fOXPo3Jn4eB4/ZulS\nDhxgwoTcbYv+DqVJ9muOH+fly0//Tn4yZGXx1VdkZREYWKS7bCcnLC0/ZQsKmQx7+xxfpf37\n0dbml19EcuGdOwweTLNmbN7MiRO0aEFUlGrnw4fJyvo4zN2LIA4OHDqkSiC5coWHD6leXbz0\n8CAmhkmTSE8nI4OZM4mI4A2nqxzHuXyZR4/Ey9RUTp5UHedjRFub779n3z727mXUqI/eQjOv\nKK6KfU9kMiZPZtgwoqNxcODYMZYv588/8+DInToRFcWQIcjltGyJuTkjR/LVV5w4wfnzeXD8\nIs769Xz5JbNmIZPh7k6zZigUNGmCqyuNG7/PAceNw9mZLl3w9iY2Fn9/Bg0qNFOKYt6V777j\n1ClOny6KagtvIpPh6UlQEP37F3ZT8o0pU/D2JjUVNzfCw1mwgJkziYykUSO0tEhLw82NtWvF\nzr6+uLjQrRuenty5g78/w4eLQtpi3pWRI4U8ateuJCbi70+nTqoo2caG336jf39mzBAzpgEB\nVKmSy3E8PLCzo3Fjhg9HU5NffkFdnR49CvJSiikIigO792fYMIyNWbKEFSuoUoWQENzd8+bI\nEybQv///2LvvuCiONoDjv6ODKGADURSxoCjYEBEromKNHezGEjSxa4w9icauMfYu9th7w4oV\nbCBiAXvBAii913n/4F4RRAUEDsh+P/zBLbOzz94de3OzM8/g4cHJk1y4wLJl1K/PlSv/iVnc\n5cvj5oa3N3fucPgwt26xdCmdOzNtWjY7bGrU4MoVfv+d8eMpXZqJExk1KqeDluSOtWtZsYJT\npwrGIpVdutC5M2Fh+XF6R45o25bjx5k9m337KF+etWvp0weZjGnT8PHB0BBzc2QyeWELCy5f\n5o8/GDcOfX2mTWPECIVGX5ClXBWnTmXSJHR0GDKE335LU6BTJ+zsuH2b5GTq1v1irhNVVU6c\nYMYMFi4kMRFbW/76S5qMXAhJDbvsk8no3z+3vu7o69OuHe3a5Url+ZxMRq1a1KqVY89t3bo5\n05kqyUtnzzJyJKtX598JE+nY2aGtzcGD8gWyCqXWrWndOv3GChUyyKwGWFoW5nvTeaxaNfbv\n/1oBbe3UEY1foafHkiUsWZJTcf13ubq62traAkKIdevWHTlyRE1NrXv37n369FF0aNIYO4lE\nkv88eECPHowZw+DBig4l01RV6d6d7dsVHYdEIsl9bf8/IXzBggV//fVX/fr1zczMxo8fv3Ll\nSsUGhtRjJ5FI8puAADp0wNY2P+Yi/roBA2jcmBcvMDZWdCgSiSRPbNq06eTJk+bm5oCDg0PP\nnj2Hp6yGoThSj51EIslHoqP54QdKlcrv02Az1LAhZmasW6foOCQSSV6JiYkx/3+ehVq1ar1L\nl3tGEQrahVMikRReiYk4OvLhA0ePoqWl6GiyZcQI1q0jOlrRcUgkktwkhHj16lV4eHjDhg0v\nX76csvH8+fNlv7Quch6SGnYSiSRfEILBg7l+HRcXSpdWdDTZ1b8/KiqsWaPoOCQSSW7S1NQ0\nNjbW0dHZvXu3s7MzcPPmzY4dO06cOFHRoUlj7CQSST4gBD//zOHDnD9fMJKbfImmJr/9xrx5\nDB5caPOeSCSS0NDQ5OTksLCwkJAQVVVVoEKFCq6urlbZXnUq50g9dhKJRMGSkhgyhJ07cXGh\nbl1FR/PdfvmFokX5809FxyGRSHKTkpKSnp6eiYmJkZERULp06fzQqkPqsZNIJIoVGUnv3ri5\ncfYs9esrOpqcoKHBihV07EiXLjRtquhoJBLJFwghDh065OPjk267hobGmjVrsjFaztvb+8SJ\nE5MmTcqhALNJathJJBKF8fHBwYHYWK5exdRU0dHknLZtGTaMnj25do3y5RUdjUQiyYiGhkb1\n6tXr1KmTbruysrJWtmZvPX/+fN++fVLDTiKR/BclJrJiBVOnYmfHli3o6Sk6oJy2eDEPHtCq\nFWfOSG07iSQ/0tHRGThwYM+ePXOqwk6dOnXq1Cmnass2aYydRCLJU0Jw9Ch16jBjBv/8w+HD\nhbBVB6ipceQIZctibc3584qORiKR5ImuXbsqOgSpYSeRSPJKSAirV2NhQffuNGmCry9OTqnL\nxhc+2tq4uNCjB61a0a8fT54oOiCJRJLLTpw4oegQpFuxEokkNwmBjw/nznH8OK6uFC/Ojz8y\nfDjlyik6sjyhpsbSpTg48NtvmJrSpg19+tCuHbq6io5MIpF8h1mzZmW4PSkpKY8j+ZzUsMst\n+/ezejVv3mBmxtSphSGJQ55JTsbZmW3beP+eunX5/XeqVlV0TJJMS0zk6VPu3sXbm1u3uHGD\noCBMTLC3Z9w4WrRA5b931WnUiKtXuXqVDRsYPpzISCwtadQIKytq16ZSJZSVc+W4wcHMmoWr\nKyoqtGvHb79RpEiuHEiST1y7xty5PHqEkREjR9Kxo6IDKrwWLVpUu3Zt3c++oiUnJysknk/9\n9y6xeWLZMiZOZNgwunXjwgWsrblyhfyR4KYAmDSJdesYMQJDQ44epX59PD2pVEnRYUnSSkwk\nIIA3b3jzhleveP6cZ894/Jhnz4iPR0cHCwvq1qVvXxo3lmYPADRqRKNGrFuHmxvnz+PmxsaN\nhIairo6JCSYmVKhAmTKULUuJEpQoQfHi6OhQrFg2DxcTQ9OmKCkxaBDx8axezeXLnD1b8Fbg\nlWTShQu0akWvXowezb17dO/OmjUMHKjosAqpJUuWHD9+fO/evem2a2hoKCSeT0kNu5yXlMSU\nKaxcyaBBAD//zMCBTJ3KmTOKjqwgeP+ev//m5Elatwb4+Wfs7Zk9G2dnRUcmodWECaZAUBAf\nPvD+vXyrjg7ly2NsTKVK2NtTtSqmplJL7otUVWnWjGbN5A9fvsTXV94g9vPjzh3evSMwkMjI\nT3eyhiFZPdDWrYSG8uCBvGnYpw+mppw8Sfv2OXAWknxoyhSGDWP5cvnDqlWZOFFq2OWWH3/8\n0dPT8+bNm/XzX/pNqWGX8548ISqKdu1St3TowLBhiguoQPH2RlUVOzv5Q5mMdu3YulWhMUnk\n4sqVizUzo3hx9PUpXRp9fcqVk+7ufZcKFahQAXv79Nvj4ggJISyMyEjc3HxGjXLJas1eXjRp\nktrhV7YstWvj5SU17AqtO3eYPj31Ybt2jB7N69f/lfGseW/ZsmWfb4yNjc37SNKRGnY5T18f\nmYzXrzEwkG/x88PQUKExFRwGBsTF8f59mmevTBmFxiSRuzR69Etr6zJAUlLS9u3bmzYdkO26\nfvzxRzs7u379+u3Zs6ddu3ba2trAhw8f3NzcfvjhB+Dq1atqamp+fn7//PPP4MGDk5KSbG1t\nHR0dK1eu3K5du6pVq2poaNSqVQuwsLDo2LGjt7c3YGRkFBYW5uHhERgY2LZt29evX7979y4w\nMBCIjo5WVVWNiooyNDRUUVEJDQ3V0tLS09Nr3rz5tWvXNDQ0nj9/bmFhER4ebmtre+/ePSUl\npSZNmnh5eV24cEFVVbVcuXKmpqahoaFCiDNnznTv3j0xMbFkyZKLFy92dnZWVlbu27dvTEzM\niRMnXr58WbduXTs7u9jY2AMHDvTu3TurT87Tp083bNgwa9YsAwNlICEhDF5ntZIyZbh3L/Vh\ncjJv3kgXosLMwIDXn7xNXr9GVZWSJRUXkERBpIZdztPVpU0bhg9n2zYqV+bSJebMYexYRYdV\nQFSrRu3aDBzI+vUYGHD8OGvWsGaNosOSpJWUlOTm5jZgQPYbdpcvXy5atGi/fv08PT2bNWuW\n0rALDQ319vZOadg9efJEQ0PjwYMH3t7ebm5ucXFxFStWfPToUWBgYIkSJZSVlYsWLZrSsHv6\n9Om1a9cePnwIvHz5Mj4+/sWLFwkJCTdv3nz//n1sbGxcXJxMJktOTk5ISAACAwOVlJQSExOj\no6PDw8OvXr367NkzmUwWHR199+7dqKgoDQ2NlNo0NDQ8PDwCAgKA4ODgDx8+REREqKmpRUZG\nXr58WUlJSUdHJ+Vc1NTUevXqFRUV5enpef/+fS0tLTs7u5iYmFu3bmWjYefv7+/m5paUlKT8\nHbMqunRhzhzmz2f0aBIS+P13wsIy6BqUFBo9ezJjBjVq0LAhDx4wZgxdupAPRnxJ8po0jDZX\nbNqEtjampqirY2dH9+789puiYyoglJXZu5cPHzAyQlMTBwd+/ZV+/RQdlkRS0Jibs2ULCxei\nrY2ODvv2sWeP1GNXmP35J61a0bgxamrUrImhIatXKzomiSIUzh67hIQEDw8PxcawYAEvX2oE\nBqoaG8eWKpXg5aXYcPLIu3fvsrpLQEDA5y/WqlU8f64ZGqpSqVKMrm6iol/MwikuLi6ru/j6\n+qqqqgIJCQkfPnz4nv+y+Pj4wMBADw8Pf39/b2/v4sWLA35+fm/fvk2p9sWLF2pqau/evUtK\nSvrw4UN8fPzjx4+TkpLi4+Pfv3///PlzLS2tlJLJycnh4eEJCQlCiJiYmJRfhBBxcXFJSUkp\n2QeEECnH9fDwSPlrysakpKSYmJiPGQoSEhKSk5MjIyMTEhJkMllwcHB8fHxK4aSkpNjY2JTK\nU549mUymqqrq4eERFBSkqqrq6ekZERHh7+8fFhb26tUrDw+P8PDwDN/e3/Tw4cOIiAhPT8+U\nZ9vX1zerNcTFxXl4eFSuzMGDSk+eaKmoiMqVY9TUkqV/pdyW0r+bJe/evcupD6wRI3BwUHv5\nUt3AIN7IKO75c54/z5GKC6eULvxCSBQ6Z86ckRXiZPb5Xp8+fTL/Yg0ZkuW5fpIcdOTIkUy+\nUgkJCZ9nbJLkGV1d3Y8N1m86cuSIouP9TxsyZEjmr4F9+vRRdLz/XTKZ7MyZM5l/sQoKmfj/\nF1mJRCKRSCQSSYEmjbGTSCQSiUQiKSSkhp1EIpFIJBJJISE17CQSiUQikUgKCalhJ5FIJBKJ\nRFJISA07iUQikUgkkkJCathJJBKJRCKRFBJSw04ikUgkEomkkJAadhKJRCKRSCSFhNSwk0gk\nEolEIikkpIadRCKRSCQSSSEhNewkEolEIpFICgmpYSeRSCQSiURSSKgoOgCAS5cubd++/f79\n+1FRUdra2hYWFoMGDbK0tFR0XBKJRCKRSCQFiUwIodgIVq5c+ccffzg6OlpYWGhqakZGRt67\nd2/nzp3Lli3r169fNiqMiIjYu3dvYmJijocqyQwrK6vatWtnsvC9e/fc3NxyNR7JlygrK3fr\n1k1XVzeT5Q8fPhwQEJCrIUm+RF9fv1OnTpksHBoaun///qSkpFwNSfIlNjY2NWvWzGRhLy+v\nGzdu5Go8ki9RUVHp0aNH0aJFFR1IDlN8w65y5cqHDh1K92/g7u4+ePDgBw8eZKPCPXv29O7d\nu0KFCjkUoCQLgoODGzZseOLEiUyW79at27lz50qUKJGrUUky9OrVqw0bNgwYMCAzhePi4jQ1\nNQ0NDdXV1XM7MEk6cXFxb9++jYmJyeSTv2XLliFDhpQvXz63A5N8LigoyM7Obv/+/Zks365d\nO3d39+LFi+dqVJIMvXz58t9//3VwcFB0IDlM8bdiQ0NDzczM0m2sX7++v79/9ipMTk4uVarU\n06dPvzs0xQgOZv16Hj+mQgWGDKFMGUUHlBVTpkzx9PTMfPnk5OSBAwf+888/339oDw/27iU0\nFEtLBgxAVfX7qyzkTExMkpOTM1lYCCGE2Ldvn7W1da5Gla+4uXHoEBERNGxInz4oKysmjGvX\nrjVs2DDzX8KTk5ONjIwK7jWwQBs7duyLFy8yXz45Ofnnn3+eM2dOrkVUwERHs2ED9+5RujT9\n+1O1ai4eq0yZMpm/BhYgip88UaVKlRUrVny6RQjx999/W1hYKCokBXr0iKpV2byZuDj27sXU\nFA8PRcdUEKxZQ4MG3LxJRARTptCoEbGxio5JUsAtWEDTpty9S0gIo0fTqhXS+A6JJFd9+IC5\nOYsWERPDuXOYm3P0qKJjKoAU32O3YsWKzp07L1iwoHr16pqamtHR0T4+PpqamocPH1Z0aAow\nfDhNm7JnDyoqJCczZAg//URWusD+iwIDGTuWjRtJuakYFISlJX//zdSpio5MUmA9e8bUqezf\nT8rAtrdvqVuXtWsZPlzRkRUuR47g4oKuLk5OGBsrOhqJok2ZQokSXLyIpibAjBkMGoS/v8I6\nywsoxTfs6tWr9+zZM1dXV19f35RZsVOmTGnWrJnyt17Jt2/fjh8//vMBwo8ePQoODs61eHNR\nUhLu7uzfj4oKgJISw4djZUV4OMWKKTq4fOzWLVRV6d9f/rBECXr25PJlhcYkKeCuXcPAgI/T\nFQwN6dKFy5elhl2OEQInJ7Zt44cfuH6d5cvZs4e2bRUdlkShrlxh1Ch5qw4YPpw//8THh0zP\nRZFAfmjYASdPnnzw4EG7du0sLCxWrly5cOHC8+fPT5s2TUND4yt7qaurlypVKi4uLt32pKSk\nhISE3Iw3tygpoaJCfHzqloQE+UbJV6iqkphIcnLqt7qEBNTUFBqTpIBTVU3zn4j0pspps2ez\nZw+XLmFlhRBMm0a3bly+TL16io5Mojjp/u9SPsml/7usUnyTYdasWYsXL65Tp87ixYvnz5+/\ncuVKR0fHI0eOhIWFLV++/Cs7lihRYtmyZZ9vHzVqVPam0yqcTEaLFixaRPPmFC1KTAxz5mBj\ng5aWoiPL3ywtUVNj3jymTEEm48kTtm5l+nRFhyUpyGxsiIhg5Up5F93du+zdS9rBwJLs8/Zm\n5kx27MDKCkAmY/ZsXr6kTx+8vPjqN3pJYdayJatW4eiIvj6JicycibExlSsrOqyCRvGTJzZs\n2ODu7n7u3LmtW7eOGTNm165df/zxx7Fjxw4dOqTo0BRg5UrevaNiRZo1w9gYLy82blR0TPme\nnh6bNjFnDqamNG5MzZo0bMgvvyg6LElBVrYsa9YwfjxmZtjYUK8eP/xA3765ftynT/8Tzcfx\n42nThh490mxcuZLwcBYsUFBMknxg5kz09KhcmWbNMDFhzx527EBJ8e2UAkbxPXZhYWGmpqZA\ny5Yto6KiUhLalSlTJjQ0VNGhKUCZMty9y4EDPHrE4MF060aRIoqOqSDo0gVfX44eJTiYP/6g\nVStFByQp+Pr3p1EjTpwgIoK5c2nWLNePeOsW7dsTF8eIEbl+LAW6dInz5/H2Tr9dR4c5cxgx\ngqFD0ddXRGQSRStShKtXOXqUO3coU4auXZGSnGaD4ht2JiYmR48e7dixo4qKysGDB5WUlICz\nZ8+WK1dO0aEphro6vXopOogCyMhI6qWT5LBKlRg5Mo+OdeoU3btjYMD793l0REWZP59u3ahR\nI4M/9evHwoUsXMiiRXkeliR/UFKiUycyvcyKJAOKb9jNnTu3S5cumzdv7tGjR8eOHYH9+/f3\n69dv06ZNeR9MQAC7dhEQgJkZjo7yJLdhYezcyYsXVK5M797SiLeC58YNzpzh4UOUlKhWjXbt\n+E8mSZTkvI8XhypV6NUr+xeHXbsYMICBA6lVi8mT2bOHO3coUYIePTAyytGIFc3Xl5MncXfP\n+K/KykyZwrBhTJ2Knl7eRlYYJSaydy9371K6NA4OGBrm1oHu3+fYMaKisLGhTZvcOookkxR/\n77p169bPnj1r3Ljxxy3Vq1e/ePGio6NjHkdy6RKmpqxahacnY8ZQrx6hoTx4gKkpc+fi7c30\n6ZiZ4eeXx3FJvssff2Bjw9Kl7NjB1q0sXUrdumQ060YiyZr79zE1Zd48vL2ZNo0aNXj9Ojv1\nrFpF376MH8+4ccTHExnJ0KHcusWGDVSvzsmTOR23Qq1ejZUVDRp8sYCjI8WLs25dHsZUSEVE\n0KABI0bg6cmaNVSrxrlzuXKg1aupXZt9+3B3p0sXHBxQ9Eql/3WKb9gB+vr6ZT5ZOcvMzKx+\n/fp5HIMQ9OtH//74+ODiwpMnyGRMmcKQITRpwpMnnDjB06dUrlzIh78UMjduMGcOEyYQF4e3\nN1euEBLCxIn8+itPnig6OEkBN3gwzZrx+LH84mBikp2Lw+zZjBrFnDn8+CPAyZMIwaNHnDrF\n/fuMHs2AASQk5IsL9feLiWHbNoYN+1oZFRV++YXVq/ksRakka/74g9hYHj/GxQUfH5yc6Ncv\n55/Vly8ZM4aNG7l5kzNnuH2bM2fYujWHjyLJkkJyvfh+T5/y6hUTJ8on4Ojq8ssvnD3LjRv8\n+qv8nqyWFmPGcOEChXFxucLp4kXq1SMoiB9+oEYNbGxo1gwhMDKSMhhLvktkJDdvZnBxyHxf\nhRD89hszZ7JkCZ07yzc+eoSaGqVKAchkTJxIUBBPn2p+pZ4CZN8+kpP55pLrgwfj74+LS57E\nVHi5uuLkRPHiADIZkybx7h2+vjl8FDc3SpVKzQ9frRpdunD+fA4fRZIlUsNOLqWt9um0amVl\nkpMRIv1GqZO5AElKQkmJpCRkMvmWlJdVSUlqnUu+y1euGJncfdgwVq5k/Xpat05f7Ucp9Qsh\nI0cNHz5cJpNN/yzZo4GBwaRJk7Jd7ae7GxgYTJs2LV2BTZvSD1POsFjJknTtyoYN2Q4E4PXr\n19bW1hoaGp+fZvYYGxuPGTMmR6rKGylXv4+UlJDJcv66l+4o/P8fQaJAUsNOrnJlDA355x/5\ndTkykjVraNFCPh4rpfs6Pp7ly2nSREqrU2A0bcqtWxgYcOwYjx/j4YGrK+rqvHjBJ6M6JZIs\nK1aM2rVZulR+cYiLY/lymjbN1MUhMZF+/di1i02bsLZO86cqVYiPJyRE/vCff9DVpXLl6ByM\nPDY2dufOnRYWFlu3bhW59j11wYIFndLObHz2jAsXGDjwi8U2bNjQvXv3lN8HDuT4cQIDsx+A\ns7Ozl5fXqVOnRvxXR880a8bGjYSHyx8uXkypUlSvnsNHsbHB35+9e+UPnz3j4EGaN8/ho0iy\nRPGzYvMJJSW2bqVTJ1xcqFwZd3eKFWPuXF6/pkULqlenZk08PYmP5+pVRccqyTQbG0aPZsEC\nihenenWEQF+f2bPl2Ywlku+xcWOai0NCQqYuDnFx8rWMt22jWrX0f23fHldXqlalcWNeveLB\nA3btQlU1JztADh48GB0dvXHjxvr1658/f97Ozi4HK/+o/8ebc/+3ZQtmZqQbPv1psZs3b378\n3c4OAwN27mT06GwGEBISYmBg0CwP0g/mV3/9RaNGVKmCjQ3Pn/PoUepC5DnIxIT58+nVixUr\nKFYMV1dsbdM33yV5TOp6SmVnh49V137pAAAgAElEQVQPvXtTrhx//SVPN1CrFg8fMmwY5cox\nfjy+vlSsqOhAJVmxcCHnzjFoED/8QNeuDB6Muzu//abosCQFX+3aPHrE0KGUK8evv+Lri7Hx\nN3aJjuaHH7h2jR07MmjVAerqaGuzaBFGRnTrxr17OZ/Qa9OmTR07drS0tGzYsOHmzZu/VCwh\nIeH3338vX758kSJFLC0tT5w4kbI9KCho0KBB5cqV09DQMDExmTFjRnJGN94+vcdatmzZefPm\nL1s2/c2bCkWLFm3evPnjx4/TFWvevPm6dev2798vk8nOnj3bvHlTmcz2339TKxw5cqShoWFS\n2sH/CQkJU6dONTY2VlNTMzQ0HD58eGRkJNC4ceMlS5a8fPlSJpP9+uuvn4f3lbNwc3Nr3ry5\nnp6etrZ2/fr1jx49+nEvFRWVRYsWlS1bVl1dvXHjxk+fPv3Wk61Iurrcvs3cuRgZ4ejIgwe0\nbZsrBxo7lhs3aNYMU1O2bePIEemmloJJPXZpGBnx+QiTkiUZN04R0UhySNOmNG2q6CAkhVHJ\nkowfn9nC4eF06MCLF+zYwdfzrw8YwIAB3x9dBl69enXu3LkjR44AAwcOHDNmzKpVq4oWLfp5\nyUmTJjk7Oy9fvrx69erbt2/v3Lmzm5ubpaXl4MGDPT09t23bVrZs2Tt37vz44496enqjRo36\nykFVVVUXLlwaHj7lxYsnWlrhzZs3HzlypEvayRHHjh1r1apV6dKlt2zZoq2t7e/v379//1ev\nXj55UqFyZZKTk/fv39+/f39lZeVP9xo9evSOHTtWrFhhY2Nz7969oUOHvnv37sCBAy4uLhMm\nTDh+/Li3t7dGRkvPfuksYmNj27dv36VLl5UrV6qoqGzbtq1Lly4PHjyoWrUqcPz48UaNGh06\ndCgoKGjIkCEjRow4mb+z0aipMWhQXhyobl3q1s2LA0kyQ2rY5bpbt3B3R0sLe/tvXM0l+dnV\nq9y8iZ4ebdtSurSio5HkISE4e5a7dzEwoEMHihXLTiXBwbRpQ1AQO3Yocr2szZs36+vrt2nT\nBnB0dBwzZsyePXsGDx6crlhkZOTq1at///33vn37AvXq1fvw4cPjx48tLS3//vtvoFKlSkDV\nqlW3bdvm4uLy9YYdoKRUoUOHEUZGQImuXbsuX748XQFtbW1lZWVVVVVdXV2ge/fuo0ePVlbe\numfP9ClTuHLlyrt37walbaSEhoZu2LDhzz//7NevX0pIAQEBw4YN8/PzMzIyUldXV1JSSqnt\nc186Cz8/v9DQ0F69etWoUQOYNWtWSnPz/2ehtH79eplMBvTt23fNmjXfer7zo6dPOXeOxESa\nNMHcXNHRSHKB1GGau4YNw9qaDRuYNQtTU3bvVnRAkqxLSdDQogVbtjBtGqamUiKG/5CYGFq0\noFMntm9n7FhMTbl1K8uVBAZia0tICFu3KrJVJ4TYsmVL7969hRCJiYlaWlqdO3fO8G7s3bt3\nY2JiGnySR3jbtm29evUC1NXVly1bZm5urq+vX7JkyVOnTgUHB3/9uMnJhIRYfGw96unphXyc\nHvIFGhoa/fr1S0zcsm8fwJ49exo3bpzSbfaRt7d3QkKCjY3Nxy1WVlZCiNu3b3+98q+cRaVK\nlapXrz5gwIBZs2Zdv349OTm5WbNmH1uHNjY2sv9PsNfX1w8LC/vmgfKbVaswM2PRIlatok4d\ncmjGsCR/kRp2uWjXLrZvx92dO3d49ow//2TIEN69U3RYkixauRJXV7y8uH2bly8ZOpR+/YiI\nUHRYkjzx55/4+fHoEZ6e+Plhb0/v3lnLefT6tXwkwL//yhPUKcqFCxeePXv2999/q/7fv//+\ne+XKlSefZesODQ0FPr9Fm5CQYGtre+LEiXnz5rm7u3t5ebXJxAJSUVFoaGi2a5e1aJ2cnEJC\nnt6+ffXx4+QDBw583q0YHh4OFPukBzUl4PCPE0G/4CtnoaSkdOnSpf79+2/evNna2rpcuXIr\nVqz4uGORIkU+/v6xhVeA+PgwZgzr1/PoEffuceIE8+dLOecKIalhl4tOn6Z1a27fZvNmXr9m\nwgQ0NaVJtQXP6dMMHChPE6CkxMyZRETg4ZHDRwkNZfduVq784jKaEoU4fZpRo+SDKNTUmDWL\nx4+5e5e9e1mx4ttprl+8oHlzihVj0yZ0dPIg3q9xdnauU6fOzbTKli27ZcuWdCVTbj5+3q92\n/fr1J0+erFq1qn379iYmJuXKlUtpAn5dRAQ1amR5PqaZmVmjRo10dHYsXnwxMjKyR48e6Qqk\nNOk+bcal/K7zrSf662dRsmTJefPmPXny5NGjRw4ODiNHjjx06FDWQs+vzp/H1DQ1mXDr1rRs\nyalTxMdz+DDLl3PqlJSCrjCQGna56MYNDh9m0SL++ANTUzZvRkuL6JzMSCXJC9HRaH6S+V9F\nBVXVHH4dU9YpHjmSNWto2hQHB2k9pfwi3aufkly3ZUuGDWPdOuzs6NCB+PiM971/n0aNKFuW\nDRvQ1s6LaL8iPDz8wIEDffr0sUyre/funye0q1q1qra29oULFz5u6dKly9y5c+Pi4oASJUqk\nbHzy5Imbm9vXk+G5upKQQM2amQoyXVVOTk4JCfsPHtzm6Oj4aW9Zilq1aqmpqV395Lvy1atX\nlZSU6tWr9/WjfOUsnj9/fuDAgZTtVapUWbJkSenSpe/cuZOp6PO9mJg0b2ZAS4uAAMzN6d+f\n9evp0gVra751n1yS30kNu9xy7hy+vujpceUKL1+yaBFOTrx+TcOGio5MkkU2NuzenXrvdedO\nEhLSp+P6HrGx9OpFjx68ecPdu9y5w4ULLF2aY/VLvoeNDVu2pDbd1q5FSYm2bXn7Fm9vfHy4\nc4e5czPY8eZNmjXD3JxVq8hoXmZe27VrV0xMzOf9Xg4ODq9evTqf9oZc0aJFhw4dunjx4g0b\nNnh6ek6YMOHIkSO2trYWFhaamprLly9/+/btpUuXHBwcOnfu/PLlSz8/vy8175YvR0uLjObd\npqenp3f37t3bt28HBASkbOnRo4eSUnxAwLauXeXTJtauXWttbR0fHw/o6Og4OTktWrRo7969\nr169Onjw4IwZM/r27WtoaPh55Z/u+JWzePnyZY8ePebNm+fj4/PkyZPly5e/f/++SZMm346+\nIGjYEC+v1EGiz55x9iw3b2JsjJ8f3t48f05CAmPHKjRKyXeTZsVm7P17XF2JjsbaOuN0U9/k\n4oK9PaGh1KhBx46EhpKYSPv2VKmS07FKAAgN5dw5wsKwtMTCIidrnjiRgwepXp02bQgI4ORJ\nlizJycFSd+/i78+8efJVR83McHLi5EkpyU6+MHMmdetiZISVFdHRXLxIcjILF6KuDlCpEiNG\ncPAgf/yRZq8LF+jUiZYtmTWLtAk6FGbTpk0NGjQoX758uu0NGzY0MjLavHlzukzFc+bMkclk\nv//+e2hoaLVq1Q4cOGBtbQ1s3bp10qRJlStXNjc3X7Vqlbq6etu2bWvVqvXq1avPD/r0KUeO\nULJkpiIcNWpU3759GzVq5Ozs3LNnT0BTU7NDh7Z7994JCpJ/Ifbz80uZ05DycPHixUWLFh03\nbpy/v7+BgcGgQYNmzpyZYeWf7liqVKmvnMX27dsXLlz4119/KSsrm5qabt++vUWLFpk6gXxA\nCC5d4uFDjIxo2VJ+SfmoUSMGD6ZJEzp1Ql2dQ4do3JhTp3B2ls/11tdn8mRGjlRI7JKcIwq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IoaG8qhR792JpCbB1K6tW4eIi36VBA+bP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\n6aYlErp4kR4/pl27yNqav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alternative\n","scatterplotMatrix(wineb[2:6], groups=wine[,1])"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":437},"id":"5dCiTeYmF8mO","executionInfo":{"status":"ok","timestamp":1717434993726,"user_tz":-120,"elapsed":2890,"user":{"displayName":"toni Monleon","userId":"05269440626861479105"}},"outputId":"e4a37035-0990-454d-d90a-90bd2ca848d6"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["plot without 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d2fbt2bED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\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4KDKRiJ36t83YeS1TgaR1QUidaABgMZ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wNJzF+m7RValBN/jkQ2pEsb5eTLvUEduvEJ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\n/3g4T0Z/Wc3qmLeVlRY9a9Qo++8vBN1f9iRpaXbePJWX7fi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E5BAuDqF6CCkhLI1u+UoIKSHUDKFeCCkhXPUHD7hDCPuFcGkZv44eHfbd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\nnBZ++sGZPST09P6Nnipg5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1+W3laF9M+k60i6eTngcoCVsBeJz+6teMPi/Xp79k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\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PQVumPHjrTZIiMjly9fPmrUqO7du3/4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UkvoSn5uEaOa8So5YmMbFFwk4xrSlBDXm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\nu6Gnhg5jxlC5Mk+fksJo9bOwcuXK1q1bz58/38HBwdjYODI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DvyAxpMEe9KspPYheYXXRqkpquSkzj4MHmTiRdeuIicHHh0KF+PtvJJKk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\nM2QOkPapzMG8tNYSN4knk97AjRvbYgtXXGHkSJmZFi1y4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mz4cDfe+OO69DvWHT93stqNj1GjRjVp0iQiYk5EXsTtEUMj6lZINH5RRE7E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\ng9Tq7kFqFxt8W909SBVIqUX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2MPoeIL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\nERERk/h/aanbmqiPJqUAAAAASUVORK5CYII="},"metadata":{"image/png":{"width":420,"height":420}}}]},{"cell_type":"markdown","source":["In this matrix scatterplot, the diagonal cells show histograms of each of the variables, in this case the concentrations of the first five chemicals (variables V2, V3, V4, V5, V6).\n","\n","Each of the off-diagonal cells is a scatterplot of two of the five chemicals, for example, the second cell in the first row is a scatterplot of V2 (y-axis) against V3 (x-axis)."],"metadata":{"id":"pIkrZnn5rSAc"}},{"cell_type":"markdown","source":["Other possibility is present the correlation and the groups using pairs"],"metadata":{"id":"jmaTw-JewiDy"}},{"cell_type":"code","source":["#correlations on the pairs\n","# Customize upper panel\n","upper.panel<-function(x, y){\n"," points(x,y, pch=19, col=c(\"red\", \"green3\", \"blue\")[wine[,1]])\n"," r <- round(cor(x, y), digits=2)\n"," txt <- paste0(\"R = \", r)\n"," usr <- par(\"usr\"); on.exit(par(usr))\n"," par(usr = c(0, 1, 0, 1))\n"," text(0.5, 0.9, txt)\n","}\n","pairs(wineb[2:8], lower.panel = NULL,\n"," upper.panel = upper.panel)"],"metadata":{"id":"_TuqmmnXwhEf"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["**Correlation between variables**\n","\n","\n","Correlations are useful because they can indicate a predictive relationship that can be exploited in practice. However, in general, the presence of a correlation is not sufficient to infer the presence of a causal relationship (i.e., correlation does not imply causation).\n","\n","![](https://images.prismic.io/sketchplanations/f1f6cafb-6444-453f-a5c5-8f603ab41a23_SP+562+-+Correlation+is+not+causation.png?auto=compress%2Cformat&fit=max&w=3840&q=50)\n","\n","\n","\n","\n","Formally, random variables are dependent if they do not satisfy a mathematical property of probabilistic independence. In informal parlance, correlation is synonymous with dependence. However, when used in a technical sense, correlation refers to any of several specific types of relationship between mean values. There are several correlation coefficients, often denoted ρ\n","or r, measuring the degree of correlation. The most common of these is the Pearson correlation coefficient, which is sensitive only to a linear relationship between two variables (which may be present even when one variable is a nonlinear function of the other). Other correlation coefficients have been developed to be more robust than the Pearson correlation ρ that is, more sensitive to nonlinear relationships. Mutual information can also be applied to measure dependence between two variables.\n","\n","How to measure the linear correlation:\n","\n","![](https://wikimedia.org/api/rest_v1/media/math/render/svg/b551ad29592ae746bf05fe397fbdc56201f483a5)\n","\n","where E is the expected value operator, cov means covariance, and corr is a widely used alternative notation for the correlation coefficient.\n","\n","and the interpretation:\n","\n","![](https://upload.wikimedia.org/wikipedia/commons/3/34/Correlation_coefficient.png)\n","\n","\n","Take into account that is the measure of a \"LINEAR\" CORRELATION:\n","\n","![](https://upload.wikimedia.org/wikipedia/commons/thumb/d/d4/Correlation_examples2.svg/2560px-Correlation_examples2.svg.png)\n","\n","\n","\n","\n","\n","\n","\n"],"metadata":{"id":"BGtD2qK_w-ye"}},{"cell_type":"markdown","source":["The usual is measure the correlation between numeric variables using cor() = Pearson correlation, see in https://en.wikipedia.org/wiki/Pearson_correlation_coefficient"],"metadata":{"id":"Np-064wXy7ZI"}},{"cell_type":"code","source":["#Antoher possibility is present the correlation matrix\n","cor(wineb[2:14], method=\"pearson\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":526},"id":"V3T-HzIKrR2b","executionInfo":{"status":"ok","timestamp":1717435208424,"user_tz":-120,"elapsed":542,"user":{"displayName":"toni Monleon","userId":"05269440626861479105"}},"outputId":"bdd081b5-776e-45f4-9686-7cc13cf5b9aa"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/html":["\n","\n","\n","\t\n","\n","\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\n","
A matrix: 13 × 13 of type dbl
AlcoholMalic acidAshAlcalinity of ashMagnesiumTotal phenolsFlavanoidsNonflavanoid phenolsProanthocyaninsColor intensityHueOD280/OD315 of diluted winesProline
Alcohol 1.00000000 0.09439694 0.211544596-0.31023514 0.27079823 0.28910112 0.2368149-0.1559295 0.136697912 0.54636420-0.07174720 0.072343187 0.6437200
Malic acid 0.09439694 1.00000000 0.164045470 0.28850040-0.05457510-0.33516700-0.4110066 0.2929771-0.220746187 0.24898534-0.56129569-0.368710428-0.1920106
Ash 0.21154460 0.16404547 1.000000000 0.44336719 0.28658669 0.12897954 0.1150773 0.1862304 0.009651935 0.25888726-0.07466689 0.003911231 0.2236263
Alcalinity of ash-0.31023514 0.28850040 0.443367187 1.00000000-0.08333309-0.32111332-0.3513699 0.3619217-0.197326836 0.01873198-0.27395522-0.276768549-0.4405969
Magnesium 0.27079823-0.05457510 0.286586691-0.08333309 1.00000000 0.21440123 0.1957838-0.2562940 0.236440610 0.19995001 0.05539820 0.066003936 0.3933508
Total phenols 0.28910112-0.33516700 0.128979538-0.32111332 0.21440123 1.00000000 0.8645635-0.4499353 0.612413084-0.05513642 0.43368134 0.699949365 0.4981149
Flavanoids 0.23681493-0.41100659 0.115077279-0.35136986 0.19578377 0.86456350 1.0000000-0.5378996 0.652691769-0.17237940 0.54347857 0.787193902 0.4941931
Nonflavanoid phenols-0.15592947 0.29297713 0.186230446 0.36192172-0.25629405-0.44993530-0.5378996 1.0000000-0.365845099 0.13905701-0.26263963-0.503269596-0.3113852
Proanthocyanins 0.13669791-0.22074619 0.009651935-0.19732684 0.23644061 0.61241308 0.6526918-0.3658451 1.000000000-0.02524993 0.29554425 0.519067096 0.3304167
Color intensity 0.54636420 0.24898534 0.258887259 0.01873198 0.19995001-0.05513642-0.1723794 0.1390570-0.025249931 1.00000000-0.52181319-0.428814942 0.3161001
Hue-0.07174720-0.56129569-0.074666889-0.27395522 0.05539820 0.43368134 0.5434786-0.2626396 0.295544253-0.52181319 1.00000000 0.565468293 0.2361834
OD280/OD315 of diluted wines 0.07234319-0.36871043 0.003911231-0.27676855 0.06600394 0.69994936 0.7871939-0.5032696 0.519067096-0.42881494 0.56546829 1.000000000 0.3127611
Proline 0.64372004-0.19201056 0.223626264-0.44059693 0.39335085 0.49811488 0.4941931-0.3113852 0.330416700 0.31610011 0.23618345 0.312761075 1.0000000
\n"],"text/markdown":"\nA matrix: 13 × 13 of type dbl\n\n| | Alcohol | Malic acid | Ash | Alcalinity of ash | Magnesium | Total phenols | Flavanoids | Nonflavanoid phenols | Proanthocyanins | Color intensity | Hue | OD280/OD315 of diluted wines | Proline |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| Alcohol | 1.00000000 | 0.09439694 | 0.211544596 | -0.31023514 | 0.27079823 | 0.28910112 | 0.2368149 | -0.1559295 | 0.136697912 | 0.54636420 | -0.07174720 | 0.072343187 | 0.6437200 |\n| Malic acid | 0.09439694 | 1.00000000 | 0.164045470 | 0.28850040 | -0.05457510 | -0.33516700 | -0.4110066 | 0.2929771 | -0.220746187 | 0.24898534 | -0.56129569 | -0.368710428 | -0.1920106 |\n| Ash | 0.21154460 | 0.16404547 | 1.000000000 | 0.44336719 | 0.28658669 | 0.12897954 | 0.1150773 | 0.1862304 | 0.009651935 | 0.25888726 | -0.07466689 | 0.003911231 | 0.2236263 |\n| Alcalinity of ash | -0.31023514 | 0.28850040 | 0.443367187 | 1.00000000 | -0.08333309 | -0.32111332 | -0.3513699 | 0.3619217 | -0.197326836 | 0.01873198 | -0.27395522 | -0.276768549 | -0.4405969 |\n| Magnesium | 0.27079823 | -0.05457510 | 0.286586691 | -0.08333309 | 1.00000000 | 0.21440123 | 0.1957838 | -0.2562940 | 0.236440610 | 0.19995001 | 0.05539820 | 0.066003936 | 0.3933508 |\n| Total phenols | 0.28910112 | -0.33516700 | 0.128979538 | -0.32111332 | 0.21440123 | 1.00000000 | 0.8645635 | -0.4499353 | 0.612413084 | -0.05513642 | 0.43368134 | 0.699949365 | 0.4981149 |\n| Flavanoids | 0.23681493 | -0.41100659 | 0.115077279 | -0.35136986 | 0.19578377 | 0.86456350 | 1.0000000 | -0.5378996 | 0.652691769 | -0.17237940 | 0.54347857 | 0.787193902 | 0.4941931 |\n| Nonflavanoid phenols | -0.15592947 | 0.29297713 | 0.186230446 | 0.36192172 | -0.25629405 | -0.44993530 | -0.5378996 | 1.0000000 | -0.365845099 | 0.13905701 | -0.26263963 | -0.503269596 | -0.3113852 |\n| Proanthocyanins | 0.13669791 | -0.22074619 | 0.009651935 | -0.19732684 | 0.23644061 | 0.61241308 | 0.6526918 | -0.3658451 | 1.000000000 | -0.02524993 | 0.29554425 | 0.519067096 | 0.3304167 |\n| Color intensity | 0.54636420 | 0.24898534 | 0.258887259 | 0.01873198 | 0.19995001 | -0.05513642 | -0.1723794 | 0.1390570 | -0.025249931 | 1.00000000 | -0.52181319 | -0.428814942 | 0.3161001 |\n| Hue | -0.07174720 | -0.56129569 | -0.074666889 | -0.27395522 | 0.05539820 | 0.43368134 | 0.5434786 | -0.2626396 | 0.295544253 | -0.52181319 | 1.00000000 | 0.565468293 | 0.2361834 |\n| OD280/OD315 of diluted wines | 0.07234319 | -0.36871043 | 0.003911231 | -0.27676855 | 0.06600394 | 0.69994936 | 0.7871939 | -0.5032696 | 0.519067096 | -0.42881494 | 0.56546829 | 1.000000000 | 0.3127611 |\n| Proline | 0.64372004 | -0.19201056 | 0.223626264 | -0.44059693 | 0.39335085 | 0.49811488 | 0.4941931 | -0.3113852 | 0.330416700 | 0.31610011 | 0.23618345 | 0.312761075 | 1.0000000 |\n\n","text/latex":"A matrix: 13 × 13 of type dbl\n\\begin{tabular}{r|lllllllllllll}\n & Alcohol & Malic acid & Ash & Alcalinity of ash & Magnesium & Total phenols & Flavanoids & Nonflavanoid phenols & Proanthocyanins & Color intensity & Hue & OD280/OD315 of diluted wines & Proline\\\\\n\\hline\n\tAlcohol & 1.00000000 & 0.09439694 & 0.211544596 & -0.31023514 & 0.27079823 & 0.28910112 & 0.2368149 & -0.1559295 & 0.136697912 & 0.54636420 & -0.07174720 & 0.072343187 & 0.6437200\\\\\n\tMalic acid & 0.09439694 & 1.00000000 & 0.164045470 & 0.28850040 & -0.05457510 & -0.33516700 & -0.4110066 & 0.2929771 & -0.220746187 & 0.24898534 & -0.56129569 & -0.368710428 & -0.1920106\\\\\n\tAsh & 0.21154460 & 0.16404547 & 1.000000000 & 0.44336719 & 0.28658669 & 0.12897954 & 0.1150773 & 0.1862304 & 0.009651935 & 0.25888726 & -0.07466689 & 0.003911231 & 0.2236263\\\\\n\tAlcalinity of ash & -0.31023514 & 0.28850040 & 0.443367187 & 1.00000000 & -0.08333309 & -0.32111332 & -0.3513699 & 0.3619217 & -0.197326836 & 0.01873198 & -0.27395522 & -0.276768549 & -0.4405969\\\\\n\tMagnesium & 0.27079823 & -0.05457510 & 0.286586691 & -0.08333309 & 1.00000000 & 0.21440123 & 0.1957838 & -0.2562940 & 0.236440610 & 0.19995001 & 0.05539820 & 0.066003936 & 0.3933508\\\\\n\tTotal phenols & 0.28910112 & -0.33516700 & 0.128979538 & -0.32111332 & 0.21440123 & 1.00000000 & 0.8645635 & -0.4499353 & 0.612413084 & -0.05513642 & 0.43368134 & 0.699949365 & 0.4981149\\\\\n\tFlavanoids & 0.23681493 & -0.41100659 & 0.115077279 & -0.35136986 & 0.19578377 & 0.86456350 & 1.0000000 & -0.5378996 & 0.652691769 & -0.17237940 & 0.54347857 & 0.787193902 & 0.4941931\\\\\n\tNonflavanoid phenols & -0.15592947 & 0.29297713 & 0.186230446 & 0.36192172 & -0.25629405 & -0.44993530 & -0.5378996 & 1.0000000 & -0.365845099 & 0.13905701 & -0.26263963 & -0.503269596 & -0.3113852\\\\\n\tProanthocyanins & 0.13669791 & -0.22074619 & 0.009651935 & -0.19732684 & 0.23644061 & 0.61241308 & 0.6526918 & -0.3658451 & 1.000000000 & -0.02524993 & 0.29554425 & 0.519067096 & 0.3304167\\\\\n\tColor intensity & 0.54636420 & 0.24898534 & 0.258887259 & 0.01873198 & 0.19995001 & -0.05513642 & -0.1723794 & 0.1390570 & -0.025249931 & 1.00000000 & -0.52181319 & -0.428814942 & 0.3161001\\\\\n\tHue & -0.07174720 & -0.56129569 & -0.074666889 & -0.27395522 & 0.05539820 & 0.43368134 & 0.5434786 & -0.2626396 & 0.295544253 & -0.52181319 & 1.00000000 & 0.565468293 & 0.2361834\\\\\n\tOD280/OD315 of diluted wines & 0.07234319 & -0.36871043 & 0.003911231 & -0.27676855 & 0.06600394 & 0.69994936 & 0.7871939 & -0.5032696 & 0.519067096 & -0.42881494 & 0.56546829 & 1.000000000 & 0.3127611\\\\\n\tProline & 0.64372004 & -0.19201056 & 0.223626264 & -0.44059693 & 0.39335085 & 0.49811488 & 0.4941931 & -0.3113852 & 0.330416700 & 0.31610011 & 0.23618345 & 0.312761075 & 1.0000000\\\\\n\\end{tabular}\n","text/plain":[" Alcohol Malic acid Ash \n","Alcohol 1.00000000 0.09439694 0.211544596\n","Malic acid 0.09439694 1.00000000 0.164045470\n","Ash 0.21154460 0.16404547 1.000000000\n","Alcalinity of ash -0.31023514 0.28850040 0.443367187\n","Magnesium 0.27079823 -0.05457510 0.286586691\n","Total phenols 0.28910112 -0.33516700 0.128979538\n","Flavanoids 0.23681493 -0.41100659 0.115077279\n","Nonflavanoid phenols -0.15592947 0.29297713 0.186230446\n","Proanthocyanins 0.13669791 -0.22074619 0.009651935\n","Color intensity 0.54636420 0.24898534 0.258887259\n","Hue -0.07174720 -0.56129569 -0.074666889\n","OD280/OD315 of diluted wines 0.07234319 -0.36871043 0.003911231\n","Proline 0.64372004 -0.19201056 0.223626264\n"," Alcalinity of ash Magnesium Total phenols\n","Alcohol -0.31023514 0.27079823 0.28910112 \n","Malic acid 0.28850040 -0.05457510 -0.33516700 \n","Ash 0.44336719 0.28658669 0.12897954 \n","Alcalinity of ash 1.00000000 -0.08333309 -0.32111332 \n","Magnesium -0.08333309 1.00000000 0.21440123 \n","Total phenols -0.32111332 0.21440123 1.00000000 \n","Flavanoids -0.35136986 0.19578377 0.86456350 \n","Nonflavanoid phenols 0.36192172 -0.25629405 -0.44993530 \n","Proanthocyanins -0.19732684 0.23644061 0.61241308 \n","Color intensity 0.01873198 0.19995001 -0.05513642 \n","Hue -0.27395522 0.05539820 0.43368134 \n","OD280/OD315 of diluted wines -0.27676855 0.06600394 0.69994936 \n","Proline -0.44059693 0.39335085 0.49811488 \n"," Flavanoids Nonflavanoid phenols Proanthocyanins\n","Alcohol 0.2368149 -0.1559295 0.136697912 \n","Malic acid -0.4110066 0.2929771 -0.220746187 \n","Ash 0.1150773 0.1862304 0.009651935 \n","Alcalinity of ash -0.3513699 0.3619217 -0.197326836 \n","Magnesium 0.1957838 -0.2562940 0.236440610 \n","Total phenols 0.8645635 -0.4499353 0.612413084 \n","Flavanoids 1.0000000 -0.5378996 0.652691769 \n","Nonflavanoid phenols -0.5378996 1.0000000 -0.365845099 \n","Proanthocyanins 0.6526918 -0.3658451 1.000000000 \n","Color intensity -0.1723794 0.1390570 -0.025249931 \n","Hue 0.5434786 -0.2626396 0.295544253 \n","OD280/OD315 of diluted wines 0.7871939 -0.5032696 0.519067096 \n","Proline 0.4941931 -0.3113852 0.330416700 \n"," Color intensity Hue \n","Alcohol 0.54636420 -0.07174720\n","Malic acid 0.24898534 -0.56129569\n","Ash 0.25888726 -0.07466689\n","Alcalinity of ash 0.01873198 -0.27395522\n","Magnesium 0.19995001 0.05539820\n","Total phenols -0.05513642 0.43368134\n","Flavanoids -0.17237940 0.54347857\n","Nonflavanoid phenols 0.13905701 -0.26263963\n","Proanthocyanins -0.02524993 0.29554425\n","Color intensity 1.00000000 -0.52181319\n","Hue -0.52181319 1.00000000\n","OD280/OD315 of diluted wines -0.42881494 0.56546829\n","Proline 0.31610011 0.23618345\n"," OD280/OD315 of diluted wines Proline \n","Alcohol 0.072343187 0.6437200\n","Malic acid -0.368710428 -0.1920106\n","Ash 0.003911231 0.2236263\n","Alcalinity of ash -0.276768549 -0.4405969\n","Magnesium 0.066003936 0.3933508\n","Total phenols 0.699949365 0.4981149\n","Flavanoids 0.787193902 0.4941931\n","Nonflavanoid phenols -0.503269596 -0.3113852\n","Proanthocyanins 0.519067096 0.3304167\n","Color intensity -0.428814942 0.3161001\n","Hue 0.565468293 0.2361834\n","OD280/OD315 of diluted wines 1.000000000 0.3127611\n","Proline 0.312761075 1.0000000"]},"metadata":{}}]},{"cell_type":"markdown","source":["In statistics, Spearman's rank correlation coefficient or Spearman's ρ, named after Charles Spearman (See Wikipedia https://en.wikipedia.org/wiki/Spearman%27s_rank_correlation_coefficient) and often denoted by the Greek letter\n","rho, is a nonparametric measure of rank correlation (statistical dependence between the rankings of two variables). It assesses how well the relationship between two variables can be described using a monotonic function."],"metadata":{"id":"VYtY4bcgHqyn"}},{"cell_type":"code","source":["#Antoher possibility is present the correlation matrix using Spearman\n","cor(wineb[2:14], method=\"spearman\")"],"metadata":{"id":"Fvia1sDpMohK","colab":{"base_uri":"https://localhost:8080/","height":526},"executionInfo":{"status":"ok","timestamp":1717435225858,"user_tz":-120,"elapsed":406,"user":{"displayName":"toni Monleon","userId":"05269440626861479105"}},"outputId":"86a756bd-7412-4fc3-b447-6c852a8b3656"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/html":["\n","\n","\n","\t\n","\n","\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\n","
A matrix: 13 × 13 of type dbl
AlcoholMalic acidAshAlcalinity of ashMagnesiumTotal phenolsFlavanoidsNonflavanoid phenolsProanthocyaninsColor intensityHueOD280/OD315 of diluted winesProline
Alcohol 1.00000000 0.14043018 0.24372221-0.30659789 0.36550338 0.31092007 0.29473988-0.16220710 0.19273419 0.63542453-0.02420284 0.10305021 0.63357986
Malic acid 0.14043018 1.00000000 0.23067358 0.30406913 0.08018776-0.28022473-0.32520214 0.25523566-0.24482493 0.29030671-0.56026489-0.25518507-0.05746615
Ash 0.24372221 0.23067358 1.00000000 0.36637364 0.36148788 0.13219338 0.07879581 0.14558274 0.02438423 0.28304684-0.05018326-0.00749991 0.25316348
Alcalinity of ash-0.30659789 0.30406913 0.36637364 1.00000000-0.16955822-0.37665712-0.44376999 0.38938987-0.25369530-0.07377598-0.35250702-0.32588976-0.45608973
Magnesium 0.36550338 0.08018776 0.36148788-0.16955822 1.00000000 0.24641696 0.23316660-0.23678609 0.17364700 0.35702874 0.03609452 0.05696275 0.50757486
Total phenols 0.31092007-0.28022473 0.13219338-0.37665712 0.24641696 1.00000000 0.87940437-0.44801307 0.66668937 0.01116179 0.43945748 0.68720700 0.41946986
Flavanoids 0.29473988-0.32520214 0.07879581-0.44376999 0.23316660 0.87940437 1.00000000-0.54389740 0.73032169-0.04291039 0.53543014 0.74153289 0.42990441
Nonflavanoid phenols-0.16220710 0.25523566 0.14558274 0.38938987-0.23678609-0.44801307-0.54389740 1.00000000-0.38462885 0.05963853-0.26781255-0.49494998-0.27011194
Proanthocyanins 0.19273419-0.24482493 0.02438423-0.25369530 0.17364700 0.66668937 0.73032169-0.38462885 1.00000000-0.03094715 0.34279465 0.55403138 0.30824932
Color intensity 0.63542453 0.29030671 0.28304684-0.07377598 0.35702874 0.01116179-0.04291039 0.05963853-0.03094715 1.00000000-0.41852176-0.31751560 0.45709642
Hue-0.02420284-0.56026489-0.05018326-0.35250702 0.03609452 0.43945748 0.53543014-0.26781255 0.34279465-0.41852176 1.00000000 0.48545434 0.20774049
OD280/OD315 of diluted wines 0.10305021-0.25518507-0.00749991-0.32588976 0.05696275 0.68720700 0.74153289-0.49494998 0.55403138-0.31751560 0.48545434 1.00000000 0.25326568
Proline 0.63357986-0.05746615 0.25316348-0.45608973 0.50757486 0.41946986 0.42990441-0.27011194 0.30824932 0.45709642 0.20774049 0.25326568 1.00000000
\n"],"text/markdown":"\nA matrix: 13 × 13 of type dbl\n\n| | Alcohol | Malic acid | Ash | Alcalinity of ash | Magnesium | Total phenols | Flavanoids | Nonflavanoid phenols | Proanthocyanins | Color intensity | Hue | OD280/OD315 of diluted wines | Proline |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| Alcohol | 1.00000000 | 0.14043018 | 0.24372221 | -0.30659789 | 0.36550338 | 0.31092007 | 0.29473988 | -0.16220710 | 0.19273419 | 0.63542453 | -0.02420284 | 0.10305021 | 0.63357986 |\n| Malic acid | 0.14043018 | 1.00000000 | 0.23067358 | 0.30406913 | 0.08018776 | -0.28022473 | -0.32520214 | 0.25523566 | -0.24482493 | 0.29030671 | -0.56026489 | -0.25518507 | -0.05746615 |\n| Ash | 0.24372221 | 0.23067358 | 1.00000000 | 0.36637364 | 0.36148788 | 0.13219338 | 0.07879581 | 0.14558274 | 0.02438423 | 0.28304684 | -0.05018326 | -0.00749991 | 0.25316348 |\n| Alcalinity of ash | -0.30659789 | 0.30406913 | 0.36637364 | 1.00000000 | -0.16955822 | -0.37665712 | -0.44376999 | 0.38938987 | -0.25369530 | -0.07377598 | -0.35250702 | -0.32588976 | -0.45608973 |\n| Magnesium | 0.36550338 | 0.08018776 | 0.36148788 | -0.16955822 | 1.00000000 | 0.24641696 | 0.23316660 | -0.23678609 | 0.17364700 | 0.35702874 | 0.03609452 | 0.05696275 | 0.50757486 |\n| Total phenols | 0.31092007 | -0.28022473 | 0.13219338 | -0.37665712 | 0.24641696 | 1.00000000 | 0.87940437 | -0.44801307 | 0.66668937 | 0.01116179 | 0.43945748 | 0.68720700 | 0.41946986 |\n| Flavanoids | 0.29473988 | -0.32520214 | 0.07879581 | -0.44376999 | 0.23316660 | 0.87940437 | 1.00000000 | -0.54389740 | 0.73032169 | -0.04291039 | 0.53543014 | 0.74153289 | 0.42990441 |\n| Nonflavanoid phenols | -0.16220710 | 0.25523566 | 0.14558274 | 0.38938987 | -0.23678609 | -0.44801307 | -0.54389740 | 1.00000000 | -0.38462885 | 0.05963853 | -0.26781255 | -0.49494998 | -0.27011194 |\n| Proanthocyanins | 0.19273419 | -0.24482493 | 0.02438423 | -0.25369530 | 0.17364700 | 0.66668937 | 0.73032169 | -0.38462885 | 1.00000000 | -0.03094715 | 0.34279465 | 0.55403138 | 0.30824932 |\n| Color intensity | 0.63542453 | 0.29030671 | 0.28304684 | -0.07377598 | 0.35702874 | 0.01116179 | -0.04291039 | 0.05963853 | -0.03094715 | 1.00000000 | -0.41852176 | -0.31751560 | 0.45709642 |\n| Hue | -0.02420284 | -0.56026489 | -0.05018326 | -0.35250702 | 0.03609452 | 0.43945748 | 0.53543014 | -0.26781255 | 0.34279465 | -0.41852176 | 1.00000000 | 0.48545434 | 0.20774049 |\n| OD280/OD315 of diluted wines | 0.10305021 | -0.25518507 | -0.00749991 | -0.32588976 | 0.05696275 | 0.68720700 | 0.74153289 | -0.49494998 | 0.55403138 | -0.31751560 | 0.48545434 | 1.00000000 | 0.25326568 |\n| Proline | 0.63357986 | -0.05746615 | 0.25316348 | -0.45608973 | 0.50757486 | 0.41946986 | 0.42990441 | -0.27011194 | 0.30824932 | 0.45709642 | 0.20774049 | 0.25326568 | 1.00000000 |\n\n","text/latex":"A matrix: 13 × 13 of type dbl\n\\begin{tabular}{r|lllllllllllll}\n & Alcohol & Malic acid & Ash & Alcalinity of ash & Magnesium & Total phenols & Flavanoids & Nonflavanoid phenols & Proanthocyanins & Color intensity & Hue & OD280/OD315 of diluted wines & Proline\\\\\n\\hline\n\tAlcohol & 1.00000000 & 0.14043018 & 0.24372221 & -0.30659789 & 0.36550338 & 0.31092007 & 0.29473988 & -0.16220710 & 0.19273419 & 0.63542453 & -0.02420284 & 0.10305021 & 0.63357986\\\\\n\tMalic acid & 0.14043018 & 1.00000000 & 0.23067358 & 0.30406913 & 0.08018776 & -0.28022473 & -0.32520214 & 0.25523566 & -0.24482493 & 0.29030671 & -0.56026489 & -0.25518507 & -0.05746615\\\\\n\tAsh & 0.24372221 & 0.23067358 & 1.00000000 & 0.36637364 & 0.36148788 & 0.13219338 & 0.07879581 & 0.14558274 & 0.02438423 & 0.28304684 & -0.05018326 & -0.00749991 & 0.25316348\\\\\n\tAlcalinity of ash & -0.30659789 & 0.30406913 & 0.36637364 & 1.00000000 & -0.16955822 & -0.37665712 & -0.44376999 & 0.38938987 & -0.25369530 & -0.07377598 & -0.35250702 & -0.32588976 & -0.45608973\\\\\n\tMagnesium & 0.36550338 & 0.08018776 & 0.36148788 & -0.16955822 & 1.00000000 & 0.24641696 & 0.23316660 & -0.23678609 & 0.17364700 & 0.35702874 & 0.03609452 & 0.05696275 & 0.50757486\\\\\n\tTotal phenols & 0.31092007 & -0.28022473 & 0.13219338 & -0.37665712 & 0.24641696 & 1.00000000 & 0.87940437 & -0.44801307 & 0.66668937 & 0.01116179 & 0.43945748 & 0.68720700 & 0.41946986\\\\\n\tFlavanoids & 0.29473988 & -0.32520214 & 0.07879581 & -0.44376999 & 0.23316660 & 0.87940437 & 1.00000000 & -0.54389740 & 0.73032169 & -0.04291039 & 0.53543014 & 0.74153289 & 0.42990441\\\\\n\tNonflavanoid phenols & -0.16220710 & 0.25523566 & 0.14558274 & 0.38938987 & -0.23678609 & -0.44801307 & -0.54389740 & 1.00000000 & -0.38462885 & 0.05963853 & -0.26781255 & -0.49494998 & -0.27011194\\\\\n\tProanthocyanins & 0.19273419 & -0.24482493 & 0.02438423 & -0.25369530 & 0.17364700 & 0.66668937 & 0.73032169 & -0.38462885 & 1.00000000 & -0.03094715 & 0.34279465 & 0.55403138 & 0.30824932\\\\\n\tColor intensity & 0.63542453 & 0.29030671 & 0.28304684 & -0.07377598 & 0.35702874 & 0.01116179 & -0.04291039 & 0.05963853 & -0.03094715 & 1.00000000 & -0.41852176 & -0.31751560 & 0.45709642\\\\\n\tHue & -0.02420284 & -0.56026489 & -0.05018326 & -0.35250702 & 0.03609452 & 0.43945748 & 0.53543014 & -0.26781255 & 0.34279465 & -0.41852176 & 1.00000000 & 0.48545434 & 0.20774049\\\\\n\tOD280/OD315 of diluted wines & 0.10305021 & -0.25518507 & -0.00749991 & -0.32588976 & 0.05696275 & 0.68720700 & 0.74153289 & -0.49494998 & 0.55403138 & -0.31751560 & 0.48545434 & 1.00000000 & 0.25326568\\\\\n\tProline & 0.63357986 & -0.05746615 & 0.25316348 & -0.45608973 & 0.50757486 & 0.41946986 & 0.42990441 & -0.27011194 & 0.30824932 & 0.45709642 & 0.20774049 & 0.25326568 & 1.00000000\\\\\n\\end{tabular}\n","text/plain":[" Alcohol Malic acid Ash \n","Alcohol 1.00000000 0.14043018 0.24372221\n","Malic acid 0.14043018 1.00000000 0.23067358\n","Ash 0.24372221 0.23067358 1.00000000\n","Alcalinity of ash -0.30659789 0.30406913 0.36637364\n","Magnesium 0.36550338 0.08018776 0.36148788\n","Total phenols 0.31092007 -0.28022473 0.13219338\n","Flavanoids 0.29473988 -0.32520214 0.07879581\n","Nonflavanoid phenols -0.16220710 0.25523566 0.14558274\n","Proanthocyanins 0.19273419 -0.24482493 0.02438423\n","Color intensity 0.63542453 0.29030671 0.28304684\n","Hue -0.02420284 -0.56026489 -0.05018326\n","OD280/OD315 of diluted wines 0.10305021 -0.25518507 -0.00749991\n","Proline 0.63357986 -0.05746615 0.25316348\n"," Alcalinity of ash Magnesium Total phenols\n","Alcohol -0.30659789 0.36550338 0.31092007 \n","Malic acid 0.30406913 0.08018776 -0.28022473 \n","Ash 0.36637364 0.36148788 0.13219338 \n","Alcalinity of ash 1.00000000 -0.16955822 -0.37665712 \n","Magnesium -0.16955822 1.00000000 0.24641696 \n","Total phenols -0.37665712 0.24641696 1.00000000 \n","Flavanoids -0.44376999 0.23316660 0.87940437 \n","Nonflavanoid phenols 0.38938987 -0.23678609 -0.44801307 \n","Proanthocyanins -0.25369530 0.17364700 0.66668937 \n","Color intensity -0.07377598 0.35702874 0.01116179 \n","Hue -0.35250702 0.03609452 0.43945748 \n","OD280/OD315 of diluted wines -0.32588976 0.05696275 0.68720700 \n","Proline -0.45608973 0.50757486 0.41946986 \n"," Flavanoids Nonflavanoid phenols Proanthocyanins\n","Alcohol 0.29473988 -0.16220710 0.19273419 \n","Malic acid -0.32520214 0.25523566 -0.24482493 \n","Ash 0.07879581 0.14558274 0.02438423 \n","Alcalinity of ash -0.44376999 0.38938987 -0.25369530 \n","Magnesium 0.23316660 -0.23678609 0.17364700 \n","Total phenols 0.87940437 -0.44801307 0.66668937 \n","Flavanoids 1.00000000 -0.54389740 0.73032169 \n","Nonflavanoid phenols -0.54389740 1.00000000 -0.38462885 \n","Proanthocyanins 0.73032169 -0.38462885 1.00000000 \n","Color intensity -0.04291039 0.05963853 -0.03094715 \n","Hue 0.53543014 -0.26781255 0.34279465 \n","OD280/OD315 of diluted wines 0.74153289 -0.49494998 0.55403138 \n","Proline 0.42990441 -0.27011194 0.30824932 \n"," Color intensity Hue \n","Alcohol 0.63542453 -0.02420284\n","Malic acid 0.29030671 -0.56026489\n","Ash 0.28304684 -0.05018326\n","Alcalinity of ash -0.07377598 -0.35250702\n","Magnesium 0.35702874 0.03609452\n","Total phenols 0.01116179 0.43945748\n","Flavanoids -0.04291039 0.53543014\n","Nonflavanoid phenols 0.05963853 -0.26781255\n","Proanthocyanins -0.03094715 0.34279465\n","Color intensity 1.00000000 -0.41852176\n","Hue -0.41852176 1.00000000\n","OD280/OD315 of diluted wines -0.31751560 0.48545434\n","Proline 0.45709642 0.20774049\n"," OD280/OD315 of diluted wines Proline \n","Alcohol 0.10305021 0.63357986\n","Malic acid -0.25518507 -0.05746615\n","Ash -0.00749991 0.25316348\n","Alcalinity of ash -0.32588976 -0.45608973\n","Magnesium 0.05696275 0.50757486\n","Total phenols 0.68720700 0.41946986\n","Flavanoids 0.74153289 0.42990441\n","Nonflavanoid phenols -0.49494998 -0.27011194\n","Proanthocyanins 0.55403138 0.30824932\n","Color intensity -0.31751560 0.45709642\n","Hue 0.48545434 0.20774049\n","OD280/OD315 of diluted wines 1.00000000 0.25326568\n","Proline 0.25326568 1.00000000"]},"metadata":{}}]},{"cell_type":"markdown","source":["A Scatterplot with the Data Points Labelled by their Group\n","If you see an interesting scatterplot for two variables in the matrix scatterplot, you may want to plot that scatterplot in more detail, with the data points labelled by their group (their cultivar in this case).\n","\n","For example, in the matrix scatterplot above, the cell in the third column of the fourth row down is a scatterplot of V5 (x-axis) against V4 (y-axis). If you look at this scatterplot, it appears that there may be a positive relationship between V5 and V4 (\"Ash\" vs\"Alcalinity of ash\").\n","\n","We may therefore decide to examine the relationship between V5 and V4 more closely, by plotting a scatterplot of these two variable, with the data points labelled by their group (their cultivar). To plot a scatterplot of two variables, we can use the “plot” R function. The V4 and V5 variables are stored in the columns V4 and V5 of the variable “wine”, so can be accessed by typing V4 or V5. Therefore, to plot the scatterplot, we type:"],"metadata":{"id":"sPwHCBGxrRnV"}},{"cell_type":"markdown","source":["If we want to label the data points by their group (the cultivar of wine here), we can use the “text” function in R to plot some text beside every data point. In this case, the cultivar of wine is stored in the column V1 of the variable “wine”, so we type:"],"metadata":{"id":"6ZSkdL8LsJRa"}},{"cell_type":"markdown","source":["If you look at the help page for the “text” function, you will see that “pos=4” will plot the text just to the right of the symbol for a data point. The “cex=0.5” option will plot the text at half the default size, and the “col=red” option will plot the text in red. This gives us the following plot:"],"metadata":{"id":"fDydfDOmsJAj"}},{"cell_type":"markdown","source":[" Re-run data in order to use the original variable name : V1, V2, etc\n"],"metadata":{"id":"Xe3OLsICIRHf"}},{"cell_type":"code","source":[" wine <- read.table(\"http://archive.ics.uci.edu/ml/machine-learning-databases/wine/wine.data\", sep=\",\")"],"metadata":{"id":"GGnrtb-LIdUW"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["plot(wine$V4, wine$V5) #\"Ash\",\"Alcalinity of ash\"\n","text(wine$V4, wine$V5, wine$V1, cex=0.7, pos=4, col=\"red\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":437},"id":"r-sIZ03KsJIy","executionInfo":{"status":"ok","timestamp":1717435494013,"user_tz":-120,"elapsed":815,"user":{"displayName":"toni Monleon","userId":"05269440626861479105"}},"outputId":"8a20d578-a633-43d1-a1d4-1651d117d15a"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["plot without title"],"image/png":"iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAIAAAByhViMAAAACXBIWXMAABJ0AAASdAHeZh94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WHD4MMjJgbg5btsDTp6Cu3mpk9L66erCj\n4+XCwu8ZjJLY2N3x8e/8/SsjI+l0OvtbMDc3P3369OPHj8eOHatHo13JzdW4etUxMPDf6uqP\nASeRICUFbGw+mlhXB8HB8PvvoK0Nw4ZBYCCkp/dUuD6XkydPGhgYPHv27OrVq/Ly8tOmTSso\nKGBfmjRpkq2t7dSpU2k0Gjs+I0eOzH75ktd97oPUBupTikB0KSixQyB6Ow8ePLC2tm5tbT1z\n5kxOTs6tW7fGjh0bEBAgeOWLjYqK4JWvfk1FRYWdnd2tW7d27dq1aNEiMpk8atQoKSkpEolk\nYGAwePBgIpFIoVCcnJzExcX9/PwOHjx47+VLvWfPChsb2yNWXw86OiUs1v6iolcYFrx/v0Fm\n5g8LF7K0tLZu3Xpo06a3YmInTpx48eLFnTt3Jk+ePH/+fHt7e0VFxcNRUYNzc09eu2ZhYTF+\n/PiUmJj2gAcGgoLCJ1aqqsKaNUAkAgAUFMCpU+Dh0QPB+nxWrVq1ePHiIUOGqKqqvnjx4vjx\n4xUVFZaWlhkZGQBw6dKlR48eMZnMqKio3Nzc2NhYU1NTs717j8fEfJzizBnw9YXwcPD3Bxig\nTykC0eX0aLOVvgHqY4foQZhMpr6+/vz583nkMTExBAIhNTW14ymePsXpdDwhoUvs603MmTPH\nwsKCwWA8fvyYQCBcvXoVx/Fhw4aJiYnt3r379OnTJBJJS0urpaXl4cOHZDI5Nja2ubl51KhR\nEyZMwPGPgfph7FgGkdh04wZHyIqP99LWfg8Q/f33nNu9f/+eTqdjGMbTme/Cpk3vAcrOnfso\nCgnBHR0/sTUnByeTcQzDFy/uDceEdMitW7eIROLdu3ePHTumq6vLFra1tXl7ew8fPry6ulpO\nTs7V1dXBwYFb6+DBgxISErVr1+KOjvjly7iSEp6WJvgGA+YpRfQPenMfO5TYdQxK7BA9SFxc\nnJiYWFVVFf8lV1fX5cuXd6B/+zauqIifOdMlxvUm6uvrqVRqTEwMjuNLlixhHzvx/PlzAFi9\nevWQIUPGjBkTFBREIpESEhJwHJ85c6aPjw+O40lJSQQCoTIigh2ouujocoDMjRtx/GP0Xhw6\nVA7wt5ubra0t546nTp1SUFDw8vL6pBny7du4ouIPamo7duz4KORP7Jqb8efP8ZgY3MwMnzev\ny6LyzZgxY8a0adNwHH/48CGBQCgpKWHLX79+jWHYL7/8oqysPHHiRJ5/gbBYLCMjo3vjxuF2\ndqIOixswTymi39CbEzu0FItA9Gqys7ONjIzkBLVytbOzy8rKEqXMs/LVrykoKGhsbLS3tweA\nrKwsOzs7AMjOzlZQUJg0aVJOTk5WVtbo0aMNDAzYQbO3t2f/wcrKagaGSc+bB+HhgOPUWbNm\nAuhu3PgxejiuvXbt90pK8suWZWdnc+6YnZ1taWnp6Oj48Vv4T6V63LgOvhoxMTA1BQ8PCA2F\nY8egvLyr4vKN4ITUxsbG1NR0zZo1OI4DgJaW1gR5+aCdO280NFy7di0wMJBbC8MwW1vbiooK\nqKuD0lJYuBA0ND5+2DWwA+kpRSC6AZTYIRC9GgzDWCyWwEssFktUY9grV2D1aoiLAze3rjKu\nN8EOBTtWGNbeep0dPW4hjuMYhgFX9LCrV3e3taXv2QNMJqxenXPw4E0A7OrV9ugxmbB6ddz6\n9XcoFBaLxdZlw5m8/VvgCrioryYmBiwsgPOdsk/I4Jq2x2ltbc3JyXnx4kV7ZSsAcIWUQCCc\nOnUqNjbW1dU1MjKy7NdfD1VXZ2NYfX39+vXr2Yk1N+1Bk5UFHOf9KCgMtKcUgegGUGKHQPRq\nzMzMcnJyKioq+C/dv39/yJAhgtUYDAgKgpAQUFCA4uL2j5AEsX+go6MjKSl5//59ADAzM+P8\nobq6+tKlS4MHDzYzM7t58+bLly/NzMyAEz0GozUwcDOBoGNqCvPmwfffa6mpWZDJhAUL4Pvv\nQUyMLTTQ1iYUFz84f36MoSEnjGZmZikpKXfv3mXPwwk4XlSUf/euvYYGsFhQUgLFxVBXB0xm\n+7dgaQmFhbBiBeTnw7NnsHYt2NuDomIPho5DVVXVvHnzpKSkjI2NTUxMpKSkAgMDq6qqgCuk\nADBs2LDU1FR5efl58+Zt3Lx5OIuVQiAQicSff/6ZZ0JWcXH+3bsadPpH99vaPl4eeE8pAtEd\n9OAycF8B7bFD9CCtra2mpqYBAQFtn26xP3v2LJFIzMjIEKx25QoOwPupqOgOi3uOhQsXGhsb\nV1dXP336lEgkRkRE4DhubW1NIpH27dt3/vx5IpGop6fX1tbGLgW4detW08WLAgIl+vNfGBkM\nhqysLIZhDx8+FBpwOp1XWFSEJyXhjo44hYLLy+Pe3nhRUY+GrR32Yb7m5uZRUVGlpaVlZWVR\nUVEWFhaGhoZVVVX37t0jEAjXr1/nVmlpaXFxcbGzs2v66adHZPK6det45mwSFxfgPocB+ZQi\n+ge9eY8dSuw6BiV2iJ4lNTVVRkZm5MiR4eHhKSkply9fXrRoEZFI3LNnT0+b1ruoqamxsLDQ\n0dHZv3//smXLiESira2thIQEhULR1tbW19cnk8kUCsXW1pZMJs+cOTM0NNTExERPT49TCsCm\nvLzcxMTEwMDg4MGDSUlJcXFxmzdvlpSUZOuePHkyOTk5NjZ2xYoVRCKRRCJNnDjx/PnzT548\niYyMnDFjBolE+ueff3oqCF/M8uXLTU1N6+rquIUMBsPU1HTp0qU4jv/0009kMnnVqlXXrl1L\nTk4+ceKElZWVsrJyTk4OHhJSM3iwuLi4h4cHJxTTp08nkUj/93//10MOIRBdCErs+jYosUP0\nOAUFBTNnzlRVVQUAKSkpJycnnncnCDb19fXBwcH6+voEAoFEIrGzMQCQkJCQlJQEADExMUlJ\nSTKZjGGYjo7OypUrq6ur+eepra1du3atrq4ugUAQExMbNmxYaGhoUVFRYGCguro6AEhKSjo6\nOkZFRWVkZHh7eysoKACAjIyMu7v7gwcPut/xr6StrU1OTu706dP8l8LDw2VlZdkvjCMjIx0c\nHCQkJABAQ0Nj/vz57TlxSAju6Jienj5lyhR2KGRlZcePH5+UlNTNjiAQ3UNvTuza98MiRBAa\nGrpo0SIGg0Gj0XraFsRAp76+XlJSEutNe+17J01NTUQikUwm4zje0NDA/svb0NBApVIJBEJb\nWxuTyaRSqR3O09jYSCaTSSQSt5AzD7eQwWBISUl9Wy+6jfLycmVl5efPn5vyHP8FkJ2dbWpq\nWlZWpqyszJawWKzGxkZ2otzO1q1w/Tr8twnvW4bi0SOYMQNUVDiTIxC9ASaTSaFQEhMTHRwc\netoWXlDxBALRl6DRaCir6wzi4uJkMhkAMAzj/JNMUlKSnY0RicTOZHUAQKVSebI67nm46btZ\nHQCIiYkBgMBjiNlCdjDZEAiET7I6PkSEori4+MaNG48fP2YwGB2bJfCoWQQCIRKU2CEQCMRA\nR0ZGRkdH59atW/yXbt++ra2tLbCTIgAIKPvlrnvl4vHjx8OHD9fQ0Jg8ebK9vb28vPzChQvr\n6upEmcV/0i4CgegIlNghBiT5+eDpCbKyoKwMs2e3N0pFIESTlgauriAtDaqq8N138O6d0JGP\nHoG+PowY0Y3GfS1LlizZsWPHixcvuIU5OTnbt29fsmSJUDVTU9DQgF27IDm5ve1waSn/qKSk\nJGdnZzMzs6ysrIaGhrq6uujo6ISEBHd3d4GvCdvhP2kXgUB0BErsEP2Eurq6Bw8eXLx4MT09\nvaWlRdRQHAcPDyCRICkJYmMhLQ1WruwuMxG9CCaTmZaWduHChaSkpPr6ehEj379/n3jzJnPU\nqCoNjba0NIiNhexsEJbu9M0FxFWrVjk5Odna2q5bt+7ChQsXLlxYt26djY3NyJEjV69eLVTt\n/XvetsPq6vyjFi9ePG3atJMnT5qYmLBXcsePH3/nzp28vLzQ0NAu9AqBGHigxA7R52EymevW\nrVNWVh41atSiRYuGDh2qpaX1zz//CFV49w4MDeHwYTAyAktLWLUK7t7tRnsRvYLjx49raGgM\nHz58yZIljo6OysrKmzZt4j5rgU1DQ8OSJUuUlJT8J07cymIpnzih5+ISU1QEgYGQni546r65\ngEgikS5cuLB3797k5ORFixYtWrTo8ePHe/fuvXjxIv8uw88iJyfn6dOnmzZt4pGrqKgsWLDg\n3LlzXzM5AoHgASV2iD7PzJkzw8PDw8PD6+vrKyoqKisrly1bNnfu3GPHjglWUFGBS5dASan9\nx5IS0NHpNmsRvYH9+/cvWbJk7dq11dXV5eXlDAbj2LFjoaGhCxYs4B7GYrEmT558/fr1qKio\nvIaGXxsa3paVBQQErJk6tfqPP8DDA0DQqmufXUDEMCwwMDA+Pr6ysrKysjIhISEwMPDri3Xy\n8/OpVKqenh7/pSFDhuTn53/l/AgEgpuv+ncYAtHj3LhxIzo6OjU1lXO4lry8fHBwsJSU1Jo1\na3x8fOh0uij99HTYvRuio7vDVkTvoKKiYsOGDX/99RfnxHoJCYnp06fr6+vb2dn5+vpSKJSi\noiJdXd28vLykpKSMjAxtbW32SOXa2u27d29razv19q3/jh1iYWGwfTuYm6NtmiKgUqlMJrOl\npYW7tJYNu3FMj1iFQPRX0Bs7RN/mwoULkyZN4j8yddGiRRiG3bx5U5RyfDyMHQuHDoGTUxea\niOhlXL16VVpaes6cOTxyCwsLTU1NT0/PCRMmbNmyxcXFZeHChaamplpaWh8HaWvD06dN585Z\nNjZWTp3aR1ddu5lhw4YRiUSBfxmvXbtmIyJ6nS65RSAQHFBih+jbvH792sTEhF9OJpMNDAxe\nv34tVPPMGfD1hfBw8PfvOvMQvZDCwkIjIyP+RnQLFy589+7d8OHDGxoaCgoK6uvrBw0alJ6e\n/ssvv3wcJCYGpqbUadN+UVUddO0aTJrUR1dduxM6nT537tyVK1cWFxdzy8PDw6OiolaKKF3q\nXMktAoHgBi3FIvo2kpKSwjqd1tXVCW2jeuUKrF4NcXEwdGgXGofolQh8Zh49enTq1KmJEydK\nSkqyawUoFIqmpqajo+OOHTvmzp2r/ewZbNoEaWlAIABAbWMjAABqFt059uzZM2nSJHNz85kz\nZ1pYWLx//z4hIeH69ev79u2zs7MTqvb+fTfaiED0E9AbO0Tfxs7O7tq1a/zFjC9fvszNzRX8\nO4PBgKAgCAkBBYX29Z3iYmCxusNcRC/Azs4uPT29qKiIWxgZGeno6JiWlsb9zNjZ2eXl5Wlp\naV25cgVsbKCwEFasgPz8jH/+Ca6paR4+HBQVBdwALSDyISkpGRcXt3PnzsLCwt9+++3s2bNK\nSkpJSUnLli3radMQiH5HTx9W2wc4fPgwADAYjJ42BCGA8vJyWVnZVatWsQ8pZ1NdXW1nZzdm\nzBjBOleu4AC8n4qKbrIY0dOwWCx7e3tnZ+fa2lqOcPr06aampkpKSjU1NRxhQUEBlUo1MDBY\nu3YtjuN4UhLu6MgSE6shEpPU1fGiovZxISG4o+PHG9DpvE8XZ+TnkpSE6+l9MvlAoNu8Hpjh\nRXwLmpubASAxMbGnDREAWopF9G0UFRUvXrw4derUhISE8ePHq6mp5eTknD9/XklJ6dKlS4J1\nJk4EHO9eMxG9CAzDzp075+bmZmJi4uvra2BgwD7AtKGhIT4+XkZGhjNSW1v77Nmz3t7eZ8+e\nFRcXV1JSem5mdj4729DQ8Nq1a8A18hOELyCmpaU9efKkpqbG2Nh4xIgRMsJmYNNfSm5zcnKS\nk5Pfvn2rr6/v6OiooqIiavSXel1WVpaYmJiXl6empmZtbW1kZNSBQn8JLwLBA1qKRfR5Ro8e\nnZmZOWHChJSUlEOHDr19+/ann356/PhxB78/EAMYDQ2N1NTUtWvXFhYWHjp06MmTJ6NGjaLR\naObm5jwjraysCASCg4PDgwcPQkNDKysrf//997t377bnZJ1edS0uLh49erSVldX27dvPnz8f\nEBCgqakZFhYmysq+X3JbW1vr5+dnYmISHBwcFRUVFBSkra39888/4yL+ZfX5XrNYrE2bNmlp\naQUFBUVFRW3YsMHExGT69OnoIFrEAKWnXxn2AdBSLALR7/nw4YOOjo6Pj09jYyNHWFNT4+Tk\nZGNjw73Q/wmdW3VtaGgwMjIaMWJEXl4eW8JkMg8ePEgmk//+++8OLONZ5+07sFisMWPGGBsb\np6SkcCQRERF0On3jxo0dKH+O18HBwTIyMhcuXGCxWGxJcnKykZGRq6srRyJ01bXPhhfRs6Cl\nWAQCgejVUKnUmJiYCRMmGBsbT5w4UUtLKy8vLzo6WkFB4caNG/y9UdrpXNnmX3/91dDQkJyc\nLCUlxZaQyeQlS5Y0NjauXbs2ICBATEzsWznSe4iOjk5KSsrKyuI0AsQwzMfHh0wm+/r6Ll68\nWE1N7ctmLi8vT0hIyMrKkpeXV1NT27Vr14ULFzw9PTkDrKysrl+/bmpqGhMT4+XlhVZdEQMK\ntBSLQCAQAABDhgzJzMxcsWJFRUXFpUuX6uvrt2zZkpKSoi7oVPvP4vLly9999x0nq+MQFBT0\n/v37pKSkr5y/d3L58mV2iswj9/T0VFJSunHjxpdNu2/fPm1t7WXLliUkJBw9etTX1xfDsMGD\nB/MM09bWnjBhwpUrVwDQqitiYIHe2CEQiF7Go0cwYwaoqMD9+99sZOeQlpb+/vvvv8lU3JSW\nluoIOo9YSkpKUVGxpKREsFp+PqxaBf/+C21tMHs27N0LcnLf3Lauo7S01MzMjF+OYZiOjo5Q\nr0USFhb2448/Hjp0aPbs2ezXqOvWrTt69Kirq2t6erq0tDT3YF1d3efPnwMA/Hd2HAIxEECJ\nHQKB6EJYLFZcXFxaWlplZaWxsfHYsWM1NTUFjqytrY2NjaWePu384EGjgYEyi9XBgkLfWV+T\nkZGprKxsa2tjh6KqqsrY2NjNzU1FRaWmpkZwbSyOg4cHGBnBwoWQkABpabByJZw+3e22fzls\nrwVeqqio6KAiWBBMJjM4OPj333+fO3cuR6ihoaGsrNzc3HzgwIGNGzfy3EVWVvZz74JA9HXQ\nUiwCgegqXr58OXz48MmTJ1+6dOnFixdbt27V19ffsmUL/8iIiAhtbe2VK1e+Kiyca2Z2PDMz\nNTU1Li5O1Ox9Z33N2dk5PDx82LBhU6ZMiY6Ozs7ODgkJ0dPTmzVrFo7j9vb2AnTevQNNTfj5\nZxATAzExmD0b4uP7VqNjZ2fnq1ev1tfX88jT09NzcnKcnZ0FqwkvNE5KSqqtrQ389PWbs7Nz\nTk6Om5tbbGwst7y+vj42NtZJxDHQqI80or/NYrWsAAAgAElEQVTS09UbfQBUFYtAfAG1tbVa\nWlrjx49/9+4dRxgZGSkpKblr1y7ukbdv3yaRSDt27GAymWxJy+bNBWpqVCo1PT29g9v0harG\n7OxsAoGgra1dUlLCEf7+++8Yhglto41/00bHPQG70NjT07Ouro4jzM/PNzY2njp1qlA14V6f\nO3dOSUmJX2PKlCmqqqqampocSV1dnYeHh66u7ocPHz6O67o+0oiBB6qKRSAQA46DBw9iGHbx\n4kUqlcoRTpkyZf/+/StXrly8eDHnJN/g4OB58+atX7+eM4xEImlra4+1tNy8ebPQRtN9h4iI\nCFVV1YaGBjs7u1GjRsnLy2dkZCQkJNjZ2aWkpDQ2NnKH6COcktv0dHByguho+Ooyju6ESqXG\nxsZ6enrq6uqOGTNGTU0tNzc3Li5u1KhRf//9t1A14YXGcnJy79+/b25uplAo3PITJ05YWlq+\nevXKw8PDwMDg7du3t2/flpWVjY2NFRzYjm6EQPRp0FIsAoHoEm7cuBEQEMD/mzUgIKC1tTUx\nMZH9Y01NzaNHjwIFbW8PDAy8efMm3vePCblx48a8efNyc3PXrVsnLi5eWFhoa2t7586duLi4\npqamhw8filKOj4exY+HQIRCxqthbMTY2Tk9P37Vrl7y8fF5enomJyYULF27cuMFT5dBJ7O3t\nSSTShQsXeOSSkpKSkpJTp041NjbOy8uTl5ffvXv3s2fPPh4+gVZdEQMJ9MYOgegc7BLFe/dA\nTAzc3ftciWJXITws5eXlAhuFiIuLKyoqlpeXs3+sqKjAcVzgSA0NjQ8fPjAYjC/LA3oP7FDI\nyMgsXbq0XfRfPa+8vDwnFAI4cwZWrIAzZ8DNrXtM7YDPL0OmUqlz5syZM2fO199cUlJy3bp1\ny5cv19TUHDlyJFvY1NS0bNmyN2/eXL9+XehhM6amUFvb/mcNDQCAoqK+9foTgeg8KLFDDFBY\nLNa1a9cSExPfvHmjra09cuRINzc3DMMEj+aUKCYlQX09zJ3b50oUO0leXl5UVFRWVhaVSjU3\nN582bZqoukKRYZGXly8tLeVXYjKZlZWVCgoKnGEAUFpaOmjQIJ6RJSUl4uLi/O3fehU4jt+6\ndevu3bv5+fkaGhr29vaTJk3iaWisoKDwSSj+q+dlVVZWV1dzQsHLlSuwejXExcHQoV1heW1t\nbURERHp6en19vYmJiaenp7GxsSiFXlCGvGnTpsrKSicnJ3V1dfaCbHl5OY1Gi42NFXWE4M6d\nHy3/Rp1xEIheC1qKRQxEysvLR44c6ePjk5KSQqVSk5KSPD09XVxcqoX9xnr3DgwN4fBhMDIC\nS0tYtQru3u1ek7uD3377zcTE5OTJk62trRUVFVu3btXT0+MpNvwEkWEZO3bsuXPnmEwmjxJ7\nKc3R0ZH9o7y8vKWl5WlOlsy1ahYbFubn6IixWILv3gvW12pra93d3SdMmHD//n0qlfrkyRN/\nf397e3ueJm2urq5nz55tbW1t//m/et7KykoSiSS4KpbBgKAgCAkBBYV274qLQVgoPp/bt28b\nGBj8/PPPpaWlOI6fOXNm8ODBmzdvFqXTO8qQ2Ukz+6ww9n8xDBN6NAib3mE5AtFN9GTlRh8B\nVcX2M9ra2uzt7W1sbIqLiznCgoICc3PzsWPHdmqKkBDcyamLzOsp/v77bwqFEhERwZG0tLQE\nBweLi4tnZmZ2aopPw1JdXa2qqurj41NbW8sRxsXFycjIbNmyhVsvNjaWRCIdPHiwra3tM2oV\ne0FV46RJk0xNTTknwOI4Xlpa6uDgYGlp2drayhFWVlYqKyv7+flx14e+nD37IZG4bds2wVNf\nucLrHQBeUfFNzH758qWkpOTq1as5Zcg4jl++fFlCQuLgwYOiNJOScDk5XFr6m5jxBWzZskVG\nRiYhIYEj+fDhw9y5c+Xk5MrKyjpQ7gsF1Ii+Qm+uikWJXcegxK6fceXKFSqVyp3VscnLyyOR\nSHfu3OlA/+lTnE7HuX619ANYLJampqbAJGP8+PH+/v4dTyEoLM+ePTMwMKDT6WPHjg0ICDA3\nN8cw7Pvvv29ra+PRPn78uISEhJaWlre3t5eX16BBg2RkZKKior7Cpy4nKSmJQCBkZ2fzyMvK\nymg02sWLF7mFT58+1dPTk5GRcXNz8/f3NzMz24RhBWpqH0+p70bmz5/v5ORUXl6+d+/euXPn\nent7b9q0KTU19Y8//lBSUuJOST8hNBTX0sKNjXsqsauvr5eQkDh9+jSPvLW11dzc/Mcff+xA\nHyV2iG9Hb07s0FIsYsBx+/ZtJycn/gPI9fT0bG1tb926JUq5L5coiuDly5dv3ryZMWMG/6WA\ngIDbt293oC8kLGZmZpmZmceOHbOysqLRaHPmzHn+/PmePXv4F87mzp2bn5+/YcMGVVVVXV3d\nkJCQ/Px8Ly+vr3Ora7l9+/bw4cP596UpKyu7uLjwPEgWFhbPnz8PCwuztLSUkpIKDAxcvny5\ntra20G2dXcmtW7fMzMwMDAz+/PNPFouloqISHx9vbW2dk5NTXl6ekZEhWI29oMn3F6fbePjw\nYWtrq4+PD4+cSCT6+fl1/JQiEAMDVDyBGHBUV1cL22etoqIidJsd9L4SxW8H22tlZWX+Syoq\nKlVVVaKURYZFTEzM29vb29u7QxuUlZUXLlzYWYt7AZ/7IFEoFF9fX19f3/aft27t+B7f+jBc\nNpWVlWFhYevWrduyZQsnyY6Pj/fy8iIQCEK/7p4+cbW6ulpGRkZcXJz/UsdPKQIxYEBv7BAD\nDmVl5cLCQoGX3rx5I7S2jlOi2O+yOgBge/3mzRv+S4WFhaLqDft1WESjrKwsMGIg5EGqqKjY\nvXu3v7+/u7v7qlWr8vLyOrhBWBj4+YGp6TexlhsCgcB+Lcr96nT06NEbNmxgsVhCq3R7Gna6\nzH9GGXT4lCIQAwmU2CEGHOwaxuzsbB75kydPUlNTx48fL0Cni0sUexxtbW1TU9OwsDAeOYvF\nOnbs2IQJEwSr9fewiGb8+PEZGRn87YVfvXp1+/ZtngcpLi7O2Ng4LCxMWlra2dCw9vnz6NOn\nX2VntxQUCK3n7bJaThzHGxoaPlbp/kdNTQ0A8MvbYZchNzcDi9UjZci2trbS0tLHjx/nkTc2\nNoaHhwv+m8umFxRQIxDdR09v8usDoOKJ/oenp6eenl5KSgpH8uDBA01NzRkzZghW6MoSxV7C\nlStXSCTSrl27OJWSNTU1M2fOlJOTKywsFKbT78MimsDAwEGDBt29e5cjefr0qZGR0bhx47iH\nFRYW0mi0H374ob0u4bPqebtgy7+0tDSdTvfx8amsrGRLWlpaDhw4QCKRCATCrVu3cBzHk5Jw\nPb3edrjq4cOHKRTKyZMnOfU3JSUl48aN09LS4i6+5qUXWI7oZ/Tm4gmU2HUMSuz6H/X19dOn\nT8cwzMDAYOzYsXp6ehiGzZkzp7GxsadN60n++ecfOp0uKyvr5ORkZWVFpVL19fW5018ED83N\nzQsWLCAQCDo6OmPHjjU0NMQwzNvbmyfJWL16tbW1NX8BbHR0NJlMrq6uFnWPb5HYlZWVhYSE\nTJkyxd7efu7cuWpqamvXrjUxMREXF7e0tHR2dpaXl6fRaDt37gSA7Ozs9gJYDw8Bt+7p2tL/\n/e9/4uLiSkpKo0ePHjp0KJlMtrS0fPnyZQ+ahBiA9ObEDsP7/jmMXU1oaOiiRYsYDAaNRutp\nWxDfkszMzIcPH75580ZLS2vEiBEdtN0fGNTW1v7777/Z2dni4uLm5uYuLi4kEiqx6oCXL1/e\nu3evoKBAQ0PDzs7O3NycZ4C1tbW3t/f69et55K2trdLS0hcvXhS1jLh1K1y//jXFEwkJCd7e\n3srKym5ubkpKSi9evDh37hyJRMrPz8/IyHj69GlDQ4OpqamLi8vOnTsvXryYm5uL/f03eHrC\n4cOf3LqkBFgs2L8fEhIgMhIAQFUViMQvNuyLeffuXXx8fHZ2tqys7LBhw0aNGtUjxcWIgQyT\nyaRQKImJiQ4ODj1tCy/of9mIgcuQIUOGDBnS01b0LtgrdD1tRS9GUJmqgYGBgYGBCKXa2lr2\nyWk8kEgkOp1eyznGtAt49+7d5MmT58yZs2fPHuJ/SdiaNWusrKyGDRuWkpLi6uoKAM3NzXv3\n7t2zZ09kZCSGYYILYHvNiavKysrTp0/v/vsiEH0ClNghEIiByIcPH44ePXrnzp3c3Fx1dXVb\nW9slS5YoKSmJ0vnSw1LV1dVfvXrFL6+tra2srFTvytzo0KFDampq3FkdAFhYWJw9e9bX11dT\nU1NfX19KSio7O5tCoYSHh3t4eAid6/37rrMTgUB8K1Bih0AgBhzFxcVubm41NTW+vr7Ozs7F\nxcUREREHDx6MiYkRfHIrG3aZKnuB8nPw8vL6/fff169fLyMjwy0/ePCgvLy8ra2tYDX20ien\nlhO+ZOkzMTHR09OTyKfl4+Ojqqq6YMGCQYMGMRiMwYMHjxw5Eu02QSD6ASixQyAQAwscx/38\n/JSUlB4+fEin09nCbdu2LVmyZMqUKTk5ORwhL1/WoffRoxX794+qrXVzc/v7778HDx4MAM3N\nzQcOHNi8efOpU6fIZLJgxW+x9FlfX8+TTXKQlZVVUVHpW02hEQhEh6A+dggEYmCRmJj46NGj\nU6dOcSdwJBLpwIEDYmJip06dEqbIzsbCw8NTU1MdHByWL1+ek5PTwc3CwsDPDxs8eMiQIYqK\nikOGDFFVVR0yZIi0tPSOHTuOHz/u7+8vVPf9e8DxTz6fv2iroaGRm5vLL29qaiosLNRg54sI\nBKIfgRI7BAIxsEhKSrKwsNDU1OSRUygUNzc3/obDbKqqquzt7bdu3SovL6+uru7h4ZGRkTF0\n6NCIiAhRN/uvyTCZTL569Wp2dvYff/yxaNGi2NjYN2/efPfdd9/KKWFMnTr1/Pnz/CdkHD58\nWExMbPTo0YLVUEdfBKLPgpZiEQjEwKKhoUFKSkrgJWlp6YqKCoGX5s2bBwBZWVnyhw7B9esb\nNmzYsGHDzp07v/vuu+HDh+vp6Qm+Gffq7aNHxjNmGH/rg19FM23atLCwMBcXlyNHjowaNYpA\nIDQ0NBw6dCg4ODg0NFRSUlKwWq8pgEUgEJ8LSuwQCMTAQktLKzc3l8VicZ+UyiY7O9vQ0JBf\n5dWrV9HR0SkpKZyuJbdu3QoNDU1PT8dx3MXFJTg4eN68efw1Ch8pKwM/vy+oqP1KCARCdHT0\nypUrXV1dxcXFFRUVi4qK5OTkjhw5Mnv2bKFqqAAWgeizoKVYBAIxsJg4cWJtbe3Jkyd55Kmp\nqf/++6/ANn6PHz9WVla2VFVlL1C+LSiY7+6u2tb2w+rVHh4eLS0t69evHzduXGNjo9C7YlgX\nHfzaIVJSUsePHy8qKoqIiNi8efO9e/cKCwtFZXW9k0ePQF8fRozoaTsQiN4OemOHQCAGFoqK\nitu3b1+8eHFNTc28efPodHpTU1NMTMzy5ctnzpw5cuRIfpXGxkZJSUnOAqUaQAEAREbCvn2M\nDx8KCgouXbo0atSo4ODgvXv3Cr6rsjIoKHSlWx2gqqqqqqragwZ8FV/aQRCBGICgN3YIBGLA\nsXLlyj///PO3336TkZFRUVGh0Whz5syZP3/+kSNHBI7X1dUtLi6uLSwEHHcbOzZowQJOmerz\n5891dHQ0NTV37tx55MgRUS/tEP+RkJAwZcoUHR0daWlpW1vbkJCQhoYGUQr/1aB0l4EIRB8G\nJXaIAU9+Pnh6gqwsKCvD7Nn955XA1/h15w6oqACBAEQi6OuDoFMTuptvvRI3f/78oqKi1NTU\n/fv3px0+XK+isu3OHWEt5RwdHRUVFXfu3AkAqamp48aNY8vz8vJenTlz4v59GDHC3dAwsaGB\nKiEBBMLHgPOUl9bVQVcfz93rlyx37tzp6uoqJSW1efPm8PBwT0/Po0eP2tjYCCtbAQAIDOzZ\n950IRB8CJXaI/klbJ7sz4Dh4eACJBElJEBsLaWmwcmUXm/ZVfKVfTCazY10WC8aNAyIRYmPh\n5El4+xbGjv06q4XSWXfCwsDPD0xNv+3MFApl+PDh096/N9u6lfDpqcEcdfYbODKZfOjQoZ07\nd37//fdNTU1UKpXBYERGRh61sTmPYTQbGwCQdnY2B2il00FC4uODZGoKGhqwaxckJ7f/oaWl\n8158ljsAXxKobubBgwcbNmw4f/78qVOn5syZ4+npuXHjxoyMDCqVilolIxDfBJTYIfoVdXV1\n69evNzMzo1KpCgoKrq6uly9fFqXw7h0YGsLhw2BkBJaWsGoV3L3bXcZ+BqWlpUuXLjU0NKRQ\nKKqqqp6enomJiaIUPvXrgrp68ZkzBAKBQqFQqVRHR8fS0lKhupmZICcH//4L7u4wcyb4+0NR\n0Tf0Bcfx06dPjxw5UkZGhkajDR8+fPv27U1NTaJ0Or0S19zc/Ntvv1laWtJoNDqdPmLEiJMn\nT+KiX5JxTZ6enj5t2jQtLS0ymSwmJkYgECQkJDAMI5PJf/7559WrV69evfrhw4cpU6bQ6fQZ\nM2YMt7Ghv3wJNjbAZDKp1BEYxjI3ByLx44PE02Q4JATExDofqwcPHnh5eQ0aNIhCoRgaGi5e\nvLikpOSbBKqn+Ouvv7y8vKZOncotlJaWPnDgQFRU1Nu3b3vKMASi34ASO0T/obS01MrKKjIy\nct68edevXw8NDTUyMvL29t64caNQHRUVuHQJOEe/l5SAjk73WNt5srKyhg4d+ujRo++//z4u\nLm7v3r1SUlJOTk5hYWFCdbj8srGxSb927Z2ExJo1a3bv3u3m5pacnKytrZ2VlSVY19wcSko+\nvvV58wa+3RGiOI7PnTt38eLF9vb2p06diomJmTZt2sGDB52cnBgMhlC1zq3E1dfXOzs779+/\n39vbOyYm5vTp0yNGjFi6dOmsWbNYLFaHk1dVVdnY2DQ3N7NfHbW0tFCpVDKZ7OzsLC8vf/Pm\nTT8/v8zMzLVr19JotBs3blRWVk67fp2ipgYAICY21dJSwsVFjJ238T9In9/y99ixY05OTpKS\nknv27Ll169aaNWtSUlIsLCwyMzO/MlA9SFpamqurK7/czs5OQkLi6dOn3W8SAtHfwBEdcfjw\nYQBgMBg9bQiiAzw9PW1tbevr67mFN2/eJBKJcXFxHes/fYrT6XhCQlfZ90W0tbVZWFhMmTKl\npaWFW3706FEymfzixQvR6r/++qsFQJO4OLdfJSUlEhIS6urqHd/+/Hkcw/B9+77IdgGcOHFC\nUlLyyZMn3MLy8nIDA4OlS5d2oBwSgjs6iri+YsUKPT29srIybuHTp0+lpKSOHj0qeu769esf\nEokhISE4jtPpdAzDUlJSWCzWwoULNTQ0Pnz4sGnTJgAYN27chw8frK2tjYyMYmJiqqurGxsb\n3wQFPZeVlZWVzcrKwl1ccElJAQ8SnY4DfPIpKhJhz8uXL8XExEJDQ7mFLS0tPj4+ZmZmra2t\nopzpKFA9iL6+/pEjRwRekpWVjYyMFKXci/1CDDSam5sBIDExsacNEQBK7DoGJXZ9guLiYgzD\nkpKS+C8FBAT4+Ph0oH/7Nq6oiJ850yXGfQX3798nEoklJSX8l+zt7deuXStafaqsbBWRyO/X\n/v37AeD58+eilP/3P5xAwJct+0yTRWFra7tu3Tp+eUREhKSk5IcPH0Qpi/y93tTUJCUl9X//\n93/8l4KDg62srEQbljh+fIq4eGtrK/tk1enTp7Pl9fX10tLSEREROI4rKiqSSCQcx2traxcu\nXMh+OUcgEH4CyJSRaU+yhw/HMezrH6T169dbW1vzy8vKykgkUoLof3704gRowoQJS5Ys4ZcX\nFBQAQEZGhmC1t2/xoiJ87Vrc2hovKsKLinDRqS0C0cX05sQOLcUi+gnPnj0TFxe3EbS7yMnJ\nKT09XZTymTPg6wvh4SDiRPYeIj093cjISGAHMmdn5w79CqupOeXmxu8Xe7UxNjZWqO6yZbBm\nDWzdCgcOfIHZwnj27JmzszO/3NnZuaGhIS8v74tnzs/PZzAYAid3cnJ69uwZLnKnXVlZmbS0\nNJFIPHv2LABs27aNLZeUlLS2tmbHecSIEa2trQAgLS19+PDhurq6tLS0+/fvb9y4cfDgwUZG\nRnDmDGRkAJX69Q9Senq6QF+UlZVNTEyePXv2lfP3FDNnzjx58iQ7e+aA4/jGjRuHDh065NMS\nlo/w1KBoaICITaIIxMAGJXaIfkJrayuJRMIwjP8SmUxm/z4WzJUrsHo1xMWBm1sX2veltLW1\nkUiCG4mTSKQO/XIjEHK1tQXqAkCLsArNzZvh0CE4exY2bPh8k4WC47gwd9h9Rjpb+ykIdiiE\nTc5isUQndiwWi/3wsMs4JCQkOJc4caZQKNwqFApl6NCh9vb24uLiAP89SMOGgZDv67P48u+9\ndzN9+nRXV9eRI0ceP3789evXDAYjMTFx6tSpMTExR48eFarGU4OC4+jgWgRCGCixQ/QTjI2N\nGQxGTk4O/6Xk5GRjY2PBagwGBAVBSAgoKLRvaS8uBhEb7bsdIyOj3Nzcuro6/kud8YspLV1w\n7x6/X5GRkQDg5OQkQLe0FLZuhYAA0NWF5OT2z7fIJDAMMzIySklJEegLmUzW1dUVrNmJygMd\nHR0KhSJscgMDA/6TYbknV5OSamYwoLjYz9GRCLBr1y72xdbW1rS0NHacHz58yDsJx7DGRpg9\nG6ZNg+ZmaG2Fq1fh6lUQXeorEmNjY4G+1NfXv3jxQuj3/vklGt0MhmERERFLlixZu3Ytu0Hx\nqFGj6urqHj58aGlp2dPWIRD9gp5dCe4ToD12fQUHBwcvL6+2tjZuYWZmpoSEhMCtVziO41eu\n8G5pB8ArKrrD3M7R3NyspaW1YsUKHnlcXByBQBC4pxDHO/CrublZUVFRRkZGsO7mzQJ0c3K+\niTu7du1SUlIqLi7mFjY1NdnZ2U2bNk2oWucqDwICAmxsbBobG7mFJSUlKioqv/32W+cn1xUT\nI5FIVVVVOI7v2LFDVla2urr64sWLADBs2LAODOP+3LzZmZgIJDk5mUAg3Lhxg0e+evVqdXX1\npqamTvoiukSjB2GxWAUFBampqTzVTghEn6A377FDiV3HoMSur5CZmSkrK+vi4nLt2rW3b99m\nZmbu379fTk7O19eXvRLXR7l16xaFQvHz84uPjy8rK3vy5ElISAiVSu2wcgLH8ZaWFlVVVQKB\n4OfnFxMTk5KS8ssvv8jIyBAIhGvXrnWD8Tw0NTU5OTmpq6sfO3YsJyensLDw0qVLVlZWmpqa\nPNneF1BSUqKtrW1paRkZGfn69evc3Nzjx49ramqOGDGCJ9sTyN69e0kk0rp163777TcAIBAI\nenp6RCIxKCjIzs4OAMhk8vv377/SyM6zYcMGcXHxX3/9NTU1taysLCEhwd/fX0xM7N9//+02\nGxAIhEBQYte3QYldHyIvL2/y5MmcvVBqamo7d+7soDdEXyA1NXX06NGcA68MDAyOHz/eSd3G\nxkY3Nzcikch5T6+mpnbv3r0uNVi0PcHBwQr/tVuTkJD47rvveHqUfDHv3r2bPXu2pKQke3J5\nefn169d3UGzLRWRkpLm5ucCdmlpaWux3eN3JiRMnjIyM2AaQSCRnZ+fk5ORutgGBQPDTmxM7\nDO/qgwv7PqGhoYsWLWIwGLRv16YV0aW0tv5/e/cdF8W193H8LF0EQQRFZFEDNkAUxRYUe8Ru\nbNhF9Iq9JFHRmKtYk2ia0ViSGCsqWBJjkkuCAlYQ1KtijAU0goCdpiBtnz82D5fAgiXsLjt8\n3i//gDNzdn9shsmXmTnn5CckJFhZWVlX7slaX1Vubm5CQoKtra2lpeVrdP/jjz8SExM7d+5s\n9CqLH6hPcnJydnZ2w4YNy3z67XUpFIpbt26ZmJjY2dm9RvesrKzExERlYSEhIXXr1u3WrVvF\nVvhK0tPTk5OTHR0dX/8/XHS0GD1a2NqKkycrtDSgisrNzTU2Nj516tSbb76p7VpKIti9GMEO\nQGWTlJSUnp7eqFGjF6e9LVvEqlXCzU08fkywAypEZQ52jIoFAJ2Rl5e3fPlyGxsbuVzu6upq\nZmbWr1+/GzdulNen0i8gC6ACEewAVDLR0cLJSXTsqO06Kp3CwsKhQ4euX79+5cqVN27cuH//\n/i+//JKXl9e2bVudXkAWf+HIR0Ug2AFQr8LCwvj4+KioqLS0tBfvvWWL8PERzs7qr0v37Nq1\n69ixYydPnpw8ebKTk5ONjY1yDHiXLl2US4mgUklNTT19+vTdu3dfam+OfFQQgh0AdcnLywsM\nDLSysnJycurQoUPNmjU9PT1Vzrv7P9w3LNvOnTsnTpzYqFGj4o16enqrVq06ffp0fHy8tgpD\nCSEhIU5OTnXr1vX09LS3t2/QoMGuXbte0IcjHxWEYAdALRQKxYgRI7766qvPPvvszp072dnZ\nMTExDRo06NSp08lyHuHnvmHZrl+/rnJ5hmbNmpmamqpcdgWat2HDhlGjRo0cOfLq1as5OTnX\nr1+fOHHipEmTPv744/K6ceSjglTAmoYAUNqBAwd++eWXc+fONWvWTNni4eGxe/fuyZMnT5o0\n6ffff6/wWU4kz8DAQOXyvoWFhfn5+UXTHEKLkpOT582bt3nzZj8/P2VLo0aNPvjgg8aNG48d\nO3bo0KFlrpsHVBBOrADUYvfu3aNGjSpKdUWWLVt248aNc+fOaaUqnebu7n7s2LHS7adOncrP\nz3dzc1PdrdIvICslBw8erFu37oQJE0q0+/j4NGnSJDg4WCtVoUoh2AFQi5s3b7Zs2bJ0u62t\nra2t7c2bNzVfkq6bOnXq3r17Q0NDizdmZGTMmTNnyJAhderUUd3N2VnI5WLNGhETI+RyIZeL\nlBRNlFsl3bx5s0WLFioXL2nZsiWHPTSAW7EA1MLIyEi56k5pOTk5lWT1C93SvXv3RYsW9evX\nb8KECd26dbOwsLh06dLGjRurV6++Ybg/Y7MAACAASURBVMOGMru9zGBkVBAjI6OcnByVm3Jy\ncszNzTVcD6ogrtgBUIvWrVuHhYWVbj937tzjx49btWqluhv3Dcu1bNmyw4cPJyYmzp07d8iQ\nIcHBwb6+vmfPnrWxsdF2aRBCiNatW585c+bp06cl2p8/f37ixAmVY1/+wpGPCsKSYi/GkmLA\na/jvf//r4eHx3XffjR07tqgxIyOjZ8+etWvX/vHHH1V3s7QU6el/a0lMFPb26qy0qtL8ArJV\nYMna7Ozspk2b9ujRY8uWLfr6+spGhUIxa9askJCQ69ev16hRQ3VPjnydUpmXFONWLAC1aNmy\n5bp16yZMmHD48OGePXtaW1vHxcV9++231atXP3z4cJnduG/4uhISEi5fvpyfn+/q6tqkSZMX\n7F18AdnXkpube/ny5T/++KNOnTotWrR48SXDf/yOOqFatWohISHe3t6XLl0aOXKko6Pj7du3\ng4OD4+LifvzxxzJTneDIR4XR7Vuxubm5MTEx4eHht27d0nYtAEqaNm3ayZMnDQwM1q5dO3ny\n5NDQ0H/9618xMTFlPuaP1xIfH9+lSxdHR8fx48f7+/s3bdrUw8Pj4sWL5fX5Z9Ph7tu3r0GD\nBh4eHgsWLOjXr5+dnd3kyZOzsrLU9446pG3btpcuXerQocPu3bvHjx+/bdu2Fi1aXLx40cvL\nS9uloUrQmSt2K1as8PT07Nq1a1HL5s2bFy5c+OTJE+W3rVu3/uabb1SOwgOgLe3bt2/fvr22\nq5Cy5ORkLy+v5s2bX7lyxdnZWQiRkJCwcOHCLl26nDlzpmnTpqq7/f8sa69h9+7dvr6+S5Ys\nmTFjhqWlZX5+fkREhL+//9tvvx0aGlrm9IT/4B11jr29/bp167RdBaoonbli98EHHxQf5P/T\nTz9NmTLl2bNnb7/9tr+/v6en57lz57p06cKiOgCqlKVLl9arV+/w4cPO/7/M6BtvvLFnz572\n7dvPnz+/wt8uOzt7zpw5K1euXLx4saWlpRDCwMCgR48ex44di4qKYp42QOt0JtiVMHfuXAsL\niwsXLhw8eHDTpk0nT548cOBARkbGypUrtV0aAGjOgQMHZs+eXWL6GD09vffee+8///lP6eGZ\n/1BkZOTTp09nzJhRor1+/frDhw8/cOBAxb4dgFelk8HuwYMHN27cmD59evFJ7QcPHjxw4MBf\nf/1Vi4VBZyQkiAEDRM2aok4dMX68tJ/mhoRlZmY+fvxY5f3Wpk2b5uXlJScnV+w73rlzx8HB\nwdTUVOU7/vnnn6//0tHRwslJdOz4+q8AQIeesStOOf1j6aWKXF1df/rpJ21UBO27cePG+fPn\n796927hx4/bt21uXs5y2QiH69xdNmoioKJGVJSZMELNni507NVgsUDGqVaumr6+fkZFRelN6\neroQosInaapevXp6iVk5ir1j0dvl5eWdOXPmypUr+vr6rq6u7du3f8HSwFVjzCygATp5xc7O\nzs7CwiIpKalEe3JyMvN6V0Hp6ekjRoxo0qTJnDlzdu3aNWrUKAcHh9WrV5c5R+O9e6JxY7Fp\nk2jSRLRuLebMEcePa7ZkoGIYGBi0bdv2hx9+KL3p8OHDDRs2rFu3ruqerzsd7ptvvpmamnr2\n7NkS7YWFhUeOHFHO6XX8+PEmTZp07959/fr1n3zyiZeXl6ur6+XQ0PLescqMmQXUTqEjhBAj\nR46MiYm5cePGgwcPFi5c6OTk9PTp06Idrl69Wr169f79+1f4W2/atEkIkZmZWeGvjH+uoKCg\nS5cuTZs2jYmJKWrZuXOnmZnZypUrX+olli9XdO6svgoBtTp06JCRkdHhw4eLNx4/ftzMzGzj\nxo1ldrOwUAjxt3+JiS/5jsOGDXNxcbl7925RS0FBwfz5883NzZOSks6fP1+tWrVp06Y9efJE\nufX+/fujR49Ol8le/I7Llys8PV+yDECLlOslnjp1StuFqKBLwa60/fv3K7fu3r27evXqenp6\nZ8+erfC3JthVZiEhIdWrV79z506J9qCgIBMTk3v37r2g/3//q7CwUEREqKs+QP1WrFihr6/v\n7e0dGBi4cuXKgQMHGhgYzJo1q7CwUB1vl5aW5unpaWFh4efnt3bt2vnz57u5uVlYWISGhioU\nip49ew4dOrREl4KCgm7duo0cOfIFL02wg46ozMFOZ56x++6779KKSU9PT0tLq1mzpnJrWlqa\npaXl3r1727Rpo906oWGHDx8eOHCgXC4v0e7j4zN79uzffvtt9OjRZXYODxc+PmLjRtG5s3qr\nBNTp/fff79Wr17Zt244dO5aXl+fq6hoWFtZZbUe1hYVFREREUFBQaGjo3r17bWxsBg4c6O/v\nX69evaysrGPHjh09erREFz09vWnTpvn6+ioUCplMpqbCAAgdGjzh6+tbztZx48ZNmTLlBQ/n\nqpKQkODs7KyM3uVTsKhupZScnNyhQ4fS7Xp6eg0bNrx7926ZPYOCxKxZIihIvPWWGuurtBIS\nxJw54sQJYWQkvL3FZ58JKytt14TX5+Hh4eHhobG3MzAwGDdu3Lhx40q0p6amFhQUODo6lu7i\n6OiYlZWVkZFhYWGhkRqBKkpngl35lEOxHj169OTJEycnp5fv2LBhw99++638YHfkyJEvvviC\nvzIrJwsLi0ePHqnc9PDhQ+UEqiocOSLmzhVhYUIqS5Wkp6eHhobGxcUZGxu7urp6e3sbGxuX\nuTfjgqEeyt+4R48e2Zdavf7hw4cGBgYVPkoXQAkSCXZKa9as+eijj17p0ppMJuvUqVP5+7Ca\nRWXWuXPnNWvWfPrppyYmJsXbz507l5CQoHpxxsxMMXmyWL5cWFuLorHVdnbi1a/4VhLBwcH+\n/v76+votW7Z8/vz5Rx99ZGlpuXv37jKP7aJxwbVrCyHEnDkiMFCTBUOqrK2tXVxcgoODW7Ro\nUWJTSEiIp6envr6+6p7JyaKw8H9jZoUQdeuKsnYGUDZd/T8ZoDRhwgSFQjF+/Pjs7Oyixtu3\nb48ePXrYsGGqF8o8flykpAh/fyGX/++fzs6epXyOMCAgICUlJSws7MSJE8nJyf369evTp8+1\na9dU97G1FYcO/ZXqhBDJyaJhQ40VDGlbvHjx2rVrDx06VLxx69atW7duXbx4cZndnJ2FXC7W\nrBExMX/9SqakqL1WQIokdcUOVZC5ufnPP/88YMAAR0fH7t27161b9/r166GhoR07dvz2229V\n9+nbV0joicmAgAB/f/8FCxYUtZiZmW3YsCE+Pj4wMDAoKOgF/S9eFGvXClUToQGvYcSIEbdv\n3x42bFibNm3atGmTn58fFRV15cqV9evX9+jRo8xuaWkarBGQMp0Jdi/zXHB5T8pDutzc3OLi\n4nbv3h0bGxsXF9e4ceN9+/b169fvNQbT6Jx79+6dP3++dISVyWR+fn5Tpkx5QX/GBUMNAgIC\n+vfvv2/fPuXKE4MGDQoJCVE5ogJAhdOZYHfhwgUhhKGhYTn75Ofna6ocVC5mZmb+/v7+/v7a\nLkTT7t+/L4QoPduLEMLBwSEtLe358+dljqKo4uOCER0tRo8Wtrbi5MkKf20XF5dly5ZV+MsC\neCGduaQxb9686tWrx8XF5ZTtvffe03aZgEbVqlVLCJGamlp6U0pKirm5eZmprmhcMKlOEh4+\nfLh58+YZM2ZMmTJl/fr1L759sWWL8PERzs4aqQ6A5uhMsFu+fLmTk9PIkSPz8vK0XQtQWdjZ\n2Tk7O+/atav0pl27dnXv3l11txLjgpX/CgvVWyvU5sCBA46OjqtWrbp3715aWtoXX3zh5OT0\n9ddfl9eHtVkBidKZW7GGhoa7d+9u3br1okWL1qxZo+1ygMpi+fLlPj4+jRs39vX1Vc62mJ+f\nv3z58p9++ikqKkp1n6JxwcU9eCCsrdVfLypYVFTUyJEjAwMD58+fr5xMRKFQfP3119OmTbOz\ns+vbt6/qbn5+Gq0SgKboTLATQjRr1iw1NbWcB+l69+5d5oS0gEQNHjz4iy++mDp16urVq1u1\napWXlxcdHf306dP9+/e7u7ur7iOtccFVXGBg4NChQxcuXFjUIpPJJk+e/Mcff3zwwQdlBjsA\nEqUzt2KVatSoYVX2wkedO3cOCAjQZD1AZTBt2rSbN2/OmjXL0tKyXr16S5cuTUhI6N+/v7br\ngtoVFhaGh4ePGTOm9KbRo0dfuHChrHVZAEiVLl2xA1AWe3v7GTNmaLsKaFpWVtbz589tbW1L\nb6pbt64Q4tGjR8oRNgCqCB27YgcAKGJubm5qapqYmFh60507d2QyWe2i9UUAVA0EOwDQVTKZ\nzNvb+5tvvim96ZtvvunQoUOZjx0nJ4ukpP+tzZqUJAoK1FsrAI3gViwA6LDAwMB27drNnj17\n9erVpqamQojc3NwPP/xwx44dYWFhZXZzdhbp6X99rZzgOjFR2NtroGAAakWwAwAd5urqeuTI\nkTFjxmzbts3Nzc3Q0PC///2vTCYLDg728vIqsxtrswISRbADAN3WtWvX+Pj40NDQuLi4vLy8\nadOm9erVy9zcXNt1AdACgh0A6DwTE5OBAwcOHDhQ24UA0DIGTwAAAEgEwQ4AAEAiCHYAAAAS\nQbADAACQCIIdAACARBDsAEhUdLRwchIdO2q7Du3hEwCqHoIdAB0QGxvr6+vr7u7u4ODg7e29\nfv36vLy88jps2SJ8fISzs6YKVK/c3Nz169d7e3s7ODi4u7v7+vrGxsa+oI+0PgEAL4lgB6Cy\n++qrrzp06PDkyRNfX98VK1a4urouW7asU6dO6UWLYpVmYCBiY0XbthosU13S09O9vLyWLVvm\n6uq6YsUKX1/fJ0+edOjQ4auvviqvm4Q+AQAvjwmKAVRqsbGxM2fO3LZt29ixY4sa582b17Vr\n15kzZ+7YsUN1Nz8/DdWnfjNnzszIyLh8+XKdOnWULbNnz96xY4efn1/btm09PDxUd5PQJwDg\n5XHFDkCltm7dur59+xZPdUKIOnXqrF+/Pigo6MGDB9oqTDPu378fFBS0fv36olSnNG7cuD59\n+qxbt05bhQGonAh2ACq1mJgYb2/v0u1dunQxNDQ8f/685kvSpAsXLhgaGnbp0qX0Jm9v7xc/\naQegiiHYAajUcnJyTE1NS7fr6ekZGxvn5ORoviRNysnJMTEx0dNTca42NTXNzs7WfEkAKjOC\nHYBKzdHR8fLly6Xb79y5k56e7ujoqPmSNOmNN95IS0tLTEwsveny5cuS//EBvCqCHYBKbcSI\nEVu3bi2dbAIDA11dXV1dXVV3S04WSUkiI0Pk5oqkJJGUJAoK1F6rGjRv3tzV1TUwMLBEe2Ji\n4tatW0eMGFFmT6l8AgBeCaNiAVRqvr6+QUFBXl5en376adeuXc3MzP744481a9YEBwcfPXq0\nzG7OzqJoMhS5XAghEhOFvb0mKq5omzZt6tGjR15e3rx585o2bZqVlRUeHv7OO+8oJ7Qrs5uE\nPgEAL49gB6BSMzAwOHLkyKJFi0aNGpWTk2NkZJSbm9u6devIyMi25UzSlpamwRrVy9PTMzIy\nctq0ac2bN1f++CYmJv7+/qtWrTIwKPscLqFPAMDLI9gBqOxMTU0///zzDz/88OrVq2lpac2a\nNbO1tdV2URrVtm3b2NjY1NTUq1evWlpaNmvWzMTERNtFAaiMCHYAdIOJiYm7u7u2q9AmW1vb\nqpZoAbwqBk8AAABIBMEOAABAIgh2AAAAEkGwAwAAkAiCHQAAgEQQ7AAAACSCYAcAACARBDsA\nAACJINgBAABIBMEOAABAIgh2AIBXFx0tnJxEx47argPA3xDsAAAiMTExIiLi5s2bhYWFL957\nyxbh4yOcndVfF4BXQ7ADgCrt4MGDjo6ODg4OPXr0aNSoka2t7ZdffqlQKMrrY2AgYmNF27aa\nqhHAyyLYAUDVtXXrVh8fnxEjRty4cSM3NzcxMXHx4sWLFi2aP39+ed38/IS1taZqBPAKDLRd\nAABAOx49ejRnzpzPPvtsxowZyhZ7e/tZs2Y5Ozv36tVr1KhR7u7u2q0QwKviih0AVFFHjhwx\nNTWdOnVqifYePXp4enru27dPK1UB+CcIdgBQRcXHx7u4uOjr65fe1KJFi5s3b2q+JAD/EMEO\nAKooY2PjnJwclZuePXtmYmKi4XoA/HMEOwCootq0aXPu3LmHDx+WaM/Pzz927JiHh0eZPZOT\nRVKSyMgQubkiKUkkJYmCAvXWCuDlMHgCAKqobt26OTk5/etf/9q7d6+xsbGyUaFQLFy4MD09\nfezYsWX2dHYW6el/fS2XCyFEYqKwt1d3wQBeiGAHAFWUgYFBSEhIjx493N3dR48e7eTklJiY\neOjQoUuXLh08eLBWrVpl9kxL02CZAF4BwQ4Aqq5mzZpdunTpk08++eWXX27evCmXy9u2bbtr\n166GDRtquzQAr4NgBwBVWq1atVatWqXtKgBUDAZPAAAASATBDgAAQCIIdgAAABJBsAMAAJAI\ngh0AAIBEEOwAAAAkgmAHAAAgEQQ7AAAAiSDYAQAASATBDgAAQCIIdgAAABJBsAOgBtHRwslJ\ndOyo7ToAoGox0HYBACq7K1euhIaGXrt2rVatWq1bt+7fv7+RkVF5HbZsEatWCTc38fixpmoE\nAAjBFTsA5SgsLJwzZ07z5s137NiRlZUVGxvr5+fXvHnzK1eulNfNwEDExoq2bTVVJgDgL1yx\nA1CmwMDAHTt2HD16tGvXrsqW9PR0Pz+/t95668qVK5aWlqq7+flprkQAQDFcsQOgWnp6+scf\nf7xx48aiVCeEsLCwCAoKqlat2oYNG7RYGwBAJYIdANVOnDihr68/ePDgEu3GxsbDhw8PCwvT\nSlUAgHIQ7ACo9vDhQxsbG0NDw9Kb7OzsHj58qPmSAADlI9gBUK127dr379/Pzc0tvSkxMbF2\n7dqaLwkAUD6CHQDVOnXqJJPJ9u7dW6L92bNne/fu9fb2LrNncrJIShIZGSI3VyQliaQkUVCg\n3loBAEIIRsUCKIu5ufnixYunT59uYWExcOBAZeO9e/fGjx+vr68/derUMns6O4v09L++lsuF\nECIxUdjbq7tgAADBDkCZFixY8OzZs6FDh8rlchcXl/v371+6dKlZs2a//fabmZlZmd3S0jRY\nIwDgf7gVC6BMMpls2bJlN2/eXLRoUZMmTQYPHnz48OHY2FhHR0dtlwYAUIErdgBeoH79+pMm\nTdJ2Ffh/0dFi9GhhaytOntR2KQAqHYIdAGjN5cuXt23bdunSpdzcXGdn5+HDhxefDloF1uEF\nUC5uxQKAdqxbt65Vq1bnz59v27Ztz549k5OT33rrrWnTpikUijL7sA4vgHJxxQ4AtCAsLOzd\nd9/dvn37qFGjihqjoqK8vb0bN248Z84c1d1YhxdAubhiBwBa8PHHH48bN654qhNCtG/fPjAw\n8OOPPy4sLNRWYQB0GsEOALTg1KlTgwYNKt0+cODAlJSUW7duab4kABJAsAMATSsoKMjOzraw\nsCi9ydLSUgiRmZmp8aIASAHBDgA0TV9fv169etevXy+96dq1a3p6enLlih0A8IoIdgCgBYMH\nD/7yyy+fP39eov2TTz7x8vKqVauW6m6swwugXAQ7ANCC999//8mTJ3379r169aqyJTk52c/P\n7+eff/7000/L7ObsLORysWaNiIkRcrmQy0VKioYqBqALCHYAoAW1a9eOjIwsLCx0dna2srKq\nW7duvXr1oqOjjx496u7uXma3tDShUPztn729BqsGUNkxjx0AaEfDhg2PHTsWHx9/8eJF5coT\nrq6uenr8vQ3g9RHsAECbHB0dHR0dtV0FAIngT0MAAACJINgBAABIBMEOAABAIgh2AAAAEkGw\nAwCdFR0tnJxEx47argNAZUGwA4BKJCMj42V33bJF+PgIZ2d1lgNAxxDsAED7zp8/P2DAgFq1\nallYWFhZWfXr1y82NvYFfQwMRGysaNtWIwUC0A0EOwDQsh9//LFDhw6GhoZbtmy5cOHCN998\nY2pq+uabbx48eLC8bn5+wtpaUzUC0A1MUAwA2vTkyRNfX9+AgIDAwEBlS8uWLQcPHrxixYqJ\nEyd6eXlZk94AvDSu2AGANh04cMDY2Hjx4sUl2hcuXGhubh4SEqKVqgDoKIIdAJ0liTGhcXFx\n7dq1MzQ0LNGur6/fvn37y5cva6UqADqKYAegErlz505KSspL7SqVMaEKhUJPT/WpWE9PT6FQ\naLgeADqNYAdA+548eTJ16lRLS8v69evb2dnZ2NjMnz//2bNn5fWRyphQZ2fns2fPFhQUlGgv\nLCyMjo52Lie5JieLpCSRkSFyc0VSkkhKEqVeBEBVQ7ADoGWPHj3q0KHDiRMnNm3adPPmzWvX\nrq1Zs2b//v3dunUrL9tJZUzokCFDMjMz165dW6L9888/f/To0fDhw8vs6ews5HKxZo2IiRFy\nuZDLxUte7AQgXYyKBaBlixYtMjQ0PHPmjJmZmbKlcePGffr08fDw+Oijj4rGikqVtbX1li1b\nRo8e/fvvv48ePbpBgwZ//vnnnj17duzYsX379jp16pTZMy1Ng2UC0A1csQOgTbm5uUFBQUuW\nLClKdUq1a9eeP3/+9u3btVWYJg0fPjwiIiIxMXHgwIFNmjQZMGBAQkLCsWPHRo8ere3SAOgY\nrtgB0Ka7d+9mZWW1VfWoXJs2bf7888/s7Oxq1appvjAN8/T0PHbsWEFBQWpqqq2trb6+vrYr\nAqCTCHYAtEmZYPLz80tvKigokMlkZY0YlSR9ff169eppuwoAOqwKnTEBVEL16tWztraOjIws\nvSkyMrJJkybGxsaqezImFABK4YodAG3S19efOHHi0qVLe/XqZWdnV9R+/fr1tWvX/vvf/y6z\np7OzSE//62u5XAghEhOFvb1aqwWASo5gB0DL/v3vf585c6ZVq1azZ89u06ZNfn7+6dOnv/zy\nyy5dukyfPr3MbowJBYBSCHYAtMzU1DQsLOzzzz/fs2dPYGCgvr6+i4vL6tWrJ0+eXKUesAOA\nf45gB0D7DA0N582bN2/evCo4YAIAKhDBDkAlwjQfAPBP8GcxAACARBDsAAAAJIJgBwAAIBEE\nOwAAAIkg2FViCQliwABRs6aoU0eMHy8eP9Z2QQAAoFJjVKzmFBYWXrhwIS4uTgjh6urq7u5e\n3pwOCoXo3180aSKiokRWlpgwQcyeLXbu1Fy5AABA1xDsNOT8+fPjx4+Pi4tr0KCBEOL27duu\nrq7bt29v1aqV6g737onGjcWmTaJ2bSGEmDNHBAZqrlwAAKCDuBWrCdeuXevWrZubm1tKSsqt\nW7du3bqVkpLi5ubWrVu369evq+5jaysOHfor1QkhkpNFw4YaKxgAAOgigp0mLFq0qF27drt2\n7bK1tVW22Nra7ty5s127dosWLXpx/4sXxdq1XLEDAADlI9ipXV5e3s8//zxr1iyZTFa8XU9P\nb+bMmT/99FNeXl55/cPDRc+eYuNG0bmzegsFAAA6jmCndg8fPszJyWnUqFHpTY0aNcrJyXn4\n8GGZnYOCxLBhYtcuMXKkGksEAACSwOAJtTM3NxdCPFY1Wcnjx49lMlmNGjVU9zxyRMydK8LC\nRMuWaq0QAABIA1fs1M7MzKxVq1YHDx4svengwYPu7u7Vq1dX0S0zU0yeLJYvF9bWIinpr3+F\nhWovFwAA6Cyu2GnCwoULR48e3aFDh7fffruo8dChQ+vWrQsKClLd5/hxkZIi/P3/1vjggbC2\nVmelAABAhxHsNGHo0KE3b94cNmyYp6dnu3bthBDR0dGnTp1asWLFkCFDVPfp21coFBqtEgAA\n6DhuxWpIQEDA+fPnO3TocOXKlStXrnTo0OH8+fMBAQHargsAAEgHV+w0x83Nzc3NTdtVAAAA\nydK9YKdQKG7dupWQkJCZmSmEsLCwaNSokVwu13ZdAAAAWqZLwe7JkycrV67cuXPn/fv3S2xy\ncHCYNGnSe++9V61aNa3UBgAAoHU6E+xSUlI8PT1v3brVqFGjPn361K9fXzlLSEZGRnx8fGRk\n5L///e8DBw6Eh4fXrFlT28UCAABogc4Euw8++CApKSk4OHjYsGGltxYUFGzevHnGjBmBgYGf\nf/655ssDAADQOp0ZFfvTTz+NHTtWZaoTQujr60+bNm348OEq5wEGAACoCnQm2D169MjR0bH8\nfZo1a3bv3j3N1AMAAFDZ6Eyws7Ozu3jxYvn7XLhwwc7OTjP1AAAAVDY6E+wGDRoUEhKydu3a\n58+fl9769OnTJUuW/PDDDz4+PpqvDQAAoDLQmcETS5cuPXHixLx585YtW9a2bVu5XG5mZqZQ\nKLKysv7888+zZ88+e/asU6dOixcv1nalAAAA2qEzwc7S0vLMmTMbNmzYsWNHREREQUFB0SZD\nQ8PWrVv7+fn5+fnp6+trsUgAAAAt0plgJ4QwMjKaO3fu3Llzc3JyEhMTlStP1KhRw8HBwcjI\n6LVf9uLFi/n5+eXscOfOndd+cQAAAI3RpWBXxMTEpFGjRqXbHz169OTJEycnp5d/qfj4eA8P\nj/KDnZKens48jwgAAKomSYWVNWvWqAx85XB0dMzLy1OU69SpU0IIAwOdDMEAAKDqkFSwAwAA\nqMoIdgAAABKhM7cXPTw8XrjP3bt3NVAJAABA5aQzwe7ChQtCCENDw3L2eZkxEK9BOeTW2NhY\nHS8OAAB00T+ZkUN9ZAqFQts1vJSAgICvvvrq/Pnz5Qx6DQgI+Oijj9TxE71wShTdNW3aNBsb\nmxEjRmi7EGjH9u3bMzMzZ8yYoe1CoB1HjhyJjY1dunSptguBdkRFRe3evTssLEzbhegeAwOD\nFi1aaLsKFXTmit3y5ct//fXXkSNHnj59uvzrdupQOf/jVQhra2sXF5cxY8ZouxBox5kzZx4+\nfMgBUGXdvXv39u3bHABVlqGh4f79+1u3bq3tQlBhdGbwhKGh4e7du69cubJo0SJt1wIAAFAZ\n6cwVOyFEs2bNUlNTy7kl2rt3fCOVgwAAEKVJREFUb0tLS02WBAAAUHnoUrATQtSoUaOcrZ07\nd+7cubPGigEAAKhUdOZWLAAAAMpHsAMAAJAIgh0AAIBEEOwAAAAkgmAHAAAgEQQ7AAAAiSDY\nVXVGRkaVc7U7aAYHQBXHAVDFcQBIj86sFQs1efDggYmJibm5ubYLgXakp6fn5+fXqlVL24VA\nO7Kzs9PS0urWravtQqAd+fn5d+/erV+/vrYLQYUh2AEAAEgEt2IBAAAkgmAHAAAgEQQ7AAAA\niSDYAQAASATBDgAAQCIIdgAAABJBsAMAAJAIgh0AAIBEEOwAAAAkgmAHAAAgEQQ7AAAAiSDY\nAQAASATBDgAAQCIIdgAAABJBsAMAAJAIgp305eXlLVy4UF9f38PD44U7b9u2TabKihUrNFAq\nKtyTJ0/ee++9+vXrGxsbN2zYcNCgQVFRUeV3SUtLmzNnToMGDYyMjOzs7CZNmpSSkqKZalHh\nXvUA4AwgMQkJCZMnT3Z0dDQ2NraxsRk0aNDZs2fL78IZQNcZaLsAqNfVq1fHjBlz48aNl9w/\nLS1NCDFy5EgHB4fi7Z6enhVfHNTs8ePHrVu3vn37dt++fcePH5+QkLBv377Q0NCzZ882b95c\nZZfc3Nzu3bufP39+yJAhrVq1io+P37Fjx7Fjx86dO1ezZk0N149/6DUOAM4AUnLt2jVPT8/M\nzMzhw4c7OjrevHkzODj4559/joyM7NChg8ounAGkQAHpSk9Pr1atmoeHx40bN4yNjVu3bv3C\nLkuWLBFCxMTEaKA8qNv06dOFEF9++WVRy4EDB4QQffr0KavLp59+KoT46KOPilr27dsnhHj3\n3XfVWyvU4DUOAM4AUtKzZ0+ZTBYZGVnUcvDgQSHE8OHDy+rCGUACuBUrZfn5+dOmTTt9+rST\nk9NLdlH+vW5paanOuqAhhoaG3bt39/f3L2p5++23q1WrduXKlbK67Nixw9zcfPbs2UUtw4cP\nd3Jy2rlzp0KhUG+5qGivcQBwBpCSdu3aBQQEeHl5FbUMGDDA0NDw2rVrZXXhDCAB3IqVMisr\nq7Vr175Sl6LTekFBQUpKiomJibW1tXqqg9p99tlnJVpyc3Pz8/Pt7e1V7p+Tk3P58uUuXboY\nGxsXb+/YseO2bdtu3br1xhtvqKtWqMGrHgCCM4C0LF++vERLampqXl5ew4YNVe7PGUAauGKH\nv0lPTxdCfP755zY2NnK53MbGpkmTJkFBQdquCxVj8+bNeXl5I0aMULk1MTGxoKBALpeXaK9f\nv74QIiEhQe31Qc3KPwAEZwDpevbsWURERJ8+fczNzd9//32V+3AGkAau2OFvlH+v79mzZ/78\n+fXq1bt69eqGDRtGjx6dmZlZ/IYOdFFkZOS8efM6duw4ZcoUlTtkZmYKIapXr16i3czMrGgr\ndNcLDwDBGUCiLC0tlZF9zJgxhw4dKuvCG2cAaSDY4W8++OCDGTNmeHt7F/1ujxkzplWrVosW\nLZowYYKRkZF2y8Nr27Nnz4QJE1xdXX/44QcDg/J+8WUyWYkW5bM1pduhQ17yAOAMIElTp059\n/PhxXFxcUFDQ7du3t2/fXs5NVc4Auo5bsfibbt26DRkypPhfbM7Ozn369Hn8+PHFixe1WBhe\nm0KhWLJkyahRo7p27RoREWFlZVXWnjVq1BCq/i7PyMgQQpibm6u1TqjJyx8AgjOARK1evXrz\n5s2nTp06evTohQsX3n777cLCwtK7cQaQBoIdXqx27dpCiKysLG0XglemUCgmTZq0bNmymTNn\nHjlypPxTs4ODg4GBwZ9//lmiPT4+XgjRqFEjNRYK9XilA6AsnAEko0uXLgMHDrx06ZLKgbGc\nAaSBYIf/ycrK2rhx4549e0q0KydHUD4/C90yd+7crVu3rlq1at26dfr6+uXvbGRk1Lp167Nn\nzz579qyosbCwMDIyUi6Xl5ixFjrhlQ4AzgBScvfu3RYtWowbN65Ee05OjhDi6dOnpbtwBpAI\n7U2hB41SOUFxdnb2hQsXbt68qfy2oKCgXr16ZmZmV69eLdrn+++/F0K4u7trrlZUEOVstLNn\nzy5rhxIHgEKh2LJlixBi6dKlRS0bN24UQgQGBqq3VqjBqx4AnAEkxt7e3sjIKCoqqqjl2rVr\nZmZmZmZm2dnZCs4AEiVTMOWgdEVGRv7yyy/Kr9euXWtjYzN+/Hjlt/PmzatVq1ZcXFzz5s27\nd+8eFhambD98+PCgQYNMTU1HjBhhZ2cXFxf3/fffm5ubh4eHt2rVSjs/Bl6Xk5NTfHz8zJkz\nTU1NS2xasGBBzZo1Sx8ABQUFXbt2PXHixMCBA1u1anX16tV9+/a5urpGRUWVfhFUcq9xAHAG\nkJLvv/9+6NChenp6Q4YMcXR0vHv3bkhIyNOnT9evX69clYQzgDRpO1lCjVavXl3Wf/cbN24o\nFIrLly8LIbp371681+nTp3v37m1paWlgYGBnZzdu3DjlztA55fzi37p1S1HGAZCZmalcNt7Q\n0LBevXrTp09/9OiRdn4A/DOvdwBwBpCSqKioQYMG2djY6OvrW1pa9ujR4/Dhw0VbOQNIElfs\nAAAAJILBEwAAABJBsAMAAJAIgh0AAIBEEOwAAAAkgmAHAAAgEQQ7AAAAiSDYAQAASATBDgAA\nQCIIdgAAABJBsAMAAJAIgh0AAIBEEOwAAAAkgmAHAAAgEQQ7AAAAiSDYAQAASATBDgAAQCII\ndgAAABJBsAMAAJAIgh0AAIBEEOwAAAAkgmAHAAAgEQQ7AAAAiSDYAQAASATBDgAAQCIIdgAA\nABJBsAMAAJAIgh0AAIBEEOwAAAAkgmAHAAAgEQQ7AAAAiSDYAQAASATBDgAAQCIIdgCqkBEj\nRshkstTU1Ap/ZUtLy7CwsAp/WQB4JQQ7AFVIy5Yte/XqZWxsXFEvGBwc7OXlZWNjk56e3rt3\nb0dHx9WrV+fk5Agh+vbtK5PJTp48qbJjYWGhg4ODiYnJo0ePSmx65513ZDLZpEmTKqpIAFUH\nwQ5AFRIQEPCf//ynZs2aFfJqH374oY+PT15e3qxZs6pVqzZmzJg6deosWrRowoQJQgh/f38h\nxLfffquy76+//pqYmDhkyJBatWoVb4+NjV23bl2FlAegCpIpFApt1wAAuufZs2dWVlYeHh4n\nTpyQyWSWlpb79+/v0aPHkCFDDh48GBMT4+7uXr9+/bS0tJSUFHNz8xLdhw0btn///oiIiM6d\nOxc15ufnt2nTRqFQXLx4ceLEid98841mfyYAOo8rdgB0W7169dzc3Iq3uLi4yGSyn3/+uahl\nz549Mpls165dxZ+xGzVqlEwmy8rKWrBgQYMGDYyNjeVy+WeffVb8z9179+5Nnz69fv36RkZG\nNjY2gwYNiomJUW5KTU19/vx5mzZtZDJZ8XdftmzZp59+WrNmTX19/YkTJz59+nTv3r0lan70\n6NHhw4ebNGlSPNUJIT755JOLFy9++OGHFfHBAKiKCHYAdFvPnj3j4uKePHmi/Pb+/fu///67\nmZlZZGRk0T4REREymaxnz57FOxoZGQkhhg4dmpGRsXfv3vDwcGdn53feeWfbtm3KHR48eNCu\nXbvdu3ePHDly69at77zzzrlz5zp16qR8ZVtbW2Nj47CwsOzs7OIv6+LiMnfuXEdHRyHEpEmT\n9PT0St+N3blzZ25u7uTJk4s3xsfHBwYGTpkypX379hXxwQCoigh2AHRbz549FQpF0RiF8PBw\nAwODYcOGHT9+vGifiIiIFi1a1KlTp3hHAwMDIYSVldXGjRvbt2//5ptvbty4UQhx8OBB5Q5L\nliy5e/duWFjYhx9+OGbMmIULF54+fdrIyOi9994TQpiami5YsCAuLs7d3X3Dhg35+fmla5PL\n5b17946Ojr5y5Urx9q1btxobG48fP754o7+/v6Wl5erVq//5ZwKgyiLYAdBtPXr0kMlkRTEu\nPDy8efPmXbt2jY2Nffr0qRAiJSXl+vXrvXr1Utm9eLp64403TE1Nk5KShBAKhSIkJMTNzc3e\n3j71/xkaGr755puxsbFZWVlCiKVLl37xxRdpaWkzZsx4+vTp2LFjfX19IyIiir++8rJc8Yt2\nMTExly9fHjp0aPFhE9u2bTt69OiXX35pYWFRQR8MgKqIYAdAt9WpU6d58+YnTpxQfhseHu7l\n5eXl5ZWfn3/mzBllixDirbfeUtndwcGh+LeGhoZ5eXlCiPv37z98+PD8+fN1/y40NFQIcefO\nHSGETCabNWvW3bt3IyIiqlWrZmpqunPnzq5du/r4+OTm5ipfsG/fvvb29sp7r8oWZcgrfh/2\n/v377777br9+/YYMGVKRHw2AqodgB0Dn9ezZ89y5c0+fPk1OTr5+/Xrnzp3r168vl8uVD8NF\nRERUr169Y8eOKvsaGhqqbM/MzBRCtGzZ8hdV7OzsivbU19fv3LmzkZHR5s2bExISevfuHRwc\nvGnTpqKtEydOfPjw4eHDh4UQ2dnZe/fubdq0qZeXV9ErzJ49Ozc3d8OGDRX0eQCougy0XQAA\n/FM9e/b85JNPzpw5c+/ePZlM1qlTJyFEx44dlfdnlVOKKIdKvLyiCUq8vb1fvlf9+vX37t1r\nZWUVGho6a9YsZeOkSZNWrFjx7bffDh069MCBA+np6UuWLCnq8ssvv+zdu/eDDz7Q09NT3gXO\nyMgQQjx79iwpKalGjRo1atR4pcoBVGVcsQOg87y8vIyNjU+ePBkeHu7i4mJtbS2E6NSpU3R0\n9K1bt27cuFHWA3blqFOnjrW19R9//JGWlla8/cGDB8ovAgMD69atW2KrEKJGjRpmZmbKcKZk\nb2/fu3fv33777eHDh7t27TIxMSn+YN/Ro0eFEMuXL5f/PxcXFyHEnj175HL5qlWrXrVyAFUZ\nwQ6AzqtWrZqnp2dUVFR4eHjRzHCdOnV6/vz5Z599Jsp+wK58w4YNy8nJWbNmTVHLgwcP3Nzc\n+vfvL4Ro0KBBampqQEBAiWneQ0JC0tPT27VrV7xx8uTJBQUFX3/99dGjR4cMGWJlZVW0aeLE\niT/+nXLeu7feeuvHH3/09fV9jcoBVF0KANB9q1evNjMzE0IEBwcrWwoLC62srMzMzBwcHIp2\n8/HxEUKkpKQoFIqJEycKIW7cuFH8dSwsLFxcXJRf37t3Tzm0YsKECdu2bVu1apWDg4OhoeGv\nv/6qUCjy8/OVd2lbtGjxzjvvmJiYjBo1asCAATKZTC6Xp6amFn/Z/Px8uVxerVo1IcTx48fL\n/1mUc/JNnDjxH38qAKocrtgBkIKePXsqpyApGpQgk8k8PT2zsrJe73KdEKJ27drR0dFTp04N\nCwubNGnSxx9/3LJly5MnTyonOtbX1//++++/+OILAwOD7777LicnJzg4+OLFi9OmTYuJiSkx\nZ55yCEV2dnazZs2UjwACgDqwViwAVICitWK1XQiAKo0rdgBQAQICAt544w1tVwGgquOKHQAA\ngERwxQ4AAEAiCHYAAAASQbADAACQCIIdAACARBDsAAAAJIJgBwAAIBEEOwAAAIkg2AEAAEgE\nwQ4AAEAiCHYAAAASQbADAACQCIIdAACARBDsAAAAJIJgBwAAIBEEOwAAAIkg2AEAAEgEwQ4A\nAEAiCHYAAAASQbADAACQCIIdAACARBDsAAAAJIJgBwAAIBEEOwAAAIkg2AEAAEgEwQ4AAEAi\nCHYAAAASQbADAACQCIIdAACARPwf2mmtLhya7dcAAAAASUVORK5CYII="},"metadata":{"image/png":{"width":420,"height":420}}}]},{"cell_type":"markdown","source":["We can see from the scatterplot of V4 versus V5 that the wines from cultivar 2 seem to have lower values of V4 compared to the wines of cultivar 1."],"metadata":{"id":"wMw2bkM3snO7"}},{"cell_type":"markdown","source":["**A Profile Plot**\n","\n","Another type of plot that is useful is a “profile plot”, which shows the variation in each of the variables, by plotting the value of each of the variables for each of the samples.\n","\n","The function “makeProfilePlot()” below can be used to make a profile plot. This function requires the “RColorBrewer” library. To use this function, we first need to install the “RColorBrewer” R package (for instructions on how to install an R package, see How to install an R package)."],"metadata":{"id":"gJbQvjZjsr4S"}},{"cell_type":"code","source":["#upload this function\n"," makeProfilePlot <- function(mylist,names)\n"," {\n"," require(RColorBrewer)\n"," # find out how many variables we want to include\n"," numvariables <- length(mylist)\n"," # choose 'numvariables' random colours\n"," colours <- brewer.pal(numvariables,\"Set1\")\n"," # find out the minimum and maximum values of the variables:\n"," mymin <- 1e+20\n"," mymax <- 1e-20\n"," for (i in 1:numvariables)\n"," {\n"," vectori <- mylist[[i]]\n"," mini <- min(vectori)\n"," maxi <- max(vectori)\n"," if (mini < mymin) { mymin <- mini }\n"," if (maxi > mymax) { mymax <- maxi }\n"," }\n"," # plot the variables\n"," for (i in 1:numvariables)\n"," {\n"," vectori <- mylist[[i]]\n"," namei <- names[i]\n"," colouri <- colours[i]\n"," if (i == 1) { plot(vectori,col=colouri,type=\"l\",ylim=c(mymin,mymax)) }\n"," else { points(vectori, col=colouri,type=\"l\") }\n"," lastxval <- length(vectori)\n"," lastyval <- vectori[length(vectori)]\n"," text((lastxval-10),(lastyval),namei,col=\"black\",cex=0.6)\n"," }\n"," }"],"metadata":{"id":"WHKYy71wsmYb"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["To use this function, you first need to copy and paste it into R. The arguments to the function are a vector containing the names of the varibles that you want to plot, and a list variable containing the variables themselves.\n","\n","For example, to make a profile plot of the concentrations of the first five chemicals in the wine samples (stored in columns V2, V3, V4, V5, V6 of variable “wine”), we type:"],"metadata":{"id":"sKr7d2tLszhs"}},{"cell_type":"code","source":[" #install.packages(\"RColorBrewer\")\n"," #library(RColorBrewer)\n"," names <- c(\"V2\",\"V3\",\"V4\",\"V5\",\"V6\")\n"," mylist <- list(wine$V2,wine$V3,wine$V4,wine$V5,wine$V6)\n"," makeProfilePlot(mylist,names)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":472},"id":"Ydw84AJPsyU0","executionInfo":{"status":"ok","timestamp":1717435552045,"user_tz":-120,"elapsed":607,"user":{"displayName":"toni Monleon","userId":"05269440626861479105"}},"outputId":"e126415b-4914-432d-ee34-29ccda742315"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["Loading required package: RColorBrewer\n","\n"]},{"output_type":"display_data","data":{"text/plain":["plot without 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EHOoI3ohu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\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LViZ92j1VphliWszGLrfiQSnn6BlRYq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GIt8FRbkydGC0uq1SOzO3KEEFU3V5FqX5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\nvK+i8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BDgAAAMBPINgBAAAA+AkEOwAA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"},"metadata":{"image/png":{"width":420,"height":420}}}]},{"cell_type":"markdown","source":["It is clear from the profile plot that the mean and standard deviation for V6 is quite a lot higher than that for the other variables."],"metadata":{"id":"0ql9jWXJs_NC"}},{"cell_type":"markdown","source":["#Calculating Correlations and p-values test for Multivariate Data\n","\n","It is often of interest to investigate whether any of the variables in a multivariate data set are significantly correlated.\n","\n","To calculate the linear (Pearson) correlation coefficient for a pair of variables, you can use the “cor.test()” function in R. For example, to calculate the correlation coefficient for the first two chemicals’ concentrations, V2 and V3, we type:"],"metadata":{"id":"a4B_6URNtn4B"}},{"cell_type":"code","source":["cor.test(wine$V2, wine$V3)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":208},"id":"y2mrTecltnAB","executionInfo":{"status":"ok","timestamp":1717435576855,"user_tz":-120,"elapsed":366,"user":{"displayName":"toni Monleon","userId":"05269440626861479105"}},"outputId":"2a48c2d1-0d15-464c-b159-29860e182c44"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["\n","\tPearson's product-moment correlation\n","\n","data: wine$V2 and wine$V3\n","t = 1.2579, df = 176, p-value = 0.2101\n","alternative hypothesis: true correlation is not equal to 0\n","95 percent confidence interval:\n"," -0.05342959 0.23817474\n","sample estimates:\n"," cor \n","0.09439694 \n"]},"metadata":{}}]},{"cell_type":"markdown","source":["This tells us that the correlation coefficient is about 0.094, which is a very weak correlation. Furthermore, the P-value for the statistical test of whether the correlation coefficient is significantly different from zero is 0.21. This is much greater than 0.05 (which we can use here as a cutoff for statistical significance), so there is very weak evidence that that the correlation is non-zero.\n","\n","If you have a lot of variables, you can use “cor.test()” to calculate the correlation coefficient for each pair of variables, but you might be just interested in finding out what are the most highly correlated pairs of variables. For this you can use the function “mosthighlycorrelated()” below.\n","\n","The function “mosthighlycorrelated()” will print out the linear correlation coefficients for each pair of variables in your data set, in order of the correlation coefficient. This lets you see very easily which pair of variables are most highly correlated."],"metadata":{"id":"LPtNIY5Jt4f8"}},{"cell_type":"code","source":[" mosthighlycorrelated <- function(mydataframe,numtoreport)\n"," {\n"," # find the correlations\n"," cormatrix <- cor(mydataframe)\n"," # set the correlations on the diagonal or lower triangle to zero,\n"," # so they will not be reported as the highest ones:\n"," diag(cormatrix) <- 0\n"," cormatrix[lower.tri(cormatrix)] <- 0\n"," # flatten the matrix into a dataframe for easy sorting\n"," fm <- as.data.frame(as.table(cormatrix))\n"," # assign human-friendly names\n"," names(fm) <- c(\"First.Variable\", \"Second.Variable\",\"Correlation\")\n"," # sort and print the top n correlations\n"," head(fm[order(abs(fm$Correlation),decreasing=T),],n=numtoreport)\n"," }"],"metadata":{"id":"k984K7Wvshnj","executionInfo":{"status":"ok","timestamp":1717494857030,"user_tz":-120,"elapsed":352,"user":{"displayName":"toni Monleon","userId":"05269440626861479105"}}},"execution_count":3,"outputs":[]},{"cell_type":"markdown","source":["To use this function, you will first have to copy and paste it into R. The arguments of the function are the variables that you want to calculate the correlations for, and the number of top correlation coefficients to print out (for example, you can tell it to print out the largest ten correlation coefficients, or the largest 20).\n","\n","For example, to calculate correlation coefficients between the concentrations of the 13 chemicals in the wine samples, and to print out the top 10 pairwise correlation coefficients, you can type:"],"metadata":{"id":"HoBu4fSBuClJ"}},{"cell_type":"code","source":["mosthighlycorrelated(wineb[2:14], 10)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":411},"id":"gkls5d6gsI3z","executionInfo":{"status":"ok","timestamp":1717435590212,"user_tz":-120,"elapsed":375,"user":{"displayName":"toni Monleon","userId":"05269440626861479105"}},"outputId":"515d4f05-db2e-4257-de16-ef3248748e34"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/html":["\n","\n","\n","\t\n","\t\n","\n","\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\n","
A data.frame: 10 × 3
First.VariableSecond.VariableCorrelation
<fct><fct><dbl>
84Total phenolsFlavanoids 0.8645635
150Flavanoids OD280/OD315 of diluted wines 0.7871939
149Total phenolsOD280/OD315 of diluted wines 0.6999494
111Flavanoids Proanthocyanins 0.6526918
157Alcohol Proline 0.6437200
110Total phenolsProanthocyanins 0.6124131
154Hue OD280/OD315 of diluted wines 0.5654683
132Malic acid Hue -0.5612957
118Alcohol Color intensity 0.5463642
137Flavanoids Hue 0.5434786
\n"],"text/markdown":"\nA data.frame: 10 × 3\n\n| | First.Variable <fct> | Second.Variable <fct> | Correlation <dbl> |\n|---|---|---|---|\n| 84 | Total phenols | Flavanoids | 0.8645635 |\n| 150 | Flavanoids | OD280/OD315 of diluted wines | 0.7871939 |\n| 149 | Total phenols | OD280/OD315 of diluted wines | 0.6999494 |\n| 111 | Flavanoids | Proanthocyanins | 0.6526918 |\n| 157 | Alcohol | Proline | 0.6437200 |\n| 110 | Total phenols | Proanthocyanins | 0.6124131 |\n| 154 | Hue | OD280/OD315 of diluted wines | 0.5654683 |\n| 132 | Malic acid | Hue | -0.5612957 |\n| 118 | Alcohol | Color intensity | 0.5463642 |\n| 137 | Flavanoids | Hue | 0.5434786 |\n\n","text/latex":"A data.frame: 10 × 3\n\\begin{tabular}{r|lll}\n & First.Variable & Second.Variable & Correlation\\\\\n & & & \\\\\n\\hline\n\t84 & Total phenols & Flavanoids & 0.8645635\\\\\n\t150 & Flavanoids & OD280/OD315 of diluted wines & 0.7871939\\\\\n\t149 & Total phenols & OD280/OD315 of diluted wines & 0.6999494\\\\\n\t111 & Flavanoids & Proanthocyanins & 0.6526918\\\\\n\t157 & Alcohol & Proline & 0.6437200\\\\\n\t110 & Total phenols & Proanthocyanins & 0.6124131\\\\\n\t154 & Hue & OD280/OD315 of diluted wines & 0.5654683\\\\\n\t132 & Malic acid & Hue & -0.5612957\\\\\n\t118 & Alcohol & Color intensity & 0.5463642\\\\\n\t137 & Flavanoids & Hue & 0.5434786\\\\\n\\end{tabular}\n","text/plain":[" First.Variable Second.Variable Correlation\n","84 Total phenols Flavanoids 0.8645635 \n","150 Flavanoids OD280/OD315 of diluted wines 0.7871939 \n","149 Total phenols OD280/OD315 of diluted wines 0.6999494 \n","111 Flavanoids Proanthocyanins 0.6526918 \n","157 Alcohol Proline 0.6437200 \n","110 Total phenols Proanthocyanins 0.6124131 \n","154 Hue OD280/OD315 of diluted wines 0.5654683 \n","132 Malic acid Hue -0.5612957 \n","118 Alcohol Color intensity 0.5463642 \n","137 Flavanoids Hue 0.5434786 "]},"metadata":{}}]},{"cell_type":"markdown","source":["This tells us that the pair of variables with the highest linear correlation coefficient are V7 and V8 (correlation = 0.86 approximately)."],"metadata":{"id":"6FbsawZtsIus"}},{"cell_type":"markdown","source":["Other nice possibility is use a correlation plot matrix\n","\n"],"metadata":{"id":"fZByG0u7jcPG"}},{"cell_type":"code","source":["#install.packages(\"ggcorrplot\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"lRq4mkOQjbam","executionInfo":{"status":"ok","timestamp":1717435656854,"user_tz":-120,"elapsed":38444,"user":{"displayName":"toni Monleon","userId":"05269440626861479105"}},"outputId":"67363384-c546-4e46-a150-d2bf988fab75"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["Installing package into ‘/usr/local/lib/R/site-library’\n","(as ‘lib’ is unspecified)\n","\n","also installing the dependencies ‘plyr’, ‘reshape2’\n","\n","\n"]}]},{"cell_type":"code","source":["#copy wine matrix and rename with correct names in order to interpret\n","wine.1<-wine\n","\n","names(wine.1)<- c(\"groups\",\"Alcohol\",\"Malic acid\",\"Ash\",\"Alcalinity of ash\",\"Magnesium\",\"Total phenols\", \"Flavanoids\", \"Nonflavanoid phenols\", \"Proanthocyanins\",\"Color intensity\", \"Hue\", \"OD280/OD315 of diluted wines\",\"Proline\")\n","\n","#correlation matrix\n","correlation.matrix.allvariables <- cor(wine.1[2:14])\n","\n","\n","# Types of correlogram layout\n","# --------------------------------\n","# Get the lower triangle correlation\n","library(ggcorrplot)\n","ggcorrplot(correlation.matrix.allvariables, hc.order = TRUE, type = \"lower\",\n"," outline.col = \"white\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":437},"id":"siR6wPEmtmP6","executionInfo":{"status":"ok","timestamp":1717435740810,"user_tz":-120,"elapsed":1278,"user":{"displayName":"toni Monleon","userId":"05269440626861479105"}},"outputId":"6aaeb69a-5da9-48b8-8e6a-7240c6006b32"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["plot without 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SBHjhwpLS2dNWuWQCA4efLkZ599NmTIkMmTJ3fxvQIAADArBLvB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\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eUnTpxITEwMCwvz8/PbuXPn9evXFQpF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Oz+dHREQgBXr+jfvWuFwu9p9IiTNnpWd9\nyrLVi2U7Op1eXl7O4/ESEhIUqReUYGxsXFNTw2azExMTJVKdikx0lmLGkZ4To3QTdd42NDSU\nzna+vr5yR3A138tgmoNgB4BM5Jj88/TpUyzYTXVvpb+/v6ioaHh4WOK1qFTqo0ePmpubWSyW\nubm5gqs+//73vzMzM5ubm83MzCS6e3h7e9fW1lZWVjo5OSk+wxT93vOvtrbWxsZGVGqGLdox\nmUwvL6958+YheXv+Sdu7d+/NmzdFS5vKmjkrPetT9q1eKpX66quvJiQkuLm5yf2+xqWvr19Y\nWLhmzRoLCwvlfueJzI5sJz0nRrkm77wtke0UjFnqvJfB9AfBDgCZyDH5Jz8/f2BgIDY2dqqv\nZW1tnZ+fz+FwKBSKxGvp6Ojw+fzR0VFLS0sFt9i8vLx6e3sbGhqam5uDgoIkRhcsWLDgxo0b\nJBIJ67mgYPeKlpaWe/fucbncjIyMhw8f2tnZGRsbo98X7QQCATbiFsnV80/cixcveDweQqi2\ntlb8uIlSZs5Kz/qc0lYvDoeTu3xqElQqNTc3t76+PioqSunffCLi2U6Uy6e5l86JUa6Xdt4W\nZTtFDixj1Hkvg+kPgh0Ak2ltbW1ubiYSibq6ulOa/HP//v0ff/zx448/lv0sZF9fX3d3N4lE\nIhAIzs7OeXl5bDabSqWK77o2NjaOjIykpaUp0kgFI6rjrqur43A4ISEh4hHH0NAwJyfH0tJS\nwWZaWNm4ra1tZGSkh4dHV1dXaWlpenp6e3u7vb09hUJ58uRJQUEBNoJTwXfU3t6+a9euxsZG\nbW3twcHB/v5+GbOdjHA4nPSsTyVu9coHh8MJhcKsrCxHR0fRmGA1wLIdlUoVhfLpTMY5MfKp\nqqoyMzOTSI0ydt42MzOTHukxVeq5l8FMAcEOgPF1dXXt37//559/zsnJuXLlCp/PDwkJcXJy\nknHyj6mpaVhYmJWVlSyv1d3dffDgwaNHj16/fv3atWuDg4NYM3omk8lgMPB4/IIFC/B4vEAg\nOHz4cGJiomhy1ORE08cn6pUv+jzgcDhlZWW+vr6iNSc+n3/+/Pk333xT7pYW0mXjNBotPDzc\n29u7p6eHyWTevHmzu7s7KioqLy+vv79fwVOWg4OD2JHAHTt2+Pv7x8XFkclkLpfLZDLHzXaK\nbH5JzPpU1lav3GxsbK5fv97c3BwdHf3Swz1KhGU7tb3clDx9+rS9vd3ExKStrQ0h9PjxYxnn\nxEwV1vmlvr4+ODhYPNspt/P26OhoTU3NJFvGKr2XwcwCwQ6AcfD5/I8//tjb2/v999/38vJq\naWkpLy9nMpmvvfZaQECAjJN/ZFwTwl5r0aJFSUlJzs7O9+/fLykpKSsrW7t27ZIlS8rLy0tL\nS7OzsysrK//5z3/Gx8djTctkIT19XJro86C8vDwnJ0dXV9fMzKy+vv7AgQNr1qwRDXefqonK\nxg0MDMhkMnZGr6+vLy8vLz8/38jIqK6uLiYmRpHOC5mZmcXFxampqViyweFwjo6Onp6eEkeJ\nlVJjJDHrU5TtNNVOAut7UlZW5uPjo5RGKrPAf/7znxMnTpibmx86dMje3t7Ly0tFc2L+9re/\nPXnypL29vbGxUTzbKbfz9uHDh0+fPj35iXgV3ctgxoFgB4CksbGxffv2BQYGbty4EfvNPjIy\nUiAQ8Hi80tLShISENWvWKHHyz+eff+7v7//GG29QKBQnJ6fo6OiWlpaqqipbW9ugoCBspkVf\nXx+BQEhOTl6xYoXs31l6+vi4TxN9HtTW1paXl3O53NbW1o0bNy5fvly+dzR52Th2xMTU1DQ4\nODg0NLS/v//u3bvr169XcFmLwWDU1NTExcVJHCW2trYuLCyU2JNVcGNU9F9MxmynBhQKBRuS\nps4XnVYkzizb29tfv349Ly9vzZo1ovCkijkxOjo6LBZLV1e3paVFItspsfM2mUyWZbqg0u9l\nMBNBsANAUl1d3W+//Zaamioq4cfj8X5+fgKBgMvldnR0hIWFKT75B9Pc3HzmzJnNmzeLDl50\nd3efP38+MTERWwUkEomenp5RUVHLly+fagWV7GPgRc9sbGwkk8kpKSkytlMZd7f3pWXjouPD\nRkZGAQEBMTExim9W9vb2MplMoVAo8fFsY2NTV1f36NEjNputlDYxmJdmOzW3kzAxMZnjFfES\nZ5Y7Ozvz8vJGRkbu378vMfZDuXNibG1ts7OzR0dH/fz8SkpKJLIdUlLnbdknR8t9L4NZA4Id\nAJKqqqpYLFZERIREaPP29ubxeBwORymV/hisPV5QUBBWNsfn80XzA4RCIdZ7VsZCvXHJke1K\nS0snf6a4cXd7ZSwbb2pqwop+5N6sFDX0R78fJa6oqBB9gooMDAzgcDiBQFBUVOTp6ams8aaT\nZDtoJ6F+EmeWBQKBo6Ojn5/fRCPdlDUnBo/Hj42NlZSULF261MLCgsFgSGQ7ZXXeliPbTele\nBrMGBDsAJA0PD2dmZg4ODgYEBIg/jsPhaDRaRkYGjUaTOCohC4lqbuxHrVAoTE9PHxwcDA0N\nFU91CKHR0dG9e/caGhoqGBHkyHYvfabIuLu9yi0bH5f0yQwDAwNnZ+fc3Fwmk+ng4CC+ullb\nW0smk9evX5+Xl4cV803ptYaHhydaXJko283xxTONkDizHBER4ejo+NJxvYrPiUEI2dvb37x5\n8969e5999tmTJ08ksp2yOm8LhcKGhgaE0P379186OVqOexnMGhDsAJBEJpOLi4u5XK65ubl4\nWxOEkJmZ2cWLFwMDA+UY6iVRzW1tbY0QMjExKSkpqaqqQgidPHlSfCoU1h7PxcXFw8NDwXck\nX7a7e/fuS0v6xv3Oyi0blzbRyQx7e3s7O7uioqLCwkJdXV0XFxdsoe7IkSNvvPGGg4PD2NhY\nYWFhZGQkiUSS8bV6enref/99Eok0UTmURLZTZNYnUAqJM8svzXYYuefEIIS0tLQGBwcrKiqs\nra3Xr1/f1dUlke0U77zd3t6ekpIyMDCA7at2dHRMKdvJci+DWQOCHQBoaGgoMzMTa0ZlYGCA\nw+GcnJxyc3PZbLaFhYX4j86GhoacnJytW7fK8evvuNXcCCFHR8fc3Fwej+ft7b1p0ybR4y0t\nLZcuXdqxY4fEQAvZie9UTjXbDQ0NrV69evIjlgwGIyMjw9vbW/o7K7FsXMLkJzNcXV1dXV1L\nS0vZbHZeXl51dfXp06fXrVuH1fCNjY3l5OSsXbtW9s1frI3Ipk2bJvkXF/23VXDWJ1AK6TPL\n4tlu8eLFZmZmfD6/ra1N/P/essyJwf4Pb2NjI31+1s7ODpuPFx0d7efnJ53tFOm8/ezZs507\nd65evTo5OdnNzS08PNzW1hb7fUaWbCfLvQxmEwh2YG4ZHR09deqUg4OD6NBie3v7Rx99lJ+f\nz+Vyb9++bWlpaWdnRyaT7e3tmUwmk8kcGBhwdXXV1tZua2v76quvNmzYIN8S2kTV3GZmZnZ2\ndiwW6/79+0NDQy4uLtra2q2trV9++eW6deskBgTJ/loSO5UkEmlK2W7JkiUvTXXHjx9/6623\nTE1NJ/rOSikbl/DSkxmOjo7h4eFCobCzs1MoFL7xxhuitYqsrCw8Hi/jLJDW1taffvrJzMzM\n2tr6pWc7cDicIrM+gRKNuz8uynb5+fnDw8NnzpyxtbUVH+vy0jkxTCbz4MGDd+/evX79ektL\nC4VCEZ80o6ur29XVxWazsdLScbOdLKTnESOEbt++XVZW9sknn4iaFGItryVa+Uz0X+Ol9zKY\nZSDYgbklLy/vzJkzooYUz58//+STT+Lj41NSUmxsbCoqKgoKCrABUNbW1gsXLqyqqqqoqLh6\n9WpBQcHFixfXrl0bFxcn30tPUs1tbW3t6elZXV1dVlZ2+fLljIyMK1eurF+/Xr7XmqSHnLIq\nb7BUt3fvXtFW9bjfWfGyceme/rKczCASiSYmJm+++WZkZCSZTO7t7dXT08vNzT1//nxKSoqM\nB5mvXr16/fr17u5uIpEoUW0JpqHBwcHMzEwej2dmZiaa1iWR7eh0enl5OY/HS0hIEF81l2VO\nTF9fH3Z+VkdHp6mpKSMjo6qqysjIyMrKCstb1tbWN27ceP78eUBAAA6HE2U7a2tr2Ss3pOcR\nI4SYTGZNTU1sbKxEKx87O7uCggIWi6XE495gFoBgB+YWGo0makgRHBxcWlqKLepoaWnZ29v7\n+PgwGAzRcM/58+fHxMRgC3hOTk5bt26VuytHa2srn88PCAiYqOIHey17e3sLCwtPT8/t27fL\nt6n30h5yEglMjq4c0qkOI53tqFSqImXj4/b0n+rJjL/97W/Hjh27cOFCfX39p59+Kvvnn6en\n5+DgIJvNfvjwoYIjp4CqidbdeTxeRkYGNvRPOttRqdRXX301ISFBYmCGLHNiRONxdXR04uPj\nh4eH6+rqsBtZW1vb1tbW2Nj44cOHxcXFMTExRCIRy3aOjo7BwcGyvxHpecQIof7+/qKiouHh\nYYmfP1Qq9dGjR83NzSwWy9zcXGKuNJizINiBOUfUbKyiooJIJC5YsED0+7T04HYCgWBnZ7dk\nyRJ3d3dDQ0P5XlEoFO7cuRM7TkulUifKdng83tbWdtGiRa6urvI1wUey9ZATJTA5po9PlOqG\nhoZqamosLCwksh2NRpO7bHzcnv5TOpkxNjaGx+OtrKzCw8O3b98+1UYnS5YsGRkZqampKS8v\nDwkJUXPPYSCj58+fp6SkxMTEpKam2tnZcTicwsJC7P6VznY4HG7cjVFZ/nGxbJeTk9PU1LRz\n586wsLDh4eE7d+6w2ezbt28LhcJly5ZlZGQYGBhgwRGHw8nRe1J6HjHWyofD4VAoFIn0pqOj\nw+fzR0dHLS0toRIAYCDYgbkIy3bFxcWNjY1UKnXRokWiv5LOdoq/HB6PxzZNCgsLpbPdRNXc\n8pG9h9zKlSun+kkwSarbs2fPs2fPvL29pdftSCSSfGXjE/X0l/1kBtbMwtPTk0ajybgw2dra\n2tzcTCQSsQMW2P9VsPei/nkSQNy49WcIIWyH9M0338R+DfP29hZfd1fumWUs2+Xm5ubl5a1Y\nsWLVqlXR0dEkEqmpqYnFYpWVlZFIpMbGxvj4eAWH9oqf7dXV1XV2ds7Ly2Oz2VQqVXzVubGx\ncWRkJC0tTb6xGWBWgmAH5gqJNnL+/v7YYkxzc/PSpUvFT56qIttRKBRXV9dxs91E1dxyvCki\nkai6HnJY8Xh0dLR49Q/6PdXZ2dlt3bpVNKdVwd1ezOQ9/ZV+MqOrq2v//v0///xzTk7OlStX\n+Hy+u7u7xmeFARHp+rPW1tZTp04NDQ25urpOsu6u3DPLWLbLy8vLy8vz9PS0trb28PBYtWoV\njUZ7/Phxc3Mz9hvOvHnzFHkVibO9WBUdk8lkMBh4PH7BggV4PF4gEBw+fDgxMRFrbw4ABoId\nmCuk28hhH9iVlZUcDkfiA1v02YCdPlPKBYyb7Saq5pb7TamuhxxWPF5dXW1gYODi4oI9KEp1\n27ZtE1+iUGS3V2Tynv7KauiP4fP5H3/8sbe39/vvv+/l5dXS0lJeXs5kMv38/AwMDCDbTQfS\n9WdXr169ceNGY2OjpaXl5Ovuyj2zLJHtyGQytjYcHh4eGhrq6uoq32F2cdJnex0cHFxdXcvL\ny0tLS7OzsysrK//5z3/Gx8eHhIQo5U2BWQOCHZgrxm0jh31gs9ls6Q9sExOTiIgIGU9LSI9M\nbW1tPXLkiI+Pj7a2tuhp0tluompuRd6UinrIiYrHS0pKsGw3UarDKKUDyCQ9/ZXV0B8hNDY2\ntm/fvsDAwI0bNxoZGVGp1MjISIFAwOPxSktLQ0NDdXV1NTgHdo4T3VzYv4J4/ZmPj8/w8HBN\nTc3Dhw9DQ0PFK1NVse4uTjrbYY8bGRnJ0b1cRLwSQDrb2draRkZGamlp9fX1EQiE5ORkaDsM\npEGwA3PFRG3kJlmMkb2NrfTI1H//+985OTmVlZVLly6VyHZOTk55eXkMBgObfDVRNbcibwqp\npoeceLbT1tb+9ddfJ0p1yjJ5T385GvpLR3CEUF1d3W+//Zaamioq3sLj8X5+fgKBgMvldnR0\nYIsiMAdWI6RvLvH6Mz8/v5GRER6PV1FRoYZ1d3ETZTv5jFsJQCQSJbKdoaGhp6dnVFTU8uXL\np3oyA8wREOzAXDFJGzm5N9pE1dwLFiyQaOG2ZMmS9vb2iooK6WxnaWnZ19d39+5dJpNpY2Mj\nMbFeWW9KuTuVIqJsV15ePm/ePPGmqYqQu6f/VBv6S6cEhFBVVRWLxYqIiJBoceft7c3j8Tgc\nTnBwsLGxMUII5sCqn/Q8Yon6MyzbKb7uLgfxbLd8+XLZR9VJmLwSQPpsr3LfBZhlINiBucLE\nxMTW1naiViPybbSJV3NLHAXV1dUNCAiYKNvhcLinT58ODAyYm5srMgp2kjelxJ1KCaJs19HR\nYWhoKKq3k5t6evpjpFMCQmh4eDgzM3NwcFCiETEOh6PRaFifGkdHRwXfJpCP9DlrXV1diayD\nZTsF193lg90O5ubmcsdHWSoBYB4xkB0EOzDLifejx46+Tp7tprTRJl7NPe7Hz0TZrqamRktL\n68MPP5S7SYFEV45x35Tio8cnIl1vp8h3U09Pf8y4EzLIZHJxcTGXyzU3N5fo5GJmZnbx4sXA\nwEBFCqeAghTMdqqG3Q5yf7kslQAwjxjIDoIdmM2k+9Fj/UQmaiMXEREhSwAaHR2tqamhUCgS\n3UR9fHyCgoImynYVFRV+fn56enpYk4KkpCRzc3M53tREXTnGfVMdHR3Sfb+UQonZTj09/UXG\nTQlOTk65ublsNtvCwkI8wzU0NOTk5GzdulW+2WtAWaaa7WbQARcZKwFgHjGQEQQ7MGtN0o8e\niWU7OdrIHT58+PTp0+Kzt8WrucfNdnw+v6KiIjs7+86dO2fPnn311VdlHz86NDQk+oiavBZH\nKb3xZKf0bKfSnv7ipFOClZWVvb09k8lkMpkDAwOurq7a2tptbW1fffXVhg0bFNkuB8oie7ab\nWQdcoBIAKBcEOzBrTd6PHiEkdxs5MplcWFhYVFQkynYS1dzjZjsymdzW1jY8PJyUlBQdHS3j\nazEYjIMHD77yyitYU7eX1uIo3htvSsSznZeXlyJNWdXW0x8jnRLodPrChQurqqoqKiquXr1a\nUFBw8eLFtWvXxsXFKf5yQG69vb1DQ0NYycFLs114eLi3t/fMWtaCSgCgXBDswCwkYz96hJB8\nbeTIZLKnp6d4tpPuOCWR7YhEIp1Oj4mJiY6Oln0OPTbCKzU1FeukIGNXDgV7400VFsioVGpo\naKhSvpWqe/qLyhP19PQkUoK1tXVMTIydnR3ETxaHAAAgAElEQVSZTHZyctq6davqDlSCl+rs\n7Pzmm2++//77S5cuVVdXL1q0iEQiTZLtZlD9mXjtr6GhIVQCACWCYAdmIdn70SOE5GsjJ1+2\nm9JLSA9mlb0rhyK98eSgYPG49LdSUU9/6fJEDw8PiX8mEolkZ2e3ZMkSd3d3Q0NDpbwpIIeO\njo6UlJRFixatX79eKBSWlZUxGAx/f38DA4Nxs90Mqj+Trv318fGBSgCgLBDswCzk6emp6n70\nQqGwoaEBIXT//n02my1LtptSNTeDwTh69Ohnn30mvjUzR2pxVNTTf5LyxGXLlikSwYHSCYXC\nTz/9NCYmZsOGDVZWVsHBwQ0NDY2NjcXFxeNmuxl0VGKi2t+wsDCoBABKAcEOzB7itThY7xIV\n9aNvb29PSUkZGBjANlU7Ojpemu2mNDIVq6sbHh62sbERP5Ewd2pxlNvTH72sVdiyZcuCg4Nn\nYkqYrVgs1s2bN9PS0vB4PEIoOzu7sLAwIiKCy+VKZztF5hGr3yS1v0FBQVAJABQHwQ7MBuPW\n4ihrDqyEZ8+e7dy5c/Xq1cnJyW5ubuHh4ba2ttg+0bjZLjw8PCAgQPYEie3Avvnmm9XV1RKn\nTXE43NypxVFWT3+MLOWJMzElzFZMJrOyshJrSV1SUvLDDz98/vnny5cv7+joqK2tLS4uXrx4\n8djYWFNT08qVK2fKDqwstb8uLi5QCQAUBMEOzHiT1OIoZQ6shNu3b5eVlYmP0sJW/rC+axLZ\nbqrV3KK6Oj8/v3E7iZDJ5LlTi6N4T38RWcoTTUxMZlCd1uw2NjbG5XLDwsJGRkZ2796dkpKC\nVU24u7tfuXJlYGDg9u3bN2/epNFocldTqN+Uan8BkBsEOzCzvbQWR+45sBNhMpk1NTWxsbHi\na2Pz5s2zs7MrKChgsVhWVlZ2dnZydBPlcDhHjhwRnZaYqEuctbX13KnFkeNYxtjYWFVVVWFh\nYU1NzcjIiIWFBQ6HmyPlibMGhUKJiIiwsLA4deoUHo9PSkrCHicSiTweLzw8nEgkrl69WtXd\nfJRLDbW/ACAIdmCmk6UWR745sBPp7+8vKioaHh6WWEaiUqmPHj1qbm5msVjm5uZytAU2NjYO\nCAgQ/8KJst38+fOhFmdcDQ0Ne/bsuXTpEo/Hq6ysfPjw4SuvvILD4eZOeeIMJRAIfv7552PH\njl2+fHl0dNTNzQ37xenMmTM6OjqRkZGiZ+bm5q5cufK1116j0+mau145qbT2FwAMBDsws8lY\ni2NoaGhiYqKUfvTW1tb5+fkcDodCoUikNx0dHT6fPzo6amlpKcePZm1tbRMTE4kHJ8p2WOU1\n1OKIY7PZX3/9dXR09Lvvvrt69eqFCxcuXbp0/vz5aI6VJ844fD5/586dZDLZ29tbIBCsWbNG\ndCOUlZVVVVXZ29vb2Nhgz7xw4cLGjRtn0OkW8UNd6Pdsp/TaXwBEINiBmU32Wpx169bJGLaE\nQmF1dTWFQhn3b/F4vLOzc15eHpvNplKp4t2GGxsbR0ZG0tLSvLy8lPLuMEqc3DWL1dfXHzhw\nYPfu3aGhoYaGhgYGBtbW1uKtjOdUeeLMsmfPnpiYmOTkZHd396ioKPFfbywtLXNzcxkMxtDQ\nUFdX19GjRxMTE52dnTV4tbIb91AX+j3bKbf2FwARCHZgZlN6Lc7Y2NiBAwdqa2tDQ0MnGl2F\nVdQxmUwGg4HH4xcsWIDH4wUCweHDhxMTEy0tLZXz3sRAtpvcixcv9uzZs2bNmsDAwEmeNqfK\nE6eh0dHR06dP0+l08cXRhoaG8+fP79q1a9zbbd68eXQ6ncvlcjgcDoezbt26mJgYNV6y/CY5\n1IUmzXYAKGjGrGYDINLa2vrkyRNbW1vsN3tsI7Kurg77iSmCx+N9fX1ff/31KX1zDofDZDK/\n//77yQeSBgQE7N69++DBg//617+w0vs7d+4kJCS4urpO/Q3JxN3dfffu3Xv37v3xxx8RQvHx\n8Sp6oZmooqKira1NvBJrIm5ubt9//z2LxWpoaDAwMAgJCVFFEAfjysrKunz5MofD2b9/v+h4\nMp/Pf/78eX9/PzY0RZxQKMzIyFi5cuWpU6daWlooFIr4gYPpTCgUfvnll6tXr169ejVCyNvb\ne2hoqKysbNeuXV988YWFhQVCKDk5GSF04cKFtLS0Q4cOwVodUBZYsQMzifRIKHd3d6wzmbJq\ncRobG5lMJkLIx8entbW1pKREotZexMLCIjIyUktLq6+vj0AgJCcnr1ixQuG3OBnRuh02rl6l\nrzWz3Lp1q7Ozc+3atZM8p7e395tvvhkcHIRWYZpCp9OlJ3yMjo7eunVrZGREurbs/PnzP/30\n0/Lly42NjU1NTUU9CKc/WQ51od/X7ZRS+wuACAQ7MGNMMhLKwMBAWbU4FAqluLi4vLy8r6/v\n7NmzXl5ekxyWJBKJnp6eUVFRy5cvp1KpCr092Zibm4eFhfn7+6vhtWaQoqKiurq61157bZIJ\nuXg8/tixY9hcTnVeGxCRnvFKJBJNTU0rKiqKi4stLCwkTiMRCITMzMzw8HAzMzNNXbN8ZG+w\nHBkZCb+kAeWCYAdmhslHQoWGhlKpVKXU4mhpafn6+rJYLC6Xu3jx4o0bNyr9vShopuxGqdOD\nBw+4XC6NRrO1tZ3oOQQC4datW/b29so92gKmZNxs5+DgkJOTw2Kx5s2bJ75A3tTUxOVyk5OT\nJ8nr00pra2t1dbWNjc2sbLAMZgoIdmBmkGUklJWVVVxcXFBQUFJSkiK/BAsEguzsbF1d3YaG\nBoQQ/D49/RkZGd28ebO5uTkqKmqiECAUCs+dO4f1/1Pz5QFx0tnO0tISO41UXFz89OnTBQsW\naGtrd3Z2Hjp0KCkpaaY0jh4bG/vwww8rKyvj4uJmZYNlMFNAsAMzgywjoYyNjQkEguK1OAKB\ngE6nJyYmslgsNpuNINtNe8bGxi0tLTU1NY8ePQoKChr34EtpaSmHw9m+fftMWf6ZHerr6/X0\n9LS1tcUflM52dDrdw8ODx+Nxudxr164VFhaeO3cuISFhppyBRQjhcLi2tjYOh2Nra2trazsr\nGyyDGQGCHZjWhoeHsdMP6hkJ1dra2tzcTKFQnJ2d9fX1AwICINvNFB4eHkwm886dOy0tLb6+\nvhKHZgQCwf79+7ds2SLHRBAgNwaDsW/fvmvXrj158sTS0lL8tzLpbGdjYxMTE2NlZWVmZubo\n6Lh161ZfX18NXrwcaDTajRs32traREtxs6DBMphxINiB6aunp+f9998nkUh0Ol3VI6HGPW9r\namoK2W6m0NXV9fHxKSkpuXv3blFRkYmJiZWVFYFAePHiBTaRIjY2Njo6WtOXObecPHmSz+cv\nXLiwoKDg5s2bDQ0NRkZGov4y0tmORCLRaDRvb28PDw+JtfkZgUQiPXnypLS01MXFBXubM7rB\nMpihINiB6ev69evNzc2bNm0iEokqHQk1yXlbCoUC2W6mMDQ0XLZsWUdHx507d5hM5qVLlzIz\nM//1r39VVFRs3rwZ6pnUT19fPz8/f926dRs3bhQKhSUlJVlZWUVFRQQCwcbGRktLa9yzFJq+\n6ikYGRmR2NnHFu34fH5ERASayQ2WwcwFwQ5MR62trT/99JOZmZm1tbWoOYWKRkK99LwtmUyG\nbDdT6OrqhoSE+Pv7GxsbGxsbU6nUqKiod999d6YU4M8yVlZWubm59+7dS0xM9PPzi42NNTY2\nrqury83NTU9P7+/vt7a21tfXF892ERERM2WbsrW19YMPPqitrbWxsTE1NcUexBbtmEyml5cX\nNtFOWYe6AJARBDswHV29evX69evd3d1EIlG8qE4VI6FkOW8rXm/n4eEx0RhZME2Ympp6enoG\nBwf7+/vT6XSJyn2gNjgc7sWLF3l5edhdo6Oj4+rq6ufnl5OT4+7unp+ff+XKlQcPHpDJ5Fde\neaW3t9fJyWnx4sWavmpZtbS03Lt3j8vlZmRkPHz40M7ODhuegS3aCQSC0NBQ7JlKOdQFgIwg\n2AHNa29v/+GHH44fP37t2rXOzk43NzcvL6/BwUE2m/3w4cPAwEDxoeDz58/HOlaQyWQnJ6et\nW7cq2G9WxvO2WLajUCghISGKvBwAc4qNjc2NGzf6+/uxG4fP56empiYkJGzZsiUiImJgYKCg\noCAzM7OsrGzr1q0zpfN2b2/v0NCQra1tZGSkh4dHV1dXaWlpenp6e3u7vb09hUJ58uRJQUEB\n9qND0xcL5hwIdkDDSkpKdu/ebWpq6ujo2NLSwuPxeDxeWFiYj4/PyMhITU1NeXl5SEiI+JBs\nAoGgxJFQsp+31dfXd3FxUfDlAJhTdHR0Ojs7CwoKsBi3a9euVatWrVq1CiFEIpH8/f1DQkJ6\nenrc3NxmRKrr7Oz85ptvvv/++0uXLlVXVy9atIhGo4WHh3t7e/f09DCZzJs3b3Z3d0dFReXl\n5fX39wcGBmr6ksGcA8EOaBKDwfj++++x3+D9/Pyio6Nra2vv3r1rYGDg6uqKDVLEKm+CgoLE\ns50Sqfq8LQBzHIVCwRbtzp07J0p1IkZGRkuXLp0RxWcdHR0pKSmLFi1av369UCgsKytjMBjY\n4FcymRwaGhoQENDX15eXl5efn29kZFRXVxcTE6Orq6vpCwdzCwQ7oDEMBuP48eN79+4VLYNp\na2v7+vqmp6cLhcKwsDD0+5BsFoulumyn0vO2AABjY+Oamho2m52YmCiR6mYQoVD46aefxsTE\nbNiwwcrKKjg4uKGhobGxsbi4GMt2CCFTU9Pg4ODQ0ND+/v67d++uX78eBhMD9cNr+gLAHCVK\ndRKLZMbGxq6uruIdBJKTk9euXdvS0pKWliYQCOR4rbKysr6+vkme4OTktHPnTjwe/+233548\nefLZs2cIoba2tkOHDm3atGkm9tMCYFqJj49HCM2UYW6jo6M//fSTxA8NNpv94MGD2NhY7I/Z\n2dnNzc3x8fFdXV27du3q6OgQPZNKpX7wwQenT59OTExU63UDgBCCYAc0gslkHjx4cPny5RKp\nDiE0NjbW0dEh8WuueLYbGhqa0msxGIzvvvuuu7t78qf5+/t/9tln5ubmV69efeONN/70pz/9\n+c9/XrlyJTSdAkBxWEvIX375RdMXIpOsrKzLly+npqaKZ7uWlpYXL140NTUhhEpKSk6fPr13\n794tW7YsX74cy3YPHjwQCAQ1NTXY8+HYBNAU2IoFGtDX11dYWFhdXW1gYCBxHOHChQuPHj3a\nsWMHHv9/fuvA9mRpNNqSJUtkf6GJ1gXHpfTztgAADA6HEwqFWVlZjo6OVCpV05czIaFQWF1d\nHRQUJN0zeWxsjMvlhoWFjYyM7N69OyUlxcnJCSHk7u5+5cqVgYGB27dv37x5k0ajYY8DoCm4\nsbExTV8DmItqamr27t07NDS0ZcsWbJsGIZSenn7x4sUvv/wSa+ypoCmlOgCASj19+nTTpk1U\nKvXbb7/F4XCavpxxjI2Nff3118+fP09LS0MIHT9+PD093c7Obv/+/Vg9Rn9/v6Gh4XfffdfW\n1nbgwAHRF6akpHh4eNy7dy8wMDAyMlJjbwAAhBBsxQJNcXd33717t66u7o8//njt2jX0e6rb\nv38/pDoAZh8DA4OwsLCmpqaGhgZNX8v4OBwOk8nctGkTDofD4XDbtm2LiYl58OCBaE8Wa65U\nV1cn8YV4PN7X1/evf/0rpDowHcBWLNAYc3NzNzc3BoNRUlLy8OFDBoOxf/9+c3Nzxb9zcXHx\nN998s337dvF9246OjpMnT/7www83btzo6OhwdnaGs64AqBOFQjExMcEOvE9DjY2NTCYTIeTj\n49Pa2lpaWrp+/XrpPdmysrKqqip7e3sbGxuEEJ/Pv3DhwsaNG2fKJDQw60GwA5okynb379+P\ni4tTVjPPnp6evLy8O3fuBAUF6evrI4RqampSU1MNDAxsbGwePXrE4/GKioqCgoJIJJJSXhEA\n8FImJibTuV8dhUIpLi4uLy/v6+s7e/asl5cXjUYTn2OLZTtLS8vc3FwGgzE0NNTV1XX06NHE\nxERnZ2dNXz4A/x8EO6BhomzH4/Gkz1LIh0KhUCiUnJycioqK0NDQkZGRTz/99C9/+cuGDRsC\nAwNXrlzZ29tbWVl59+7dyMjI6VnuAwBQMy0tLV9fXxaLxeVyFy9evHHjRoQQDoeTyHZUKpVO\np3O5XA6Hw+Fw1q1bB2fnwbQCwQ5onvierLKynb29vZaWVlFRUW1t7dDQEJ1Oj4iIwP6KQCD4\n+fndv3+fx+MtXLiQQqEo/nIAgFlAIBBkZ2fr6upihYDY+qJ0tqPRaHFxcUFBQUlJSdN5DRLM\nTRDswLSgimzn7u7+5MkTNptdWVn5zjvvSLSVsrGxSU9Pp9PpMP4VAIARCAR0Oj0xMZHFYrHZ\nbDRxtiORSKampjo6Opq+ZAAkQbAD04V4tvPy8prS2dji4uLnz5+bmppKPO7j49PQ0LBo0aKI\niAiJLVcjI6Nz584tX74c5sACADAmJia2trb6+voBAQGTZ7uIiAg4LQGmJwh2YBrBsh2VSg0N\nDZX9q5qamnbv3l1UVLRkyRKJbIfD4YKCgvz9/aUL6erq6hgMxvbt2+F3bgDmuMHBwczMTB6P\nZ2Zmhk19nTzbOTk5LV68WMMXDcAEoEExmPFycnLOnDkjEAgMDAw+++yzSRrXdXZ2zp8/HyHU\n1dX117/+dc2aNdB3CoA5rr29/dNPP+Xz+QghHR2dHTt2hIeHY3/F5/N37drF5/MTExNh8CuY\nKWDFDsx4JSUl8+fPf+WVV4qKihgMhvS6HYbD4Xz44YclJSVMJvPs2bPx8fFxcXHqv1oAwPTx\n/PnzlJSUmJiY1NRUOzs7DodTWFhoYmKCjQUbd90OgGkOgh2Y8fLz8+fNmxcfHz9//vxJsl1v\nb++zZ8+ePXtGJpO3bdu2dOlSjVwtAGD6KCwsHB0dffPNNwkEgp2dnbe3N4PBYDKZ42Y7Dw8P\nOEQPpj/YigUz3tGjR11dXVesWIEQysrKOnLkyEv3ZAEAc1xra+t///tfExMTGo0mPgwDmyHW\n39+PjRTDHuTz+Ww2WzTVGoDpDFbswIy3aNEiIyMjrOTZwcFh8nU7AABACF29evXGjRuNjY2W\nlpaLFi0SPW5iYuLt7V1UVCSxbgd9kcBMAcEOzDAjIyMlJSXV1dWGhoZYmNPS0sL+B2bcbNfb\n23vixAlvb28CgaCxSwcATBuenp7Dw8M1NTUPHz4MDQ3FZg9ixs12AMwUEOzATMLj8VJTUzMz\nM8vKyjIyMubNm0ej0aSfJpHt8Hh8Wlqaq6ur+O/lAIA5bvHixSMjIzwer6KiIigoSE9PT/RX\nomxna2sLZybAzAI1dmDGqKqq+uqrr956663FixdXV1efOHFiaGjohx9+mKiVsajezsTEZOnS\npdCtAADQ2tr65MkTW1tbExMT7JF//vOfFy5csLGx+fzzzyWKN3p7eyUm1gAw/cGKHZgZBgYG\ndu3a9cEHH2DDfOzt7Wk0Wk5Ojqurq62t7bhf4uDgoKenx2azY2NjIdUBMMd1dXXt37//559/\nzsnJuXLlCp/Pd3d319HRwdbtWCxWeXm5xLqdrq6uBi8YAPngNX0BAMikqKhIKBT6+PiIHlmy\nZAkOh6NQKAKBoL+/X/pLent7s7OzobMoAIDP5+/cudPZ2fkf//jHp59+am9vn5WV9eGHHz5+\n/BghlJycvHbt2paWlrS0NIFAoOmLBUAhsGIHZgYmk1lVVbVmzRrRfMba2tqysrK6urqTJ09e\nunTpzp07np6eJBIJ+9ve3t60tLSgoCBIdQDMcWNjY/v27QsMDNy4caORkRGVSo2MjBQIBDwe\nr7S0NDQ0VFdXV3zdDubAghkNgh2YGTo7O1kslp6enpubG0JoaGjoyy+/NDIyio+Pj4iIePr0\nKYfD4XA4kZGR2LlXHo9naGgIqQ4AUFdX99tvv6WmpooGQ+PxeD8/P4FAwOVyOzo6QkJC0O9n\nKWg02pIlSzR6vQAoBH4pATPD0qVLuVxudHQ09sd///vfrq6u27Ztw2Kcr6/voUOHCgoK8vPz\nsU7F/v7+/v7+mrxiAIAmMBgMLpe7Y8cOHA6HPdLe3o4Q6unpEe+LhBDatm3bgwcPiouLHz58\niJXqJicnq/+CAVAuqLEDM4Ouru5f/vIXQ0ND7I+vv/76jh07RE3pcDjc1q1btbW16+vrNXeN\nAADNy87OzsjIOHLkiKjng6WlJULo/PnzEs/E4/FbtmxBCN25c0fNFwmA6sCKHZiOhEIhk8lk\nsVgdHR16enoeHh7R0dHinQikK2CwfsUwagKAOe6TTz754osvsrKyEELvvfceDodzcXGxs7PL\nyclZuHBhRESE+JMdHR2JRCKRSNTQxQKgfLBiB6adxsbG//mf//nxxx+Hh4eNjIwaGxt//fXX\nrVu3pqenT/JVTU1NT58+DQ8PV9t1AgCmIR0dnV27dnl5eWGdLMfGxnA43Pvvv6+trX306NG8\nvDzxJzc0NOBwOC8vLw1dLADKB4cnwPTCZrO//vrrtWvXfvjhh2FhYWFhYatWrSKRSJWVlWw2\ne3R0VDQ9oqurq6qqytraGiHE5/P379//hz/8AaqeAQAEAiEoKKixsZHBYHR1dfn5+ZHJZHt7\neyaTyWQyBwYGXF1dtbW129ravvrqqw0bNnh4eGj6kgFQGpg8AaYRBoNx/PjxTz/91NnZWeKv\n6uvr9+7d29/f/95770VGRo6NjX300Uf19fVUKtXQ0LC5ufn1119fvXq1Ri4bADANjYyMfPHF\nFxUVFStWrMD2ZGtra7/99tvHjx/r6OhYWFjw+fykpKRVq1Zp+koBUCYIdmC6wFLd3r17HRwc\nxn3C3bt309LStLS0Tpw4YWxs/ODBg0uXLj1+/NjGxmblypX29vbqvV4AgOaVlJR0dHS4ubk5\nODjg8ZLFRdLZ7vnz5ywWq6GhwcDAICQkBDtXAcBsAsEOTAtMJvObb76Jj49/6623Jnnaf/7z\nn19++WXjxo3r1q1T27UBAKYn7OeGUChECOnq6rq6urq5ubm7uzs7O4vOQ0hnO41eMgAqBzV2\nYFro6+srLCysrq42MDBwcXGZ6Gl0Ov369etDQ0NYszoAwFyG/dwQCoWRkZFmZmb19fVlZWU5\nOTkXL14sLy9va2t7/vy5qalpeHh4c3NzYWEhVm8H2Q7MbhDswLRgbm7u5ubGYDBKSkomyXZa\nWlo1NTXd3d1xcXFqvkIAwHSD/dwoKipqb2//05/+9Pbbby9dupRGo+np6bW0tJSXlxcUFFy6\ndKm4uJhKpQoEgtra2q6uLmhdDmY3CHZgupAx25WXlwuFQlixAwCg339u5OTk5Obmenp6Ojg4\nODo6BgYGrlq1KjIy0snJydjYuKuri8PhDA4OIoT8/f0XLlyo6asGQIWgxg5MLzU1NXv37h0a\nGtqyZUt8fLz0E/73f//X19cXhsACAESwnxt4PH7fvn3SZ+oRQk+fPq2trR0YGFi+fLn6Lw8A\ndYIVO6AZY2NjXC43IyOjsLDw/v37BgYGJiYm6GXrdvX19ZcvX/7zn/+sp6enoQsHAEw72M+N\nvLy8vLw8T09PMpks8QQdHR0qlUqj0TRyeQCoE6zYAQ1obGw8cuRIS0uLmZlZb2/v8PAwQsjf\n33/btm3YT+Rx1+16e3s/+uijdevWRUZGavLqAQDT0kvX7QCYCyDYAXUrKSn57rvvNmzYEBUV\nRSQShUIhh8P55ZdfmpqazMzM9uzZg3Wkk8h2vb29aWlpQUFBsAkLAJgIZDsAYCsWqFVDQ8PX\nX3+9e/fuoKAgLS0thBAej7eysoqMjOzr66uqqmKz2UuXLiWRSOJ7sng8/vTp05DqAACTe+me\nLACzHgQ7oD4vXrzYu3fvmjVrAgMDJf4Kj8f7+vr29vZWVlY+evRo2bJlSKzejsPhrFy5ElId\nAOClxLPd8uXLSSSSpq8IALWCYAfUp7y8/NatWzt37iQQCOM+YcmSJRUVFVVVVb6+vmZmZuj3\nn9FUKvUPf/iDei8WADBTYT83zM3NfXx8NH0tAKib5GQ9AFSnsrJy/vz5Ojo6Ez2BQCBs3rwZ\nIVRcXCx60N3dPSEhQR3XBwCYLeDnBpizINgBFRodHa2vrxf9sa+vr7e3d/LzOtjv2R0dHaq/\nOgAAAGC2gWAHVOj06dNfffUV1s0EIUQmk58+fVpVVTX5V82fPx/KYgAAAAA5QLADKhQSEtLd\n3X3+/Hnsj76+vgih06dPC4XCSb6qs7NzwYIF6rg+AAAAYHaBYAdUyMXFZdmyZZcuXeLz+Qgh\nV1fXRYsWNTY2/vjjjxN9SWVl5cjISFBQkBovEwAAAJglINgB1UpOTsbj8T///DP2x/fee8/Y\n2PjmzZvHjx+XXrfr6en5+9//vnXrVl1dXbVfKQAAADDjQbsToFokEunFixc3btzw8PCgUCj6\n+vqenp4sFqu6urq8vNzCwoJCoeBwOIQQh8PZv3//ypUro6OjNX3VAAAAwIwEI8WAyo2MjOzY\nsUNfX//bb7/F4/EIIT6ff+TIER6PhxAyNDScP39+d3f32NjY1q1bQ0JCNH29AAAAwEwFwQ4o\n09jYWGtrK0LI2toaW4fDMBiMr7/+evv27TExMaIH79y5U1xc3N7erqent2DBgmXLlsFhWAAA\nAEAREOyA0lRVVR05cgRrQWdsbBwfH//aa69hA2ERQrt27Xr48OGJEyf09fU1epkAAADArAU1\ndkA5uFzuwYMH33zzzc2bN7u5uTU1NRUUFLDZ7CVLlhgYGCCEHBwcrl69Ojw87O3tremLBQAA\nAGYnCHZACYaHh1NTU//0pz8FBwfr6+vb2NhERUUNDg6y2ezCwsLFixebmpqampo+efIkIyMj\nODjY2NhY05cMAAAAzELQ7gQoQVVVVV9fH9Z/GEMgEN5+++3Nmzf39vbu3r0b259NSkoiEokn\nT57U3JUCAAAAsxkEO6AEQ0NDQqFQevn5b70AAAr0SURBVMDr6tWr33rrrZ6env3794+Ojhob\nG2/YsIHD4ZSUlGjkOgEAAIDZDYIdUAJnZ2ccDicaHSZu9erV8fHxDx48uHjxIkIoPj6eSqWe\nOnVq8qliAAAAAJADBDugBObm5r6+vllZWWw2W/pv33rrLTs7u6tXr7548YJAIGzevDksLIxA\nIKj/OgEAAIDZDYIdkMfz589LSkoyMjIePXqEPfL2228TicRDhw7V19dLPJlAIPzxj3/s6+tr\naWlBCPn4+CQmJqr7igEAAIA5AIIdmDIej/fOO+98/vnnx44de/fdd2/cuIEQsrCw+POf/zw8\nPLxnzx7pbOfh4YEQGh0d1cDlAgAAAHMGBDswNeXl5QcPHnznnXfOnz+flpZmYGDw448/Yktx\ngYGB77777sDAQFpamsTxiLa2NkNDQzs7Ow1dNQAAADAnQB87MAX9/f2pqakff/yxt7c3gUCg\nUqmurq6ZmZnW1tbOzs4IITqdbmtry2Kx8vLyenp6HBwcSCRSW1vbwYMH//jHPzo6Omr6HQAA\nAACzmZamLwDMJPn5+bq6ugsXLhQ94ubmRqFQjI2N29ratLS0KBRKcHAwjUb74Ycf0tPTb926\nZWRkNDw8nJycvGLFCg1eOQAAADAXQLADU9DR0dHT09Pf329oaIg98uTJk+7u7nPnzmG7sQsW\nLPjoo4+srKz27NnT0dFRV1eHx+M9PT1h1AQAAACgBrixsTFNXwOYMfLy8v72t78tWrQoJSWF\nRCL19/fv3bvX2Nh43bp1QqHwwoULZWVlNjY23377rY6OjqYvFgAAAJhzINiBKRgbG9u3b195\nebmBgQGNRrt3756Xl9dHH32Ew+Gwvz1w4ACDwdixY8crr7yi6YsFAAAA5hzYigUvMTo6WllZ\n+ezZM3d3d1NT008//TQnJ6e+vt7c3LypqSkxMRFLdQghHA733nvvlZeX37t3T7PXDAAAAMxN\nEOzAZHg83nfffdfV1YUQ0tHRSU5Ojo+PX7FixYoVK2pqagYGBkgkkvjzSSTS/Pnz582bp6Hr\nBQAAAOY06GMHJlRRUXHgwIENGzYcO3Zsy5YteDz+xx9/FDWow7oNZ2Zmin/Jo0ePOjs7w8LC\n1H+1AAAAAIBgB8Y3NDR0+PDhHTt2REVF2djYxMfHHzhwgEgk/vLLL9gTXF1djY2Nz507h02e\nQAjx+fz9+/cnJydTKBTNXTgAAAAwd0GwA+MrKyvr7e0NCAgQPWJvbx8eHt7c3Nzb24sQIhKJ\nH330kY6OzokTJ955553du3e/9957UVFRsbGxmrtqAAAAYE6DGjswPj6fPzY2xufzLS0tRQ/a\n2toihJ48eYL1pfP09Pzuu+8uXbr06NEjCwuLt956C4aGAQAAABoEwQ6MD8twly9f3r59u+hB\nrC+xeIscS0vLHTt2qP/yAAAAACANgh0Yn7e398qVKzds2CD+4LNnzxBCurq6GrooAAAAAEwG\nauzA+HA43LZt20xMTMQffPr0KULIwMBA9Ehvb++xY8ewE7IAAAAA0CxYsQMIISQUCplMJovF\n6ujo0NPT8/DwiI6ONjU1lXhaf38/QkhfXx/7Y29vb1paWlBQkJYW/B8JAAAA0Dz4PAaosbHx\n8OHDfX19zs7ORkZGd+7cqaysvHjx4qZNm2JiYsSfOTIyQiAQCAQCEkt1iYmJGrpwAAAAAPwf\nEOzmOjab/fe///3111+Pjo7GEtvIyMj169d//fXXf/zjH11dXRv/X3t3ExLlFgZw/C1pckKR\nNKVlhJhf5EqiIKRFLYSCxCKIS4sQ27Ro0TKcae0mJZAsaNGujRAUtS0TDLSsEHKoRZRRBE5I\nVDDNXQxXokvSveTX4++3mzNn4Fn+Gd7znr/+Wti8efPmQqFQKBTm5+dVHQCsNsJuXRsdHR0a\nGurr62toaFhYTKVSXV1dra2t2Wz25s2b27dvP3jwYOmr0qnYZ8+eXb16VdUBwGrj8MT6Vaq6\nbDb7Y9UtaGhouHDhQiqVunbtWumNxEmS1NfXJ0ly8eJFVQcAq5CwW6cePnzY399/4MCBnTt3\n/mpPY2Pj8ePHP3/+fO/evdJKa2vr1q1bu7u7VR0ArELCbp2qqqratGnTyMjIrVu3Ftl25MiR\ndDo9MTFR+lhWVjYwMKDqAGB1EnbrVEtLS19fX3l5+fDw8CJtV15e3tTUNDc3t7BSukwMAFiF\nhN369ZttV1lZKeYAYE0Qduva77Tdmzdv2tralnkwAOB/KMtkMis9A0uuWCw+fvz47t279+/f\nf/XqVUVFxcJdYXV1dc3NzaOjo+Pj4xUVFbt27frxhy9evBgZGTl37lw6nV6JwQGA/2BDsVhc\n6RlYWrlcbnBw8PXr19XV1fl8/uvXr0mS7Nmz58yZMzU1NaU9z58/z2azX7586enpOXz4cGkx\nn8+fP3/+2LFjC++xAwBWM2EX3Pj4+KVLl06cOHHo0KHSvRGTk5M3btx4+fJldXV1JpPZsWNH\naedPbefGMABYc4RdZDMzM5lM5qeLJZIkKRQKV65cuXPnTnV1dX9//7Zt20rrC2138uTJBw8e\nqDoAWFs8YxfW9+/fs9ns0aNH9+7d+9NXGzdubG9vz+fzU1NTb9++7ejoKK0vPG83OTnZ2dmp\n6gBgbXFXbFgTExOzs7OLPB7X09MzMzPz6NGjXC5Xuiss+eec7PT0dHd393JNCgD8GV53EtbU\n1FRtbW0qlfrVhrKystOnTydJMjY29uN6S0uLqgOAtUjYhfXp06d8Pr/4M5TNzc11dXXv3r1b\ntqkAgKUj7MKqqamZn59/+vTp4ttqa2u3bNmyPCMBAEtK2IXV3t6eJMn169cLhcIi2z58+NDU\n1LRcQwEAS0jYhdXY2NjW1pbL5YaHh3+1Z2pq6tu3b/v27VvOwQCAJSLsIjt79mxVVdXt27eH\nhob+/b/d3NzcwMBAb29veXn5iowHAPxZXlAcXC6Xy2az+Xy+vr7+1KlTu3fv3rBhQ5Ikk5OT\nly9f7uzs7OrqWukZAYA/Q9jF9/79+8HBwSdPniRJUllZWVtb+/Hjx2Kx2Nvbu3///pWeDgD4\nY4TdejE9PT02NjY7O5tOp5uamjo6OhyGBYBghB0AQBAOTwAABCHsAACCEHYAAEEIOwCAIIQd\nAEAQwg4AIAhhBwAQhLADAAhC2AEABCHsAACCEHYAAEEIOwCAIIQdAEAQwg4AIAhhBwAQhLAD\nAAhC2AEABCHsAACCEHYAAEEIOwCAIIQdAEAQwg4AIAhhBwAQhLADAAhC2AEABCHsAACCEHYA\nAEEIOwCAIIQdAEAQwg4AIAhhBwAQhLADAAhC2AEABCHsAACCEHYAAEEIOwCAIIQdAEAQwg4A\nIAhhBwAQhLADAAhC2AEABCHsAACCEHYAAEEIOwCAIIQdAEAQwg4AIAhhBwAQhLADAAhC2AEA\nBCHsAACCEHYAAEEIOwCAIIQdAEAQwg4AIAhhBwAQhLADAAhC2AEABCHsAACCEHYAAEEIOwCA\nIIQdAEAQwg4AIAhhBwAQhLADAAhC2AEABCHsAACCEHYAAEEIOwCAIIQdAEAQwg4AIAhhBwAQ\nhLADAAhC2AEABCHsAACCEHYAAEEIOwCAIIQdAEAQwg4AIAhhBwAQhLADAAhC2AEABCHsAACC\nEHYAAEEIOwCAIIQdAEAQwg4AIAhhBwAQhLADAAhC2AEABCHsAACCEHYAAEEIOwCAIIQdAEAQ\nwg4AIAhhBwAQhLADAAhC2AEABCHsAACC+BsJe+unZz1RHgAAAABJRU5ErkJggg=="},"metadata":{"image/png":{"width":420,"height":420}}}]},{"cell_type":"markdown","source":["#Correlation network\n","\n","In order to relate all quantitative variables one possibility is a correlation network with library(corrr)\n"],"metadata":{"id":"U9w8Z4aH2CQQ"}},{"cell_type":"code","source":["#install.packages(\"corrr\")\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"VoB7XelO2Bn5","executionInfo":{"status":"ok","timestamp":1717435834168,"user_tz":-120,"elapsed":87728,"user":{"displayName":"toni Monleon","userId":"05269440626861479105"}},"outputId":"26d85a6f-efb6-4af1-a640-af281ab8e3f1"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["Installing package into ‘/usr/local/lib/R/site-library’\n","(as ‘lib’ is unspecified)\n","\n","also installing the dependencies ‘permute’, ‘ca’, ‘gclus’, ‘qap’, ‘registry’, ‘TSP’, ‘vegan’, ‘ggrepel’, ‘seriation’\n","\n","\n"]}]},{"cell_type":"code","source":[" library(corrr)\n"," #head(wine)\n"," correlation.matrix.allvariables <- cor(wine.1[2:14])\n"," #correlation.matrix.allvariables\n"," network_plot(correlation.matrix.allvariables, min_cor = .3)#correlation network with minimum r > 0.3"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":437},"id":"_rFXMEOx2Rgn","executionInfo":{"status":"ok","timestamp":1717435870340,"user_tz":-120,"elapsed":1467,"user":{"displayName":"toni Monleon","userId":"05269440626861479105"}},"outputId":"6da775d9-e639-461b-9215-c6bfb009c379"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["plot without title"],"image/png":"iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAIAAAByhViMAAAACXBIWXMAABJ0AAASdAHeZh94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\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DkmDh8RBw6J6b0YG0cytTb8ZheRSSOf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\n2YH8GKmEnLS7OmpgZ03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JOh5ezevfvG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\nBrDy1Rd+cu6RFx1Qe/FnI7+Yf8LGj1Z0GnWr6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4oDAm+XmXzH9u2QsAIPvDzEfM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\nREREKeL/A0CfLTjjFxstAAAAAElFTkSuQmCC"},"metadata":{"image/png":{"width":420,"height":420}}}]},{"cell_type":"markdown","source":["All the previous section is the basis of the regression analysis, whose basic objective is the prediction"],"metadata":{"id":"ps8f1zyQ1JnH"}},{"cell_type":"markdown","source":["# Simple and Multiple Regression\n","\n","See the example in https://www.r-bloggers.com/2019/05/linear-regression-with-healthcare-data-for-beginners-in-r/\n","\n","and some ideas of\n","\n","http://www.sthda.com/english/articles/40-regression-analysis/168-multiple-linear-regression-in-r/\n","\n","\n","\n","![](https://miro.medium.com/v2/resize:fit:828/format:webp/1*sLGv2RExOFHMVRsUQqsl3g.png)\n"],"metadata":{"id":"yJMSwydu0MLV"}},{"cell_type":"markdown","source":["Regression analysis is used to model the relationship between an input variable called the predictor and an output variable called the response. Today we will use linear regression, which is the simplest of regression analysis and involves a single input variable and a single output variable.\n","\n","see in https://en.wikipedia.org/wiki/Linear_regression that regression is a linear approach to modelling the relationship between a scalar response (dependent variable) and one or more explanatory variables (often called 'predictors', 'covariates', 'independent variables' or 'features'). The case of one explanatory variable is called simple linear regression ; for more than one, the process is called multiple linear regression .\n","\n","Model (simple linear regression): $$y = \\beta _0 + \\beta_1 x$$\n","\n","where $y$ is the outcome and $x$ the predictor, $\\beta_0$ and $\\beta_1$ are the model coefficients\n","\n","\n","\n","1. Ordinary Least Squares (OLS) method is a frequentist approach to modeling in regression\n","2. The idea is minimize a loss function, based only on training data\n","\n","How can I estimate the parameters of the model?\n","\n","\n","\n","![](https://miro.medium.com/v2/resize:fit:750/0*gglavDlTUWKn4Loe)\n","\n","\n","\n","In the simple linear regression model: $y = \\beta _0 + \\beta_1 x$,\n"," model parameters try to minimize the discrepancy between the real data and the expected data (according to the model) for the training data.\n","\n","\n"," Objective : Find $ \\beta_0$, $\\beta_1$, fit a linear function, to:\n","\n"," Minimize $\\Delta y_i$ over all the data with the $L^2$ Norm\n"," $\\Delta y_i$ is the prediction error: $\\Delta y_i = y_i - y_{est}$, where $y_{est}=(\\beta _o + \\beta_1 x_i)$\n","\n","\n","\n","Minimize cost function:\n","\n","\n","$$\\sum\\limits_{i = 1}^n {(\\Delta y_i )^2 = } \\sum\\limits_{i = 1}^n {(y_i - (\\beta_0 + \\beta_1 x))^2 }\n","$$\n","\n","\n","\n","\n"," \n"," \n","Multiple linear regression fits the function:\n","$$y = \\beta_{0} + \\sum\\limits_{j = 1}^p {\\beta_{j} x_j }$$\n","where:\n","$x_{1} ,x_{2} ,...,x_{p}$ are the predictor features,\n","$y$ the response features, and\n","\n","$\\beta_{0}, \\beta_{1} ,...,\\beta_{p}$ the model parameters\n"," \n","Under the constraint:\n","\n","$$RSS = \\sum\\limits_{i = 1}^n {(y_i - (\\beta_{0} + \\sum\\limits_j^p {(\\beta_{j} x_{ji} )} )^2 }$$\n","\n","minimize the residual sum of squares (RSS) over the training data\n","\n"],"metadata":{"id":"6oQ-hBrui7At"}},{"cell_type":"markdown","source":["#Example in healthcare: Vitamine C and Calcium relationship\n","\n","As an example, for this post, I will evaluate the association between vitamin D and calcium in the blood, given that the variable of interest (i.e., calcium levels) is continuous and the linear regression analysis must be used. I will also construct multivariable-adjusted models to account for confounders.\n","\n","It is an example with not too high correlations, and it also serves to illustrate the use of linear models (their interpretability) compared to other current models such as ML, which do not allow their interpretability, but do improve the predictive capacity.\n","\n","\n","See data in: https://wwwn.cdc.gov/nchs/nhanes/Default.aspx\n","\n","![](https://www.mspca.org/wp-content/uploads/2019/01/Carroll-diagram.jpg)\n","\n"],"metadata":{"id":"7KyI_NA4kL5A"}},{"cell_type":"markdown","source":["Let's start loading the packages and data contained in the repository (see https://wwwn.cdc.gov/nchs/nhanes/Default.aspx) of National Center for Health Statistics of the CDC (Centers for Disease Control and Prevention, USA)"],"metadata":{"id":"hVVdulrcnIdO"}},{"cell_type":"code","source":["#install packages\n","#install.packages(\"tidyverse\")\n","#install.packages(\"RNHANES\")\n","#install.packages(\"ggplot2\")\n","\n","\n"],"metadata":{"id":"AcZiRc5s0LT1","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1717436030001,"user_tz":-120,"elapsed":119273,"user":{"displayName":"toni Monleon","userId":"05269440626861479105"}},"outputId":"daf03489-8b2e-4753-a28b-8bbd171f94f6"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["Installing package into ‘/usr/local/lib/R/site-library’\n","(as ‘lib’ is unspecified)\n","\n","Installing package into ‘/usr/local/lib/R/site-library’\n","(as ‘lib’ is unspecified)\n","\n","also installing the dependencies ‘mitools’, ‘RcppArmadillo’, ‘survey’\n","\n","\n","Installing package into ‘/usr/local/lib/R/site-library’\n","(as ‘lib’ is unspecified)\n","\n"]}]},{"cell_type":"code","source":["#install libraries\n","library(tidyverse)\n","library(RNHANES)\n","library(ggplot2)"],"metadata":{"id":"GM749YPSsIhb","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1717495042892,"user_tz":-120,"elapsed":3490,"user":{"displayName":"toni Monleon","userId":"05269440626861479105"}},"outputId":"b626361d-0d01-45c8-ecf0-21d41b5edd85"},"execution_count":4,"outputs":[{"output_type":"stream","name":"stderr","text":["── \u001b[1mAttaching core tidyverse packages\u001b[22m ──────────────────────── tidyverse 2.0.0 ──\n","\u001b[32m✔\u001b[39m \u001b[34mdplyr \u001b[39m 1.1.4 \u001b[32m✔\u001b[39m \u001b[34mreadr \u001b[39m 2.1.5\n","\u001b[32m✔\u001b[39m \u001b[34mforcats \u001b[39m 1.0.0 \u001b[32m✔\u001b[39m \u001b[34mstringr \u001b[39m 1.5.1\n","\u001b[32m✔\u001b[39m \u001b[34mggplot2 \u001b[39m 3.5.1 \u001b[32m✔\u001b[39m \u001b[34mtibble \u001b[39m 3.2.1\n","\u001b[32m✔\u001b[39m \u001b[34mlubridate\u001b[39m 1.9.3 \u001b[32m✔\u001b[39m \u001b[34mtidyr \u001b[39m 1.3.1\n","\u001b[32m✔\u001b[39m \u001b[34mpurrr \u001b[39m 1.0.2 \n","── \u001b[1mConflicts\u001b[22m ────────────────────────────────────────── tidyverse_conflicts() ──\n","\u001b[31m✖\u001b[39m \u001b[34mpurrr\u001b[39m::\u001b[32m%||%()\u001b[39m masks \u001b[34mbase\u001b[39m::%||%()\n","\u001b[31m✖\u001b[39m \u001b[34mdplyr\u001b[39m::\u001b[32mfilter()\u001b[39m masks \u001b[34mstats\u001b[39m::filter()\n","\u001b[31m✖\u001b[39m \u001b[34mdplyr\u001b[39m::\u001b[32mlag()\u001b[39m masks \u001b[34mstats\u001b[39m::lag()\n","\u001b[36mℹ\u001b[39m Use the conflicted package (\u001b[3m\u001b[34m\u001b[39m\u001b[23m) to force all conflicts to become errors\n"]}]},{"cell_type":"markdown","source":["Variables selected for this analysis include:\n","* age,\n","* sex,\n","* plasma levels of vitamin D,\n","* and plasma levels of calcium.\n","\n","All variables are assessed from NHANES 2007 to 2010 wave.\n","\n","age was measured using RIDAGEYR: Age in years, at the time of the screening interview, is reported for survey participants between the ages of 1 and 79 years of age.\n","All responses of participants aged 80 years and older are coded as ‘80.’ The reporting of age in single years for adults 80 years and older was determined to be a disclosure risk.\n","In NHANES 2015-2016, the weighted mean age for participants 80 years and older is 85 years."],"metadata":{"id":"RRm3peevsIXC"}},{"cell_type":"code","source":["#select variables from the repository (age, sex, plasma levels of vitamin D, and plasma levels of calcium. All variables are assessed from NHANES 2007 to 2010 wave.)\n","#data from 2007:\n","d07 = nhanes_load_data(\"DEMO_E\", \"2007-2008\") %>%\n"," select(SEQN, cycle, RIAGENDR, RIDAGEYR) %>%\n"," transmute(SEQN=SEQN, wave=cycle, RIAGENDR, RIDAGEYR) %>%\n"," left_join(nhanes_load_data(\"VID_E\", \"2007-2008\"), by=\"SEQN\") %>%\n"," select(SEQN, wave, RIAGENDR, RIDAGEYR, LBXVIDMS) %>%\n"," transmute(SEQN, wave, RIAGENDR, RIDAGEYR, vitD=LBXVIDMS) %>%\n"," left_join(nhanes_load_data(\"BIOPRO_E\", \"2007-2008\"), by=\"SEQN\") %>%\n"," select(SEQN, wave, RIAGENDR, RIDAGEYR, vitD, LBXSCA) %>%\n"," transmute(SEQN, wave, RIAGENDR, RIDAGEYR, vitD, Calcium = LBXSCA)\n","#data from 2009:\n","d09 = nhanes_load_data(\"DEMO_F\", \"2009-2010\") %>%\n"," select(SEQN, cycle, RIAGENDR, RIDAGEYR) %>%\n"," transmute(SEQN=SEQN, wave=cycle, RIAGENDR, RIDAGEYR) %>%\n"," left_join(nhanes_load_data(\"VID_F\", \"2009-2010\"), by=\"SEQN\") %>%\n"," select(SEQN, wave, RIAGENDR, RIDAGEYR, LBXVIDMS) %>%\n"," transmute(SEQN, wave, RIAGENDR, RIDAGEYR, vitD=LBXVIDMS) %>%\n"," left_join(nhanes_load_data(\"BIOPRO_F\", \"2009-2010\"), by=\"SEQN\") %>%\n"," select(SEQN, wave, RIAGENDR, RIDAGEYR, vitD, LBXSCA) %>%\n"," transmute(SEQN, wave, RIAGENDR, RIDAGEYR, vitD, Calcium = LBXSCA)\n","\n","#bind the two wawes:\n","dat = rbind(d07, d09)\n","#filter, recode variable RIAGEND, remove NA\n","all = dat %>%\n"," # exclude missings\n"," filter(!is.na(vitD), !is.na(Calcium)) %>%\n"," mutate(Gender = recode_factor(RIAGENDR,\n"," `1` = \"Males\",\n"," `2` = \"Females\"))\n"],"metadata":{"id":"03WECIvGsIKy","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1717495124276,"user_tz":-120,"elapsed":55758,"user":{"displayName":"toni Monleon","userId":"05269440626861479105"}},"outputId":"d989184b-55d5-40f7-80c2-fd3ee96e0b37"},"execution_count":5,"outputs":[{"output_type":"stream","name":"stderr","text":["Downloading DEMO_E.XPT to /tmp/RtmpJHSzXZ/DEMO_E.XPT\n","\n","Downloading VID_E.XPT to /tmp/RtmpJHSzXZ/VID_E.XPT\n","\n","Downloading BIOPRO_E.XPT to /tmp/RtmpJHSzXZ/BIOPRO_E.XPT\n","\n","Downloading DEMO_F.XPT to /tmp/RtmpJHSzXZ/DEMO_F.XPT\n","\n","Downloading VID_F.XPT to /tmp/RtmpJHSzXZ/VID_F.XPT\n","\n","Downloading BIOPRO_F.XPT to /tmp/RtmpJHSzXZ/BIOPRO_F.XPT\n","\n"]}]},{"cell_type":"code","source":["head(all) #our work dataset is \"all\"\n"],"metadata":{"id":"WGbxO87ILIGe"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["#dimension and summary\n","dim(all)\n","summary(all) #n=41475"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":293},"id":"KzgsG8MNLRxH","executionInfo":{"status":"ok","timestamp":1717495233511,"user_tz":-120,"elapsed":390,"user":{"displayName":"toni Monleon","userId":"05269440626861479105"}},"outputId":"1fe16d51-47eb-4fc4-8009-b4de50699623"},"execution_count":6,"outputs":[{"output_type":"display_data","data":{"text/html":["\n","
  1. 12391
  2. 7
\n"],"text/markdown":"1. 12391\n2. 7\n\n\n","text/latex":"\\begin{enumerate*}\n\\item 12391\n\\item 7\n\\end{enumerate*}\n","text/plain":["[1] 12391 7"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":[" SEQN wave RIAGENDR RIDAGEYR \n"," Min. :41475 Length:12391 Min. :1.000 Min. :12.00 \n"," 1st Qu.:47225 Class :character 1st Qu.:1.000 1st Qu.:25.00 \n"," Median :52581 Mode :character Median :2.000 Median :44.00 \n"," Mean :52279 Mean :1.505 Mean :44.27 \n"," 3rd Qu.:57378 3rd Qu.:2.000 3rd Qu.:62.00 \n"," Max. :62160 Max. :2.000 Max. :80.00 \n"," vitD Calcium Gender \n"," Min. : 5.41 Min. : 7.000 Males :6134 \n"," 1st Qu.: 43.60 1st Qu.: 9.200 Females:6257 \n"," Median : 60.00 Median : 9.400 \n"," Mean : 61.81 Mean : 9.454 \n"," 3rd Qu.: 76.70 3rd Qu.: 9.700 \n"," Max. :262.00 Max. :12.100 "]},"metadata":{}}]},{"cell_type":"markdown","source":["The dataset is complete (n=12391 rows). Before running the regression analysis, the linear model, I will check the assumption, that the distribution of the dependent variable (levels of calcium) is normal.\n","\n","Distribution of calcium level:"],"metadata":{"id":"FZWA1Kj0p47k"}},{"cell_type":"code","source":["ggplot(data = all) +\n"," geom_histogram(aes(Calcium), binwidth = 0.2,col=\"red\")\n","\n"," #other possibility is do a ks hypothesis test\n"," ks.test(all$Calcium, \"pnorm\", mean=mean(all$Calcium), sd=sd(all$Calcium))\n","\n"," #shapiro.test(all$Calcium) #problem with this, many data!\n","\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":585},"id":"lFVNf6I6o1gM","executionInfo":{"status":"ok","timestamp":1717495272688,"user_tz":-120,"elapsed":821,"user":{"displayName":"toni Monleon","userId":"05269440626861479105"}},"outputId":"02b085d5-3159-4648-88b0-ac9d62d80fe1"},"execution_count":7,"outputs":[{"output_type":"stream","name":"stderr","text":["Warning message in ks.test.default(all$Calcium, \"pnorm\", mean = mean(all$Calcium), :\n","“ties should not be present for the one-sample Kolmogorov-Smirnov test”\n"]},{"output_type":"display_data","data":{"text/plain":["\n","\tAsymptotic one-sample Kolmogorov-Smirnov test\n","\n","data: all$Calcium\n","D = 0.066675, p-value < 2.2e-16\n","alternative hypothesis: two-sided\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["plot without title"],"image/png":"iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAIAAAByhViMAAAACXBIWXMAABJ0AAASdAHeZh94\nAAAgAElEQVR4nO3deZiddX3w//sss53Mlg0ICcaEAmGJAQUUHhZLodQAAResVEXwQojLA1wo\nQigSTK0VAiVKoRoVVK4H0UpACwqPv2oVeLRFyhJFIgG0IZCESeIsme1svz8Gc6UBwiRzTs7M\nZ16vP7gy9xy+87nn3HPOe+6zTKpcLicAAIx96VoPAABAZQg7AIAghB0AQBDCDgAgCGEHABCE\nsAMACELYAQAEIewAAILI1nqAatm8eXOtR3hZY2NjQ0PDli1bCoVCrWcZkVwuNzAwUCwWaz3I\niEyYMCGbzXZ2dtZ6kJFqaWnp7u6u9RQjkkqlWltbC4XCli1baj3LiGQymYaGht7e3loPMiLZ\nbHbChAn9/f0DAwO1nmVEGhoayuXy4OBgrQcZkaE7jp6engA3uX19faVSqdaDjEhzc3M6ne7q\n6qr1IC9Lp9NtbW2v9dmwYTd6fhjK5XI6nS6VSqNnpF2TSqUC7EU6nU6n02N9L5IkCbAXqVQq\nnU6nUqkAO5KMptucXZPJZNLpdDL2dyRJknK5HGAv0ul0gB2JcccxdGM1VvbCQ7EAAEEIOwCA\nIIQdAEAQwg4AIAhhBwAQhLADAAhC2AEABCHsAACCEHYAAEEIOwCAIIQdAEAQwg4AIAhhBwAQ\nhLADAAhC2AEABCHsAACCEHYAAEEIOwCAIIQdAEAQwg4AIAhhBwAQhLADAAhC2AEABCHsAACC\nEHYAAEEIOwCAIIQdAEAQwg4AIAhhBwAQhLADAAhC2AEABCHsAACCyNZ6AIDdKrV5c/3Pf17B\nBdPpdLq+PnX88eXGxgouC7ALhB0wvmSefbblvPMqvmzqscfK06dXfFmAnSLsgPHoqalTfzVj\nRkWWOva552b+8Y8VWQpghIQdMB79fuLEH86ZU5Gl9uvoEHbAKOHFEwAAQQg7AIAghB0AQBDC\nDgAgCGEHABCEsAMACELYAQAEIewAAIIQdgAAQQg7AIAghB0AQBDCDgAgCGEHABCEsAMACELY\nAQAEIewAAIIQdgAAQQg7AIAghB0AQBDCDgAgCGEHABCEsAMACELYAQAEIewAAIIQdgAAQQg7\nAIAghB0AQBDCDgAgCGEHABCEsAMACELYAQAEIewAAIIQdgAAQQg7AIAghB0AQBDCDgAgCGEH\nABCEsAMACELYAQAEIewAAIIQdgAAQQg7AIAghB0AQBDCDgAgCGEHABCEsAMACELYAQAEIewA\nAIIQdgAAQQg7AIAghB0AQBDCDgAgCGEHABCEsAMACELYAQAEIewAAIIQdgAAQQg7AIAghB0A\nQBDCDgAgCGEHABCEsAMACELYAQAEIewAAIIQdgAAQQg7AIAghB0AQBDCDgAgCGEHABCEsAMA\nCELYAQAEIewAAIIQdgAAQQg7AIAghB0AQBDCDgAgCGEHABCEsAMACELYAQAEka31ANVSV1dX\n6xFelslkkiTJZsf8tzqdTmez2VQqVetBRmRo/tFzeIzEWN+LoesilUrt5h3JVOeHsa6urjSW\nr5GhW6p0Oj3Wj6t0Or37D6qKS6fTyZ+ulDEtlUpls9mh3Rm7UqnUqDqodvz9HPO18VoaGhpq\nPcLLhn4y6+rqxvqPaDqdrq+vL5VKtR5kRIZiYvQcHrsslUoF2IskSdLp9G7ekXR9fTWWra+v\nL4/la2ToriLAr6DZbLZcLo/1X0GH7i8C3OQO3XGUy+VaDzIiY+uOY8z/DL+Wnp6eWo/wslwu\nl81m+/r68vl8rWcZkdbW1t7e3kKhUOtBRqS9vT2dTo+ew2OX1dfXj/W9SKVSjY2NxWJxN+9I\ntre3GmW3ZcuW0li+Rurr6+vr6wcHB3t7e2s9y4jkcrlSqdTf31/rQUZkwoQJMe442traent7\ni8VirQcZkfb29lQqNXpucjOZTGNj42t9dmyfHQUAYCthBwAQhLADAAhC2AEABCHsAACCEHYA\nAEEIOwCAIMK+jx0QRsOdd6bXrq3UahVcCmC0EXbAaNd46611//EftZ4CYAwQdsDY8MVjjilV\n4u9EHbx+/V/+7ncjXwdgFBJ2wNjwq+nTi5X4U+ITBgdHvgjA6OTFEwAAQQg7AIAghB0AQBDC\nDgAgCGEHABCEsAMACELYAQAEIewAAIIQdgAAQQg7AIAghB0AQBDCDgAgCGEHABCEsAMACELY\nAQAEIewAAIIQdgAAQQg7AIAghB0AQBDCDgAgiGytBwAY29r7+5Mkafw//6fc3l6pNYv77jv4\nF39RqdWA8UPYAYzI1C1bkiTJLV1awTUHFiwQdsAuEHYAFfCNt7ylp6Fh5Os0Dwyc88gjI18H\nGJ+EHUAF/NeMGR253MjXmdLbK+yAXebFEwAAQQg7AIAghB0AQBDCDgAgCGEHABCEsAMACELY\nAQAEIewAAIIQdgAAQQg7AIAghB0AQBDCDgAgCGEHABCEsAMACELYAQAEIewAAIIQdgAAQQg7\nAIAghB0AQBDCDgAgCGEHABCEsAMACELYAQAEIewAAIIQdgAAQQg7AIAghB0AQBDCDgAgCGEH\nABCEsAMACELYAQAEIewAAIIQdgAAQQg7AIAghB0AQBDCDgAgCGEHABCEsAMACELYAQAEIewA\nAIIQdgAAQQg7AIAghB0AQBDCDgAgCGEHABCEsAMACELYAQAEIewAAIIQdgAAQQg7AIAghB0A\nQBDCDgAgCGEHABCEsAMACELYAQAEIewAAIIQdgAAQQg7AIAghB0AQBDCDgAgCGEHABCEsAMA\nCELYAQAEIewAAIIQdgAAQQg7AIAghB0AQBDCDgAgCGEHABCEsAMACELYAQAEIewAAIIQdgAA\nQQg7AIAghB0AQBDCDgAgCGEHABCEsAMACELYAQAEIewAAIIQdgAAQQg7AIAghB0AQBDZan+B\nTZs23XLLLY8//vjg4ODs2bPPPffc/fffP0mSnp6e5cuXP/HEE/l8/oADDli4cOEee+yxC9sB\nABhS9TN2n/vc5zo6Oj772c8uW7ZsypQpS5Ys6e/vT5Jk2bJlGzZsWLx48dKlS3O53JIlS0ql\n0i5sBwBgSHXDrru7e+rUqR//+Mdnz549bdq0s88+u6ura82aNR0dHQ8//PD5558/a9asvffe\ne+HChWvXrl25cuXObq/q8AAAY0t1H4ptaWlZtGjR1g83btyYTqenTJny1FNP1dXVzZo1a2h7\nc3PzjBkzVq1a1dvbu1Pb582bV9X5AQDGkKo/x26r7u7uG2+88Ywzzpg4cWJXV1dLS0sqldr6\n2ba2ts7Ozra2tp3avu36N998c7FYHPr33Llz3/rWt1Z5h4arrq4uSZLGxsb6+vpazzIimUym\nqalprD8Cnk6nkySZMGFCrQcZqVQqFWAvkiTJZDKvuyOZTGb3DDN6ZLPZ3Xz9Dn2T6+rqxvpx\nlc1my+XyWD9mgt1xlMvlWg8yIul0egzd5O6msHv++ef/7u/+7tBDD/3Qhz40tGXbStvWzm7f\n6lvf+lahUBj695lnnvn2t799F2etjoaGhlqPUAFj/bZyq6amplqPUAEx9iKdTr/+jqTH3ev3\nh+4Od//XraurG0oKRoMYdxyNjY21HqEyRs9N7o7PsOyOsHv88cevvfbas84669RTTx3a0t7e\n3tXVVS6Xt+ZaZ2fnxIkTd3b7tl/lG9/4xtbfCSZOnPjHP/5xN+zacDQ2NjY2Nvb09GztzjFq\nwoQJ/f39W0+LjlHNzc3ZbHb0HB67rLW1taurq9ZTjEgqlWpraysUCj09PTu+ZHOhsPseXBgd\n8vn8lt17lA6dq+vv7x96fdvY1djYWCqVBgcHaz3IiDQ1NTU0NAS442hubu7t7R3rD/W0tLRk\nMpnRc8eRTqdbW1tf67NVv7V88sknr7nmmk9+8pNvectbtm7cb7/98vn8M88882d/9mdJkgy9\nouLAAw+cNm3aTm3f9gvNmTNn2w87OjqqvWvDNHRAF4vFsf7zWS6XA+zFEHsxGgz9nlYul193\nR8b64zi7oFQq7ebrd+hZCrv/61ZcqVSKsRdJoDuOsX5GoFwuD+eWarfZ8aNn1X2AY3BwcNmy\nZQsWLJg5c2bHn/T390+aNOmoo4666aabnnvuubVr195www377rvvQQcdtLPbqzo8AMDYUt0z\ndr/97W/XrVt3++2333777Vs3XnDBBaeccsqFF164fPnyq6++ulgsHnzwwVdeeeXQr+87ux0A\ngCHVDbt58+b94Ac/eNVP5XK5iy++eOTbAQAYMu5eawYAEJWwAwAIQtgBAAQh7AAAghB2AABB\nCDsAgCCEHQBAEMIOACAIYQcAEISwAwAIQtgBAAQh7AAAghB2AABBCDsAgCCEHQBAEMIOACAI\nYQcAEISwAwAIQtgBAAQh7AAAghB2AABBCDsAgCCEHQBAEMIOACAIYQcAEISwAwAIQtgBAAQh\n7AAAghB2AABBCDsAgCCEHQBAEMIOACAIYQcAEISwAwAIQtgBAAQh7AAAghB2AABBCDsAgCCE\nHQBAEMIOACAIYQcAEISwAwAIQtgBAAQh7AAAghB2AABBCDsAgCCEHQBAEMIOACAIYQcAEISw\nAwAIQtgBAAQh7AAAghB2AABBCDsAgCCEHQBAEMIOACAIYQcAEISwAwAIQtgBAAQh7AAAghB2\nAABBCDsAgCCEHQBAEMIOACAIYQcAEISwAwAIQtgBAAQh7AAAghB2AABBCDsAgCCEHQBAEMIO\nACAIYQcAEISwAwAIQtgBAAQh7AAAghB2AABBCDsAgCCEHQBAEMIOACAIYQcAEISwAwAIQtgB\nAAQh7AAAghB2AABBCDsAgCCEHQBAEMIOACAIYQcAEISwAwAIQtgBAAQh7AAAghB2AABBCDsA\ngCCEHQBAEMIOACAIYQcAEISwAwAIQtgBAAQh7AAAghB2AABBCDsAgCCEHQBAEMIOACAIYQcA\nEISwAwAIQtgBAAQh7AAAghB2AABBCDsAgCCEHQBAEMIOACAIYQcAEISwAwAIQtgBAAQh7AAA\nghB2AABBCDsAgCCEHQBAENnhXOjwww+/7bbbDjzwwO2233nnnZ/5zGeefPLJKgw2UhMnTqz1\nCC9Lp9NJkrS0tJTL5VrPMiLpdLq1tTXAXiSj6fDYZel0OsBeJEmSzWZfd0fS2WHdUkVSX1+/\nm6/fVCqVJEljY2NDQ8Pu/LoVN7QjTU1NtR5kRILdcdR6ipFKp9OpVGr03OTu+KgY1s3lI488\nsmXLlu02FgqF3/zmN88888yuj1ZNmzdvrvUIL8vlcrlcrru7O5/P13qWEWltbe3t7S0UCrUe\nZETa29uz2ezoOTx22aRJk8b6XqRSqcmTJxcKhc7Ozh1fsq1QqNs9M40ag4OD3bv3+q2vr29t\nbe3v7+/t7d2dX7ficrlcqVTq7++v9SAjMmHChKampgB3HG1tbT09PcVisdaDjEh7e3smkxk9\nN7mZTGYHlfk6YTf0q0+SJEccccSrXuDNb37zLk8GAEAFvU7YPfbYYz/72c8uuuii008/fcqU\nKdt+KpVK7b333h/5yEeqOR4AAMP1OmE3b968efPm/fCHP1y6dOl+++23e2YCAGAXDOs5dvfd\nd1+15wAAYISG9XYnGzZsOOecc6ZPn57JZFKvUO0RAQAYjmGdsfvEJz5x1113HX/88SeddFJ2\n/L3vAADAmDCsSvvJT37yve997/TTT6/2NAAA7LJhPRTb19d39NFHV3sUAABGYlhh95a3vOU3\nv/lNtUcBAGAkhhV2N9xww2WXXfaLX/yi2tMAALDLhvUcu4suuujFF188+uijc7nc1KlTt/vs\n73//+8rPBQDAThpW2KXT6f3333///fev9jQAAOyyYYXdz3/+82rPAQDACA3rOXYAAIx+wzpj\nN2XKlNf61ODgYFdXV+XmAQBgFw0r7I455pjttrz44osrV67cd999jz/++CpMBQDAThtW2N19\n992v3Lhu3bq//uu/fsc73lHpkQAA2BW7/hy7vfba6/rrr1+8eHEFpwEAYJeN6MUTM2bMePLJ\nJys1CgAAI7HrYVcul2+55ZbJkydXcBoAAHbZsJ5jd+ihh263pVgsrlu3rqOj41Of+lQVpgIA\nYKcNK+xeqa6u7k1vetPpp5++cOHCyg4EAMCuGVbYPfbYY9WeAwCAEdqJM3YbN2785S9/+cIL\nL6TT6RkzZhx99NEtLS3VmwwYo7K//nXjV7/6uhdLpVJJQ0OmVGoeHNzxJTPPPluh0QCCG1bY\nlUqlT3/601/60pfy+fzWjRMmTFi8ePGll15atdmAMSm9Zk3j7bcP98JJ0ljVaQDGk2GF3fXX\nX3/99de/853vPPXUU6dNm1YqldauXbtixYpPf/rTe+6559lnn13tKYEx50cHHPDTffetyFJL\n/u//bSwUKrLU6Nfa358kSf2//dvEI46o4LLd3/xm4aCDKrggMDoNK+xuvfXWSy655Prrr992\n4/nnn3/BBRd88YtfFHbAK3U1Nj7f1laRpcqpVEXWGRMy5XKSJMW+vv4XX6zIgg2FQl2xmPT3\nV2Q1YJQbVtg9++yzp5xyyiu3n3766bfddlulRwIY7x6ZMeOL/+t/VWSpDz3yyMm/+11FlgJG\nv2G9QXE2m+3t7X3l9nw+n8lkKj0SAAC7Ylhhd9hhh/3jP/7j4P985Vp/f//NN998+OGHV2cw\nAAB2zrAeil20aNGpp5663377zZ8/f/r06eVyec2aNffee++6devuv//+ao8IAMBwDCvs5s+f\nv2LFikWLFn35y1/eunHu3Llf/epXTzzxxKrNBgDAThjuGxSfccYZZ5xxxgsvvLB27dpUKrXP\nPvvsueeeVZ0MAICdMqzn2CVJsm7duhtvvHHvvfc+4ogjDj/88HQ6vWTJkg0bNlR1OAAAhm9Y\nYbdq1arDDjvsU5/61NYtvb29ixcvnjdv3rP+1A8AwOgwrLC7/PLLm5ubH3zwwa1bZs6c+eST\nTzY3N/uTYgAAo8Swwu6hhx664oorjviff9/mwAMPvPTSS3/84x9XZzAAAHbOsMKup6envr7+\nldubm5uLxWKlRwIAYFcM9w2Kb7vttu0arru7e9myZYcddlh1BgMAYOcM6+1Orrrqqne84x37\n77//O97xjqlTp5ZKpTVr1txzzz0bN2784Q9/WO0RAQAYjmGF3cknn3z//fcvWrTopptu2rrx\nTW960ze+8Y2TTz65arMBALAThvsGxSeddNJJJ520cePGF154IZPJ7LPPPi0tLVWdDACAnTLc\nsBsyefLkyZMnV2kUAABGYrh/eQIAgFFO2AEABCHsAACCEHYAAEEIOwCAIIQdAEAQwg4AIAhh\nBwAQhLADAAhC2AEABCHsAACCEHYAAEEIOwCAIIQdAEAQwg4AIAhhBwAQhLADAAhC2AEABCHs\nAACCEHYAAEEIOwCAIIQdAEAQwg4AIAhhBwAQhLADAAhC2AEABCHsAACCEHYAAEEIOwCAIIQd\nAEAQwg4AIAhhBwAQhLADAAhC2AEABCHsAACCEHYAAEEIOwCAIIQdAEAQwg4AIAhhBwAQhLAD\nAAhC2AEABCHsAACCEHYAAEEIOwCAIIQdAEAQwg4AIAhhBwAQhLADAAhC2AEABCHsAACCEHYA\nAEEIOwCAIIQdAEAQwg4AIAhhBwAQhLADAAhC2AEABCHsAACCEHYAAEEIOwCAIIQdAEAQwg4A\nIAhhBwAQhLADAAhC2AEABCHsAACCEHYAAEEIOwCAIIQdAEAQwg4AIAhhBwAQhLADAAhC2AEA\nBCHsAACCEHYAAEFkd8PXWLt27Q033LB69eq7775768aenp7ly5c/8cQT+Xz+gAMOWLhw4R57\n7LEL2wEAGFL1M3YPPPDAFVdcMWPGjO22L1u2bMOGDYsXL166dGkul1uyZEmpVNqF7QAADKl6\n2OXz+euuu+5tb3vbths7Ojoefvjh888/f9asWXvvvffChQvXrl27cuXKnd1e7eEBAMaQqj8U\ne8IJJyRJ8swzz2y78emnn66rq5s1a9bQh83NzTNmzFi1alVvb+9ObZ83b97WNZ966qlyuTz0\n74kTJzY2NlZ714YpnU4nSZLJZLaON0alUqlMJlPrKSojm90dT0KotlG7F2GOk0gymUyywwNm\n6FpLp9Oj9rgapqGb3Bh7EeaOI5VK1XqQEUmlUqlUavQcVEOHx2upzZRdXV0tLS3bXtNtbW2d\nnZ1tbW07tX3bNc8555xCoTD07zPPPPOyyy6r8k7snObm5lqPUAF1dXW1HqEy2tvbaz1CBYze\nvZgwodYTsL2WlpZkGAdMY2Pj6PmteCRyuVytR6iAGHccra2ttR6hMkbPTe6On4pWs/x8rX7f\n2e1bnX322cVicejfc+fO7evrG8l4FVRXV5fNZgcGBsb6kwLr6+sLhcJY34uGhoZ0Oj16Do9d\n1tjY2N/fX+spXl1mcLC+1jOwnYGBgdIOD/tMJlNfX5/P57f+hjxGZbPZcrm89e5gjApzx9HQ\n0DA4ODjWzzuOwjuOpqam1/pUbcKuvb29q6urXC5vzbXOzs6JEyfu7PZt1/zYxz627YcdHR3V\n349hyeVy2Wy2v78/n8/XepYRyWQyfX19Y/1Gv66uLp1Ob9mypdaDjFRDQ8Oo3Yv6/n5hN9r0\n9fUVdnjA1NfXD4Vdb2/vbpuqGnK5XKlUGrW/9gzThAkTYtxxZLPZvr6+AJ2dSqVGz01uJpPZ\nQdjV5n3s9ttvv3w+v/WJd11dXWvWrDnwwAN3dntNhgcAGJ2qHnabN2/u6Ojo7u5OkqSjo6Oj\no6O/v3/SpElHHXXUTTfd9Nxzzw29y92+++570EEH7ez2ag8PADCGVP2h2EsvvXTDhg1D//7w\nhz+cJMl55523YMGCCy+8cPny5VdffXWxWDz44IOvvPLKoYdZd3Y7AABDqh52X/va1151ey6X\nu/jii0e+HQCAIf5WLABAEMIOACAIYQcAEISwAwAIQtgBAAQh7AAAghB2AABBCDsAgCCEHQBA\nEMIOACAIYQcAEISwAwAIQtgBAAQh7AAAghB2AABBCDsAgCCEHQBAEMIOACAIYQcAEISwAwAI\nQtgBAAQh7AAAghB2AABBCDsAgCCEHQBAEMIOACAIYQcAEISwAwAIQtgBAAQh7AAAghB2AABB\nCDsAgCCEHQBAEMIOACAIYQcAEISwAwAIQtgBAAQh7AAAghB2AABBCDsAgCCEHQBAEMIOACAI\nYQcAEISwAwAIQtgBAAQh7AAAghB2AABBCDsAgCCEHQBAEMIOACAIYQcAEISwAwAIQtgBAAQh\n7AAAghB2AABBCDsAgCCEHQBAEMIOACAIYQcAEISwAwAIQtgBAAQh7AAAghB2AABBCDsAgCCE\nHQBAEMIOACAIYQcAEISwAwAIQtgBAAQh7AAAghB2AABBCDsAgCCEHQBAEMIOACAIYQcAEISw\nAwAIQtgBAAQh7AAAghB2AABBCDsAgCCEHQBAEMIOACAIYQcAEISwAwAIQtgBAAQh7AAAgsjW\negAAqmiv7u4kSXLLlpUmT97BxTKZTFJXV18opAuF112zOHt23//+3xUbEagcYQck6ZdeSoZx\ndz7c1TZvrtRSjFx7X1+SJPU/+tFwLpwd3r1C/q1vFXYwOgk7IGmbPz/z+9/Xegqq6Kajj/7v\ntraRr5Mplz9/330jXweoEmEHJEmSFNLpR6dPr8hS0zs79+7qqshSVMr65uY17e0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is a normal distribution (non normal in the ks test, but a lot of data n>30--> we can consider normality).\n","\n","Note: If the distribution is not normal, the dependant variable should be log transform by using log(Calcium)"],"metadata":{"id":"Wr9PkZNmo1CV"}},{"cell_type":"markdown","source":["**The predictive model**\n","\n","I will use the function lm() to create a linear regression model in order to predict calcium levels in function of vitD. In the first model I will not adjust for confunders, insted, I will do a univariate model."],"metadata":{"id":"ArEtl7t5sHwD"}},{"cell_type":"code","source":["#Linear model to predict Calcium ~VitD\n","fit1 <- lm(Calcium ~ vitD, data = all)"],"metadata":{"id":"2__mYeSsqPb3","executionInfo":{"status":"ok","timestamp":1717495293495,"user_tz":-120,"elapsed":348,"user":{"displayName":"toni Monleon","userId":"05269440626861479105"}}},"execution_count":8,"outputs":[]},{"cell_type":"markdown","source":["To see the results, estimates, p-values etc, use summary function."],"metadata":{"id":"eBRfHcTYqVbd"}},{"cell_type":"code","source":["summary(fit1)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":349},"id":"f7tsxF2rqQy7","executionInfo":{"status":"ok","timestamp":1717495296986,"user_tz":-120,"elapsed":804,"user":{"displayName":"toni Monleon","userId":"05269440626861479105"}},"outputId":"2f0ac501-282c-45ee-90b0-002a11c54ae0"},"execution_count":9,"outputs":[{"output_type":"display_data","data":{"text/plain":["\n","Call:\n","lm(formula = Calcium ~ vitD, data = all)\n","\n","Residuals:\n"," Min 1Q Median 3Q Max \n","-2.51254 -0.23398 -0.00581 0.22943 2.64876 \n","\n","Coefficients:\n"," Estimate Std. Error t value Pr(>|t|) \n","(Intercept) 9.3517792 0.0087769 1065.50 <2e-16 ***\n","vitD 0.0016522 0.0001315 12.56 <2e-16 ***\n","---\n","Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n","\n","Residual standard error: 0.3683 on 12389 degrees of freedom\n","Multiple R-squared: 0.01258,\tAdjusted R-squared: 0.0125 \n","F-statistic: 157.8 on 1 and 12389 DF, p-value: < 2.2e-16\n"]},"metadata":{}}]},{"cell_type":"markdown","source":["You can see the parameter estimation (ej $\\beta$ for VitB = 0.0016, positive and p-value < 0.05, significative)"],"metadata":{"id":"bH-p5QXK5y_E"}},{"cell_type":"markdown","source":["The 95% confidence interval for the parameters estimations:"],"metadata":{"id":"-kZT_dpmqQmX"}},{"cell_type":"code","source":["confint(fit1)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":129},"id":"mQRkKQnLqQYL","executionInfo":{"status":"ok","timestamp":1717495321707,"user_tz":-120,"elapsed":535,"user":{"displayName":"toni Monleon","userId":"05269440626861479105"}},"outputId":"acb94edf-cfa1-4694-d5ee-22388bf6dca7"},"execution_count":10,"outputs":[{"output_type":"display_data","data":{"text/html":["\n","\n","\n","\t\n","\n","\n","\t\n","\t\n","\n","
A matrix: 2 × 2 of type dbl
2.5 %97.5 %
(Intercept)9.3345751259.368983370
vitD0.0013944040.001910026
\n"],"text/markdown":"\nA matrix: 2 × 2 of type dbl\n\n| | 2.5 % | 97.5 % |\n|---|---|---|\n| (Intercept) | 9.334575125 | 9.368983370 |\n| vitD | 0.001394404 | 0.001910026 |\n\n","text/latex":"A matrix: 2 × 2 of type dbl\n\\begin{tabular}{r|ll}\n & 2.5 \\% & 97.5 \\%\\\\\n\\hline\n\t(Intercept) & 9.334575125 & 9.368983370\\\\\n\tvitD & 0.001394404 & 0.001910026\\\\\n\\end{tabular}\n","text/plain":[" 2.5 % 97.5 % \n","(Intercept) 9.334575125 9.368983370\n","vitD 0.001394404 0.001910026"]},"metadata":{}}]},{"cell_type":"markdown","source":["# Intepretation\n","\n","From the results, I find that vitamin D is associated with calcium in the blood because the p-value is less than 0.05. Next, I see the direction of the association. The positive beta estimate ($\\beta$ = 0.0016522) indicate that with increasing vitamin D in the blood, the levels of calcium also increases.\n","\n","The principal metric measure (perfomance of the model, see in https://www.analyticsvidhya.com/blog/2021/05/know-the-best-evaluation-metrics-for-your-regression-model/) is R2 = 0.0125, very low but the model is significative (explain the response variable) p=2.2e-16\n","\n","To visualize this association I will use the ggplot and the function geom_smooth. See below"],"metadata":{"id":"Tsu5KETMqQFL"}},{"cell_type":"code","source":["ggplot(all, aes(x = vitD, y = Calcium)) +\n"," geom_point() +\n"," geom_smooth(method=\"lm\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":455},"id":"UFiick7Fr-tG","executionInfo":{"status":"ok","timestamp":1717495327093,"user_tz":-120,"elapsed":1485,"user":{"displayName":"toni Monleon","userId":"05269440626861479105"}},"outputId":"e2275bd1-05bc-4de9-ece8-aaf91db342ef"},"execution_count":11,"outputs":[{"output_type":"stream","name":"stderr","text":["\u001b[1m\u001b[22m`geom_smooth()` using formula = 'y ~ x'\n"]},{"output_type":"display_data","data":{"text/plain":["plot without 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GDJ2OZOAEAvKyurkydPXr58+fHjx7a2tt27d3d3dzd3UgAAAJYL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\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ffYsWM1I9bW1pcuXfrpp58OHjyoc0c/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\nDGOVhdYAAAAASUVORK5CYII="},"metadata":{"image/png":{"width":420,"height":420}}}]},{"cell_type":"code","source":["#You can visually explore the fitting curve (perfomance of the prediction of Calcium in function of VitD):\n","\n","plot(all$vitD,all$Calcium,xlab=\"vitD\",ylab=\"Calcium\",\n"," main=\"Fitted model with residual lines\")\n","lines(all$vitD,fitted(fit1))\n","segments(all$vitD,fitted(fit1),all$vitD,all$Calcium,lty=2)\n","\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":437},"id":"k24sLTiOuCCq","executionInfo":{"status":"ok","timestamp":1717495356353,"user_tz":-120,"elapsed":2237,"user":{"displayName":"toni Monleon","userId":"05269440626861479105"}},"outputId":"f658269e-8dd9-4215-97e8-47757291cff8"},"execution_count":12,"outputs":[{"output_type":"display_data","data":{"text/plain":["Plot with title “Fitted model with residual lines”"],"image/png":"iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAMAAADKOT/pAAADAFBMVEUAAAABAQECAgIDAwME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\n5d2LRHZRkSh5i88nROhf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7kWPN3+Dj8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d4aToSdJZAPBR7rJFjX9DaE1LqZXy0Ak\nz58Q2t4AOa6ztcU50G9N0bwgFXm/5urG+helvU95JIxsg4qUTWSq+VsQaSEJjoPhF8FKGQKD\noZyHA4qBNtCihN0OdiJI2W9YKDwuVA3O6mEOCv/OZ/D9vMpevR6V+hoKo22q0MkKBS7E2bKs\nXR38ox7LOMn4vSg+7gnf8fv1UAZqj1hmzePKkyDCx1f61oqI4ADSKMAxbL1vQWixKEIChEKx\nW+UArGXgA6BycGBhmDsL3YE8ii6R8LVXoyn+cBBF92oonH7PpkFKYUZRd0Ql5Od1GJij+JOq\n5kiAuyQm6EHtGooTL6LW26LAon0Q2i9H6ho+JGKIRDYQ/Jh1tqgorjbNL2R6tQxECp6Fr3Ag\ncl0vcVuA0Ilx6CdX3RgrQ5K/tiNHoJEN2UTmRbpICilMxVgHzjYE6ki6MtVQvPUolacDe7br\nlZ4A57WDJGslrFO4Nkip5laMLwDwDcPcR4NclT3RU75hIMftRgm4aOekblyvcy9w4Lnu9vXr\n+mJz6uJ8DGoN5Bk1gBAud2oSLroJTXfNeV9Q4RkpfI1FCsH+lABcwsNGseWExjp/BthCcob/\nXRYAZwYzLM+Ko+yVCS+G/661Fiotq6xlZHxj9IANLzIStWcfs9y/6MRaaFdIph5bNd5TNoRT\nSSIOQet6l5l+3jFojwSpywDpV9aLVEwySBSpeifTq2UgUo+KOI3t6PJ6me4dtzdAJjROxAWM\nM+ufiZJEfhMJ1Sv2ECnY+eFQdnyguvNYJvAJikUPZapypZ+iuyxf2+Nd2dJS5gdezqp5pqHC\nQcGwqvb4PgP24uVTTR0ZVw8lp2aB54SIBmCkhSK4MuE8LoLVscFulB5InoYVm7ntWIZ0w4KV\nECLkIVjVqB3AELzMkQF7vNDKeWzFiowq8OTfexhrJSMZ6Md43fRQt/XkJFsRShgplZKRUz/5\n1yU38hOlTSu/f9GBsl6s/EqLkbwK6/qfloQ0qTxVEG1fkLha/hBwW0MjX2i7xGCRrNSCkHqR\nilex0WCREiZLL5leLYPIhquycSQDuFxgwD/SyWTBRbk9qW/Gfm2bbPh9SgbJVyIdLHcXvSij\naTe0MiPxY53YoqVks8Jt2o/vUVBr6+2g8JY54Pu9qh++v5WOID7KwChIEziLMwv8ibVidX1B\n5BlWsdjG2RRg7VhNVaEfCS+zJs+OVwd99A+vCxDihbyJ03VCKXjgbSR4ecPRA0OVC/w5eWi4\nFBh38kQ7xzAFnL2r8Ix3CS3Pktr+x+hdPpqKZTtpfdxdnaEgSDi5LBAcS5GMj/QbQRNt6SZQ\nS1HADnwYzhbKWJd8hk44+dTj2jI2QqNdokjTykNj16rFVRtQWmy2KtZ/bBNZ409og7L4wDGN\npY0rWrUe/bWvrf6Z2WUNzfv3ouRlkVKJbIj9uVO5Zt/0rxNSJKTOsGsoZkWHcs2/f/l0cmM/\nv0LNJ7XyVHjUH1NGqpEWVLFEGrbqlGJ42vWuHN/3mq/I0AikjBZZ35Yootpyu7A94xSsi2xw\nA2sNaZV7t1LIkYJtAKvJcHz7qU2lWiv/cA+1Wm3j1vjwpx+rWMmUwfN39qhYb+Qt9HF+aUeH\n0EGVC6jt7O00zgEt5z24Od5fZuXa9awusuEgKKHJog9v59QCL8ZJuexWV1D1CPW1BinDB4mR\nDT0ltm2gF6/0gI4/kUa6Z1MimE5y8XlDg8iG0T1K1Rl1O51Ld3d0g4juwugOt0dVDeyxHcX9\n0kVasW6Yfj2NbDCbPCySUWTD0391n/67Icwk3Di/9zX6dOwiLsQ8eLb60tvXm8IPVPg9cDYH\nG9EWNbjC3uZanrNibM5cmwsgX7FJigtzLgNxeU2Bp3YrYwdzwDh63fi5RjhXOKxgBEAlV3CG\naoOlbAB3aX+gJ1fzzZ6FI/4ds+jDsUMH93w8vuF17O6o1UJE9acr5/edX7r1ZsK5XWcOHtr3\n/PBl9PLU9l3/7Bx0e0qJ/m/V3oULkPxOU+XawSv74XLVavAzPvRfZNd/4C9ppP3qExvBL7pa\n94Fw2rHkTnhiF7ofXtuWOAIx1qUPQfwNsQT2Z5GYmjPIaGNPkyIbXl2NRejltejrYm798ppJ\nc1jsNbHf6Wg//ZgNc8QHIZFmS9KQxYJIT25EX3mDUif6fymHaGUCGtmQTZjZIRs70RVA2eIe\nihnngmda33nUhtcH/7ARDyTO+tY1UqOBwN5q0yih5PCprRD7UjG2yhTXy7cfL5fmwZmU98PY\nDHj7ojkYhKCL2xtswOP6kp0Drio5jf60MgDnnFXO3muJj+cyQehISljiCyAN8hA2dp8WO98b\nf6wjPp8nRDZcqYsLoF7z8I/LwgD9g32zUxTp0xjxogWm9vz64bI8sMU2p7LWTGjzdzZhnkhx\ndbXz/7mztZzTHnftwkt3tpRm7ZUgtSX9okFbm+Eb1wvXfspxjD0wkinkUfAuKnx7aqTi0Fus\n4W0rS7x3WTkvzBH3GMaFSMnIeQdhDSOV8WStwuBmF44SGETWK001FT6K0XZimgrjteLwXV4q\n92J+fnCaVIu4ssJenuSEpOpwBmRcwSqclTT4+kFbSZHrlxe7eMnHnHl4oLnUruzWO/8scKxD\nftL7qqLOPmjA8VZlJ1ZXDLDzVk8992B3bSvSoyy02p1V19r94Nw0ded0RSpTw7m/pN74UG0/\n2ahk15uwge985OHJwXwqQ0abCRUpmzAvsmGZ+GLI2KqOsJzMxICM7eblwYFcCufQKoBI0DxA\nI0ACHL+NBRVTSobvXYWV8JvLQxtyh38n3tTkllZZ9yajkpSXt8ZSBDFqkEMFNhivcHv/B2ND\nGiJWPXRxFwes207Mw8uE1geWZWy/1tkh0WvCgi4SVqLC9a/S5MFZcND5ai1KjLMZDU7Zqla4\nWnuBPQ9cGNOojTYcNEPtRkJZK9jIbdTCn85TAiUXHKc3LvevA757t4I4lnlBuXClrlgvJW9+\nGnz+YhWulI0KFzA7BiwGIbwuoW0waaPDIiUUbSV015yS7EhHpGl+dlfcBuK7o0KD7exfKVz6\nl3bie9VWSf9NYa3ZUJGyCfMiGyro3pF0ChjhcaQX+K5956XFtRoF9ELrOfDFE7RD+OnfwEJb\nX44Fa6yQvZBVKAPJzVwa3+9KzpboEWb/JzTATgUWYuyZ/lAWhoAKeuBtAtB7zlbCguQcmiwX\nSmAFSIMFyTXOe5KDhIBsvD0ufOGdhcEhdW2Aoi4MiytGMqEnV613THiekGOtYRajkLqNAakd\nejDBqt84Nw/FVnzEGw6FpD4y8CrzWsr0m+wvUW6YWJg/hMaFINTHO4xENvRnpeQrH3X9phx2\np1HxGZukTQKAO4JrOFx1v5rCZbnPkiwJi3SW0f30tGmegkhOe5LeRvE+ZNQ2Jfkn+IN7Vjnx\nRdAG/KTVBSWFWKTniYqUTZjX/K1dj9Dx73ERj4j0xyzyli53ZI+LT61Z8ETjXcAeKvx3FxeW\ncI1+uQTmdcBznRsooCVTGCtXXIlzJY0DzoOKEQWA84Yo+B3PNFdBIagHPvxADmQlSVbl4kpG\nBsJMaSaqoNE/c+sXLlRLgBnvQVIBUCUW6XAS4vMQ4KnLmkAuFBSBs/mdbOIINeHfYjZQpx8W\n7DVqq1zVGnv8SCnhdldXe7PAK/qeB+tK+zkoM2gPCw+idkoOoOrNVWgdh5RMsZloxtHfVb86\nIFQmqviMBexkL6lnJIo64+3RqiC6QsLq3MgwEFikNSSQaNRlwR0S2SCO2XBCp8JTw+AC9dbp\nQvb0EU4MjUzh2g+vji9395cIdW2dwlqzoZEN2YR5IrmsQYjE2MUQkSaWRX8BFECkitMKl5dQ\nlBO4QdDdh201EOULl5+wh9vg+srR3VIYiNUBvqgVFsnKEYsUxBCReF/4HjbjmVISfI9XwAUv\nuVj8EvqLxEpOmLc+T9HXhsJ05bnx7qJIhugaLhiVTCIU4tj6pHYVAQqHzWR/LRbpSpAtRA7C\n9r1D7VUr2tVl4J6KZ/dV1nhywHFfh4FKsgtXwbgduDQJ27jRKLKJBk0GLFLQHFRq8u+qtU4I\nlR9ffMZQmOglcbRCQfM8vJr5iDF3zmQoZCzSOkc8DdslvKePRDakPGaDgO2mmWR8JfQWTg2q\nm8L6UVVw8Y68ZqPjV+b+2+ZT8oNINbuLIu1jsEiTyqF3+HZ/5luAcWjlybRB2xm+nxX+YU7w\nYHqzymgUPNZFHqid+JBR/wn+nC0jLUHu8poA1lKONBOwCvxHqW8HCMUFN9I5K+RVJBcRcpcw\nJ3EtkyiSs64+NN6GuKLvzRXtIk0RMpD72zE2Kt4d15E2c7i4FwYuUBOvUrDSAvL5Mq+CDmMY\npSOKnaRo1dPWQRGOC4z1SK0OHyRAircbwjASEs4UBUOsR6OhLhWISFYsd1gQqWcNhL6uSkSq\nHsgw1ihoDNOwQCO0zg3Xn4CEOdxahGcuixesfof0rm3FfgckF06jab/I3oQPT2H9r5p3gkjx\nvtMz/q+ar8kPIm2WHUMLq6DXIUXYLejOEfTSWsHUjItx4WTW3A0UBd6XOPkFNFNhy0r6IDRP\nznSdo9QcUbq80zJQtgCU4AxyDf2E0T04ayUs4nURDPoNCusU0T9em9RILbxWSc0wfGVxjZ8G\n1KSYpwCnhWDFlpbiwiLzoQZD8jJ5EZKQBGeYeLHM3rZUJYXNKVVDYKzERzEYrVgTAwWH80s1\nx0vAirPyBFXAaLQJuhCR1HL5cyKSQobLaee5gjOGMZyXRuGF2pYoPR9Gow9nUXS1CP0Vq1RF\neLx8S+LLCw2JThyzAaFl4Yo/CxeNQKxnp0UpNid8cO+RQESKUqf9LO6XQx4WKSmyobd84OZd\nM7wK/9ddMXjL/u89CxdVgH0dUq1pMCIEXDTl2jJcATZSC0zkxG4FgO3dUsaAimOMFDD5IIG0\nSKWnyFpFSnBWpr1QYsO3MHQ+iyX5P3vXARhF0bbfmdnd2+vJ5dJ77yEJJKQQQq+B0DuEIkU6\nAoLSO9IUBPkEPhRQVEAFUQEVAVEBe0F/ARUbKKCgIjXA/DN7d6mXeheQfDzK7t7u3mZuZp6d\nmXeeeV8QAlGp6wCyq6FNlhs37LHOpKRYEFW8DyhxGZ8EfqyhgSRidoV6dXo+qMkkfXrBGiKG\nBS+L6d8FDef5sAx3WJ3XGand/bqFuvaVGgpDth5YFef3vS3HTvrHrjqwdagwt2g2fmv1E+m3\n/7EiPhvuU+dJmq7g2YWss5v57+obrYL5HcTytUiVxT1lQw2hqsqGFxoaxbgp7Dubs4xS/PRL\nl2f5sJpJ1BIgw+DLh3t7It5DMnpF8Srb6okISzfNLiusH4xyGRSyEQwhjV02+X53coBLKd4J\nuOBQF1PyonWPk1fdvD6BtXF6rBBRsvwZogzElLAuMZCgbLAqcxM9mOMCof4z/pkaJxqyrN5X\n97Y2k/AOTdyQhF0av0z3tHAjEWOKxFL7fWyk4NZcsfNVpGx4poHE/qRLu7Kqx4l+geDR2Z5p\nvBq4p2yoIVRjqXl+kQNuml02v3mzI/PDhlB6QxoIcHgXQXNgGR/7bKLbJfCBXkZZANYypbM+\nF8hIIQ4OBhNfa6QybaQiq75mGA++jHQ+0EI0p3fzTmnz8g+HD174Oz9fsf5euZJPL16h+fn5\nbHOFf75iTcOtK/mXr/52Y/bUzv1Sgk8m+LVgrFOBCxqDsNcpUFkCZmKxE3TxgXe4I0nGFCVe\ny1HIIa3clkIIwAWvuk3gDY/UafCT5PNfuKgKOmq+6dP6zHi653ix31zMdJxv2eTTEtdsPhuC\nLQ1QRcoGtpndgOK95WX5xnKsFVXDPfN3DaHKRHqnW6Rf81VfD/mdvjpkWF0clVyHOzaZEVbw\n2neTijQBiL/uvQsaBFKxYqgc4LIvScWbnNIodgXrfT3SckCjpMgy9eQKRoTNiD8HCaJ/lz30\n2NBEznCsyRqeV0cWseTPtQX5K5v7Rfd4h34xINGz0dyLN0/TI39cXdbMJ7ZFii2HbOuR/Cwu\nkcsk0l+zGnom1eMhA2ZnlU2kVzuFBbbZYLFZf1lfh7V1DletwIrjHpFqCFX12TCf1A3fNN4t\nGO+i/XDDJVhQu4gCV9jYq8XKJGqRUQwufzh0+yAg3xY8uVqrpIj38KLu68btHmw8RwTcVOis\nSSesGcUSaJAsIx+Dzhca04sNzBM2re5GuguZA83TZJ+NvjR+WT3PSc+sSgGbl6FKEunHkOAZ\nm5eF422UbhtLx52mdjFaHLDm6VG6XN4ub8FyizFttGhVlUu5EPeIVEOoos+GvWQ7N3+fckO7\nflP50V+hz5U22DLIYV0py5wPq5zZltZD6jLVUnFVFst06jbFSic01NpuLLIr+bEiKDK9qrDH\nNmnLSJ/ZRUQzAX8yx8iaS/T0e2qJG7L9IBe8Jvv2hZae6uegqyes1U5pVa8bxOnQFDopMX06\npLbw49GZ6BPgvmwSonHBCW40tl4sd22+2QUfseRRhURSlA23MhtzC86lyeoSoZuL4Rn1QaUA\nPGexFkwM5P3Z/Dj8S+XLtyTuEamGUMWFfbl9lHkk2g9eXGBOp/PQG/S00q9iL3NhZkGD08ra\nDu0/7su7SpphPRP5NI1HntIJMxyzzgohm/oa2yZcESmo94Um8OIoOQlbLoo+JFMGrR5EtMgE\ncFxA7WV5m2dSNqhwPJ0AuAu8D1ltkc5lA/UgX0K2qx80aRY7dwPpr/jjo/osfCRcEKYrC/se\nMBs5kRIeAhcaiffxU5u9c62hMksQqQxlw4fIYgK/lTi1nJxPVTRZ19sucb9BJ1p1R3/hvhUX\nWVm4p2yoIVSRSL6b6KI6b1E6B5Z2qZ9Bc9Gb9LJFy8Mq2+wULuDWs//bvMIOckWylr7HL7Xs\nn5En83F+iMQu4+TtBkWgDSqT9ZsYKt+6lGXks8MhxFfZMmrq+LpaSAfoqGXdzVbdCbymwR6Q\nMRz81kE8QfmLgCyHaHhe9gDYKveGCDXkADwBY307xfuFcVMIVaVErxoF6GUtfetHmt1BXDaW\nEWm5WUcDxZuU7jq92XtVlCWPbETiygZKy1I2rLY5TZloTxhkxU2B5fX/vX8BXoPvabqJnRjG\nGqWA6MqUbK3F7SbSre/efOmlvaU80pVAFYnkto0u8k9lQyVYmJOVSTPhHfotdvNhdTYEoxkN\nLwCvdCg9l0eK6CUIj9OveJ3u1CN5oJETyVfDiZSyztVCJAcMDyWg9PN8C4JSIAvhELhgTiS2\nlRDXFvXRgIY06i/Bi2rBBRo3A+18cEfQA1vMIEO5XaQHDOAzu90A5sJDyCWGuyliREJuyUun\nANmppSlLaP3ewrJOwIjko6UBapYv0U++4PdUsCWPKheN4rE61oMZjempD+liu670ryFWZx7I\nvQBvwde0LutV/h+8Sml4aGX+QK3F7SXS+fEelloVMPtyefdVkUj1p9PV0a0pXQ+HxmYdpKPT\nWb/D9DzVAuqsRfPMbyktAfIYT1l70FSG1+kj7IwYOrVrQx5ZnMRUcVhTFR4h9S0/fixiUrhs\nKZL3Fi1CcGiFIVjPmqY6rHe5H0OcIiny5vdFWx+SyjueLSEW9Io3opbQFhl1SGnPKDLpdjTB\n8ISW1ltMe8dpl3UBWneZ4EPbww+Uvvjj5Y/HNqtk2SjKhp1aVi6536zr0PE+Oje7LKtdwGpG\npPYXYLV0kXYXbrL30iuUappU8g/VTtxWIp0OhvD+MxYtmtrTB+qcL+fGKiobHvP49SbrXNzq\nnEkP43fo+/ggXez6J00GkoqlZkaj0qFC8Ey+hnukQ5N2qyyV3CptiHQCaWxWQKsa3Lplf03N\np3951DGkaBUUrZ4nt79jbHOzooBdc5sHZCxwrZ7CsSjux4tgywcfbg5BStglIEjWI/Bj7wCK\n1O4fY9fAbDea+wzdCC0//6oH/eEBP8aGBIue7gfjfyvIxmLKhsueMymVd81LEPaWY/6eFPaX\nQqTkzjy87kCFSFPgtYqLrCzcUzZUDYPELdajG6vQmHJurKKy4VpG6Mvnr33YyfAZpSMNq365\nX99c2HhsApGVtXPINoKxEaegZbAAuzmBSHZg+ROe1r9a5tr1QsRhiOJLlaxxyBSbYgaWLGua\n+KY+uPH1GTG8TUMq9uCFMuos6g7s1bf96Nr5l0LN7k/9lv/V/SL3z3pI3evT67+/4N+shGbg\nB4t9rCxlw0vC6P+Tn+0oDChvHunPmDp7RrbdDW4n2Ycu0Gh35PYcyK5EiZWFe8qGqsGriNvC\n7v7l3FjVCdmLI/ia16wv2eHNpW5K5D4MmsQ3TOVU2xrpzTkMbbFPJdIoyGeNBRPL7jrLPvY0\n6+VmsV+sGnlhJl9pG79PyZOP6vNlwONtXWibsiGkAmXDHq5gkgNulDsh+3tfgTWliZZ4iCMV\nQW+fKhVYCdwzf1cN4rzC45lSOTdWXSJ0ed+OP3g0CvrqpBuvoVduPM9boRnZoAExg3eb5r6E\nbI0CDosW2au9l7LEiMedjLL5auBRYAvk3DzUpFqd7lbH0KTt+HGDh82Z2KN1u0FLp4x57Z0x\nLTLzRjWMjW7co+2wTW/sXtmh5+LpeQ07zBneMHN6C71HgIBFJVDlfZZHpYHF64q1jZJsTSMg\nL65REvhyXR6b6ewe+PAohEEAiudiVXeI6QVviq471J9q/Z8M2btoGyfFjeP73pw47/19v9Ab\nx/Yf+s/z1rm0P979jBvh8v9v/6/sZcKVDay699n8dsEIqbLKBkpPSWvnKBKhcpQN9NJHhwo9\nr7/36H7HiHCPSFVDYLfC49ygcm6smrLh+ILeAzsHZSrRKOgkdz8NhMUor2vFzSM2c3LElmic\nClcSoaLdPFsNLyIMt4UQw7hg9FPUrocQIWU1bhXa/6SiH5Akg58Po5AbX2SBBH9z3eykSa/f\nomvDtSq1KSA+2tc7be7GMV0e2tbELMn6qK4753ZtlN04u8nglXvntMnKzp0y/rPl6tlezUaY\nZxmzVu+bqQ+Plqex8j0yo3N2lGaGouLxzowJaPS0lUgHp3Yb0SvOkov6oWmmOftXDu4xR7VH\niUaxrfNDEYM2Xa1GOVcZ94hUNYxBi63l8s90mFTOjVVSNiwSEu/ra0ahistiHvtIW5k6/G8F\nKsJKpBUTVE1DLa0kN1YQLaDWI6xWRnanByEiaJBZC8FE0Mh1UGIIpLslyey9kayDUCxKKBH3\nvQ/X0coiqoMH3aCrAPvHGiBmFSPSs71wo+Fd9JIi4X4De2QER4Gh59D66IF/TrE2p53YbERH\n18hvKi6J/JwLFd9UHu4RqWq4kAz6pv1HjshrpIGs8qhSFfP3ZolbMB5xQYcZkVYhYrzQqmAJ\nuFIDLS7u7CtTcanKe7s5WCiaAFnmLwF+AkdxT8pIaLFYeFELbSXzEtkLZNF3DPIQvLNZIwge\n9EmlYdTqesqRakFAxnn5EzWhMgaa0FYr6SLaurZD+hUbNYbXterXXAddXadzfc198kdIXMvz\nF4VHU582Pp+y430JnudZq64ZeZ1+o+6hX8o4pXuMne8dyin0V7vgS3Zzf//W621tnsIV87cj\nuKdsqOpfW5aoVBwxbU25ZpqqECliOt8u8jflMCJFE039mxi6WKumhldLZf1PB/ZHg1UKRySb\nVzkE/uVX8prY/S5cAAAgAElEQVShFLLwV1ArDE+z/hEPvlQpVA1m5ZZfPFVJgAxoQ8947o1f\nL3Zit7kCVmGz8gSE6Q2tImJaSZubVUiU3RZTmu0mIaBxqpFu6a7Spulq19WbvesNl8jY+Bts\njFTnwa2qJE2yomwYAc/Sg4IlBPz1sLmU3pfNjgY0oU+YrlP6uDmfHkMWkd4/Xk/YzX1u/v7U\neuwwkWoBbrtE6Mrxjz8+YXchy+cfFeDhyhPpZ+6I4NzFRf5hWkYklXty5i6AIVxMxyqqaI7A\nuK4PG4v0ATRCG0gU3Q8u8AqpK8oVUhn2VJlbBV8IsB34WhtCSREBehgtN4ksMdIiAQdyywdq\nyJegg1qEVQBGrmLvCFrQi9AIAgRwQeCOKJfasnfEuR/WiWDCzaQFN398QkQqoIH4WbfHRHyl\nYXPVf17wWxwq6yNnc2XDvNTralVrS979BYvpFpN1/mBSM0pDV7ODoDX0PHzIAxV+Qp8MOfUh\nfZjl8OCudnP/HpGK445p7X4/UeLEt6Ro/as0kZRt92mro9Ph6r5M4jX04FqA2XHsdd4sBFBm\nXUQiolnVbAVwPwQiZB3fW+0DYnFTQ00g2XYQbN2jUCu7iPKDda7csSQ3WoiAJUMx9Tjrjy4B\n8ONyoHTWVKlk6A4BKnYCTEB5KJkmgN427AQcivqiDrvNL2KVAags7g19TtLT+H6mHy9/vMHs\nFeSlyIPWhlEfYpuAwOPpGpuubmkypaYXr9LftNspFcfQ3P/DmXRh2txsyhdhTLEvjbhHpOK4\nY0SaVOopl84XYFnllQ1n0UesWfrj5pXNJvbJ4NaNK1Lnsw6Q1NcdtMHpAmpgZuy5D2C4WwBB\nBZObtwuJtoMgK494583iuLhUKojVr7HtM8YwnvdPvQDqsJeBhKEeCsBc7SoB5XbJEICN8uMY\n9EKmcP/XPZeaQduArkV/5C+IwBdaNODm7FnRopzwEM+vKVn/SJpsS979BKvpKzrWMTh06dsX\nRrWlNHaZ3/6OeDn9BRKovAGS6NPes7IoLGFjJfuqbqcS6Z6yofooTaQiqIqyof59lg/ZvMBz\nkOYnKsuD+Ut9KPdU54ehLw99lINskzm3GWKBS4aK7xUK5rMsYOmOs36d+/8iFq+sFos9dRf4\n2j/VuOWJrV3JDDKclWXsRFfxPL3hPf9KxMOejywRJlN6OWSGVp7vx7Lzn4CFy12bCp/kH2Ps\nyMEjaIr6v5R6vrQi1oONgh6MNezIEeOvT3OtT+Uepgx6WuqpEOmU/nm7mf9A+z/hM+vxpUgH\n1iJx3FM2VB9OIJIFB8Splyk938+Fr6Q5LZGgz+cpGiAENd9vczIkKMEkd1DzX6C30tAXBIxi\nPAkiEPRnDh89RT0uxHmuU+NYMfHYrzk+a2T8eD59RojxP79BiBLrf/dzs+DVIlkZHvvBU65h\nq1RPnhbxvDzpWm/IjKIefTXP3DRvnaaJv8qGRT7C0zka7zihT/1rAumdwRowOeYWLPkyIfOm\n3Wx/+3n67D2fDYW4rUSqWwReziIS3emhS01UhbxOubLhkNpeDb1bgRhXdA0LP/NJ4ZAsN9tF\nULvwNwbyIERrBKR1Vc39r94lIwYTQ1gYxnreVXRRL3lca+S9WRcNG/EcQErPtocyIbtQdhO9\nsU5ZT/5/hOjZN4inzggTufOTmw2RJ7ihdnZXUjgZ94hUNWCsKgBxnEg2ZcOl19I6jA61Khvm\nhgUA9B5RZE2rYqnrqi1RRwu6WqToWEVpyhAiAgMRJIyQaNYL/IzaaJIlURYJFvQGiWCiEpU7\nJVHnLpQe7tieVj5P1IX38X9GSIoHAXkjiS+zaBXSfULO5h/Y7+wVGRSZ1W7E4JaNRh3+ct2s\n539elhEQVrfL8vM75k+e+PDEySvfObN98OgxPbYs+my55mVNk0XeLwkp7/3xIolORlvPUvr7\nNlPrOnibMlvgNzAYTfzWqmw4s1UzdIJNIrQgTtp+/p1c/+1qq7Lhvud9pn9R3bKuEu4RqWqY\npC801TnetSv02UDbjV/kn2IJxjw+IAggul7hiMRCpPBSQxShyB1ssKF11fuYBCzIkqQi/AwS\nRNnkoyfIAixKkkA4eUTrmYq7jZXtWPK0EaxxDRHVGr2np4tbUEhwyzmtA+Mm/9YzOHLEX6t7\n9xz72K7fD03IzWk/hDvyPjihfU6LQRsePODdcGrWYs+cDT5U32KSPMylbgS8Ibd8QPumKnts\n10ek+oHQZsZDfbsuDhwXh4YseP4brrXrK1K+FPZN/djjlJq3FvpsGOG9Z8WWnav2DJm/cPqS\npVtPspOfblz9TsUdOGcqG64eWL3xs/Lu/ZfithLpelK967Zjx4lUJBhzAZFeblnJunt3gU9+\nqZC15cKbia0Ns7a7IpDPCLIK97hTTCLxeGhFv658Qw1tz1iJ1AerBCN0/L3FkUKfDcv1crwZ\nCSoNv1vUo96fpkFApOC3y17u15Cy4TVfITIAMpwSdem24vYaG75WT7Ad1gyRYpAXKss/STF4\nV3TDnQciPLrZyHH80DAAlB91HHMJBHf3+oNlDkzEyF9FuhMVDgChrkhCuClviEkL6Mk/lOWM\nMoa6dXmPFZ9tB93rJSTvXhlJ6Vpx2LWvr31aJ+V6YX4+Jj+ZT5eqPFVNJ8veDR4Sl4eqWrGO\n5V8Pivvt5H7NzCPtFSf/zXjVwv9cxff+u3CbrXZ/FQxd9y8o57bqEgmhdkRZ/2BbupfIahKX\nFEgFYyIbxVSla+5tQTF3Q7ikbdymPseWf0qLw91K8KUY/OpF0RKkTA/UqAgjcAc5WoqXJSzg\nCLVLXFfWYcWJahWSkox1QWT9UvxtLLSAFBF1o7Ql/tlzBc+vS64Wd3dnTWsLsvOCbh2l57VP\nDcer1Bt/0r0wKjIXJystw9AkO7lfM0RKUHyY06uxEyq48V+HWuFFiHacpPhs2OcBC1ReAe7Q\nUsIBam4Bf4qQzOlgr4UyFqu5tw/etr9b4i8Xm4m1Hej4uqQOPDhTGo80IzMiBWC++C+NT8gi\nLjt6Sn4Ut9ThVATLhCFJiwXwh24yjg+drRHMvTFIyfQkqLwTRFhG6S+wYkJTnl+71Nb1fiMK\ngx89Z/x5P33B9ZBXqsl1TYe+vY6DKgWUoC1HwY6zmhoh0vdg1ZqvCHfOA28f7mIiFfpsUJQN\nfN8A5hqDg31hoh4a6EAgMBeJTR+5PQypJIoY6iqGG2/ARkMYF0h4c2HQBaSVuEaiHlCuKWoC\n6HG0E5LCcF8EX5sWPfl5mLYh/iNK7EBpBErc6i6S7qy8UJqHDxnK8oeM+I/inWudzePPo+GX\nbD4bXEI76qhP5ELSFwI6m2dpPkGoNyxZm8faB3i/dO7XiLJBMTr0WU/p6xrHHnj7cRcTiZae\nKWwHC3QhIX7QR4NitbzjtBIL2TMrXW//dTDy1qcf4xPE8lBnajgHKsI/RjEisf5gPIKPuz1t\nPBgQ1coEZ5pNptQrJlakHposesuMg/awLl5TVsRCRIgWTeGFPWNeHa5s2GI6OIbqVlI6VXrJ\n5rNB7ZajpSqPhVJLcG2uH423gtAClsxoROkpJVJZCdSIsuFzOENp1mxKN3k59sDbj7uZSKWx\nGj8AOoMB6ZSpS7CMhP6dzhkqhs0qF843CZZzlyQPEEGFO7Wi3+qgP/FzWUdzen4AD2i7oqGz\no/LfRWNJxAGUKl/bh73RWQHi1X8OAF/cnICR9hwAJ+oaX8iT6Gk8JorisfRGrKbA/K1DmVqq\nhpFuvOeoDyZj1G4aC5FWeNgR8NSIsiHf/ISFSLndKvjGvw61hUg3zpym9NhAFyHqbuVNKRR4\nHWf/uXyjAo0WltOPZI82YG6JVtPDAk6DrFmuI8XXIrtPg04N0UpTj9C+mZAd3G8r8vLLbQqE\nu/ySFL8QbLxIPXCdcXpvxfx9n1sII1L+cBdTAZEMDbVqqs1yZ4PHWK3gKeiauQIsmdmIHjA8\n6vQCLoGCeaTFxnc5kZaIH9f0n3Q27mYiFQ6B71+2KkxRNqw23K29ODvgXpeRVs3nkEQBPHgg\nWgxEhmaE8CNMwEunU4FR8IvRzTP561lbxZgTAEIiSuAhYbRWmwbhhgrMWmdViOktyzzS5QTU\nAcUFu79TOCFreMEdOolNBNaLVPOYucFuurk4one3ZnhMjS9fLSDSrZG4uV9qgua5mv6LTsdd\nTCS7yoaLDSvrhbtUta2EVKFGgQr97nFZt17CgrmJDhNj7/ruLl51h63pFqZVa3wbH6Snu4Zq\n1WrfRq0nyKZwdSLxHexJdQnx4A5EhJFiQjSMFHwNUIcYEaiJiwrqGOuI4C6kLT5rUzY84TcK\nRS37o5iyYbhphHfuhKHDA+LiIhJT+q34i/4wu0vrifYCzhaHM5UN7030TZlTXiiMfykcJtKN\n97Y9Z4HT0lQ+kX75wuJA5Z9t8K7SSb/0+cm07gt8Yz/+bj7eRefFlqqgfGMsegYjpBK8wmNy\nc0euGZyds3Bq90Z1Gw5ND2g2qEtOYlSb9JiInr0SmyfV69wsvm1iYpf6kWHuWpFgpxONaLCs\ndQ0O67Rs09Ktny3plrfizQk9Jiya3H/48kO7Dnz1nzdf6rN7zmNfbznx5PO7x336f9+vi9yd\nNuQR/V5tTA6cQi558COQdnAABzyjvqBuOgVdw5EJ8IfcKQ/9oeo2GF+WevrClfA1fcmVkP9G\nwVUf7orBb1Nn4aoSjYJM/OnotRZHto+0DHV4NIpLn59RNj98db3M/LfCqcqGj5P+duwBdx6O\nEumjoIJq4bxElU2kGws8WP1rfpQeSlfWkrY98UFW4aBIRC/vr26DdPdDLhaaXeRyXBXbiDoe\nTXdWMLdceD5y1Ze9UoSWX5967XpvMzsKd2GbthYJ5JFMlpUeIZaI7R1/KL+InGj+Xh/Mo1Qd\ncOQRdx6OEinNZcwTay1wXqLKJNKtbm6rjv++v73ucdHPNAkmyoF6qQmSh5h4f2htHwR+tWeE\nVHywh0ofFxOtawgbUcmg8+KGyjooHaA+bgXYHZojoa/LgTBJ1Ox/5aOVJn/z6hO/78vRf3Kz\nvceab8+kSerXzr3VwvUoy9w3pT7v/7GWCB4+8ydrOqS7v/chzfn29YVlFJHziDRFPffL80fu\nE7Y78Iw7D0eJpH3ZeWkpRFlE2iZzr8T0Vg+xlf7EF/D7Pix7u6m+/wq5Iw9IovcJiE+y8Bex\nHV/btvoX6uQK7yxU9R1giUFj+ZYKCTI3TcRlsF9uJET08yBRUl5ucKbqvQFRN+ktVxfFYrAI\nFInQre51N+qOU/qU/njHdEpv5jag9O+AIfvpsz69m4LLrjw3rK4bnk0t5m+7cBqRPsWvK/sZ\nHtYyP15eeIV/LRwlkmeN2CnLIlJ7y7Jy+jTUHacoG+pjQGPow3WkzLzG8NN1PR8MqfS+pasl\nKtAU3J3zSkV/jrKwT2P9IYS9MhCQDAQC0euXHJb1iR9DUk96DJL70DOkUR79FR+hnyD8Ec+4\nNlEdlQz8DtJHs13jcYc+5Q5OjsL31Cy201G1NEsXAXOa69viFaj+bSHS+GyLsuGqYZvlRKP5\n1X/YnYOjRBr1kPPSUoiyiBS12rJ/TDA9oygb2mhF8gLt0hMeajALvUXDuCHBKLqgUmS5O7t8\nVkUesTpssbovz+abOL7ynEdR0vmxs/IEJMWq6niwLkKiNxXdllAqmh6lNMiPtUD+G+nznj7P\n8owL6xWRf3gYO9CrN+hWHtY/4/mS6EJ5JBcqB+RoKfZbSHrA+Lqq6eh1VKd8IjlJ2dDuAUpf\n4Va7DKtn+KzZ1X/YnYOjRLrUruszbx9U4LxElUmkeGuM7lXYY71y0FpDyCbasxs80HgKOkBD\n+AjbqHK9O1ud0rDSHxcQSZE6cCU4jlQCKDEiRXqxi9pRiISrY3wpNcQGUMGDvdSJ+yOU+gQt\n+eeWuR+d6KLR0cDVNLqL9EIGYn07lbAWj10prjdvwRKlN8S3qOzDiATmhUIn6BdGJqOdkFge\nkZymbOg0ipUnXKG03iLLif9NIh0pdFbqvESVSaR+uZb9esi2hBGJYZWoK10UbowcFoy/+pCU\nsHPf7Sh0zAVW15bJrGen0QL4CYQvtBAA98RIgPQoNwFFSG0pTZDavw0JzddvAGOr9HkAbZMW\nsaLhpUSF+2gvb+2mNEakI0jXkxHJ0Mc8B8mU7idnqRY1ZURCDxk9OZFS8RwhgyrKhhrGnLhb\nCpHOSW9aTvzbiHTr3DfvvfL00oeHdm4U7yNklnGXo0RKlbtNmWFBdRJZBsoi0iH8Et/9k25M\nI3vOHzu5SlJ7R6DsA0hEevCclASoeICHuxuIawW9eC+OGFAa1+B24sFq8ngA57YQgyEIiBY1\n4IsppkBHT3U6qMd1AhDVAhBRJQIWPNjGK9wVaLCGKESaC0ZOpL9TEwIIGrsyiBhCfGV6IbE7\na8rCPTQUvKM9AAZEY8ktqOlYmvzpNzXuce5nzSJOpPxuMdZJ2X8BkS7/8vnbW1fPHdcvJz1C\nGZFq/ROb9Rg+7bFNzXqU8RVHiSRvqlZKK0CZ80gLSf/HJy8KDT/o7YEF0GK3CJ9wFV9hwCqd\nr6sADe7Uer3bBFS4KToOxKwhJjK4M+5w310uEA8oDHJB0EI7UEXAYtFNhEWkcR5pCQ3D0CMh\nkY9EzYC4/oFNQJskzQ+IO0ep51Na4THPQRKKROnJpJNPvT8rXVyOKRuel3L7wKPJZpuflf4b\nHHhYtXH51NGDWx6bMbpvTmaMsmZM9o5p1nf0jMc2vHLwaOHP69+/jO87SiS3T8u8zQGUrWx4\nK8cVJT/8Fz0zJohVpqiZl849EKvoydi4XAhEu34LtlP9ah8KCWX9qAYhGOpxa7gaqfjGUw2J\n2BtDkj5WhiSXlRGsV6jNeZuaEyVInvr3f6IpDnHFdb3ru5C6M3nAiXO3zo5LkILa9kgQ1Cp1\nyoIr5ReRE5UNn3T3gdgRpxx5RLVw/ujBVzZw7jSL8ebLjRl1MnM4d7YcPHqqDLdGNUak++ZU\n+euVQHkSofXWePdUgljLgWEHdlnkCS0f5F6E5sXbq3gCYaMMnXedvst3lBtOvQAn+jRzwwJ2\nvv4Oady0TRs8WcnX/WaaNviYN0VCUzgDKjPbIB+4AigQKJBWbINYvw20M4Di6CmIgpcPULlT\nClBVtzZApZ5moOFrmgDlEiEq8LV9dXePM7LdqdfoqxepA1HE7krf37zZeWXDQkuzg2zcGWJp\ndk5VRptbY0S60Gz4m1+fUFDl55SNKhHpcVSmBqCWAUOxQAPWX6q1HGhcLb7DrY0VO9KDVgUZ\nxCMYp8t+SV50TJtgtV4O6TSovk6nUdxaCDo5rOcH9M0OwaYGg9sFmbKWXP3IR3EeYwx1F5DR\n62hNKhturGps9m9Ts4KG/N++emf7kmEPDuqQFevFmx3JO7Zhx/smL16/4+DXZ6ruKHnw4DIu\nOEqkIuVa5eeUjaoQKbcod4pVtNoFxfu/WDC3ZNsoH2UuDOLu+W0nWPtLwE3rYoDmzy4zQsuX\n1/YkHUgHL4+m7p46iObGc4l3hw1u/jlCeyFv/YsNwW3B7GnaAO61UuR2dlzHPxNUE2puQvZy\nU9OkLRuHqkaUOO+osuHij5/seW7lrNG9W6eGKDZcvUnbsteomSue3f3hSUfFsefKcm/kKJF6\n5g2yoVoJs49KEcmkas62W8Fdg0HQK0NvDdJam6fSCqHaCUv4ChWS2A/2EN0wGyyFyzgDskQy\nFpa56Tfi93LrPY0PUToX5rVO+5v+aTBNgiWiMBzm69zrwfC4JsmwmtI9gltQgI4yFrKRdj8+\nsS24Z7Huo0fNEWmiv7Ja4j31ZnqpaLtXDWUD67K9ucFiKQhRzE2yd90ci6Xgo1NXqTXKdI3i\n7luP9MmD1oN/FJ8NofCQO3jh6fwlyiqVmQcyBgGJd7iCOx8l7AvKiwMjDZ9gmgYN0r8FCG9w\nGFAEdgfoQWQE2/19EPxf51x0hHboQ2meZyde8/cBCvZsAGPDPQVAdQQ8G2HPUZS2yzPMRBrK\nCRSlEAn1gPoUGkFGGcXgsLLhmtFq8B2fwcq7yNxupczfV05ZLAVD+GiHvzWtloKFyminRJft\nHpEqAUnuHQixsDCCEckMXO9cUN9qyWjJ+jOUH2bk6yNUjDuYj5XZhvK1slTymPYouzJzCASr\n2RgnICwQ1GRoH0Hn/ewT3oZflz/u9ukM0onoTr9IjQYBOqlhvtAJNKJ7GKzDqFMmPem2yfAS\nkihv4cZCe1/QwRYwU5gG0WXku8PKhq/hV/aUo5S+LluUDTaURaQ/jh96deOjU+/v1qQO4e9J\nZI5Mz8l7YO7qbfs+/6VcG+PdQKSmBchyXqKqQiRB29UfYmBmIKtQ3HliKWf5dz2sgyIuTkBu\nBHQ8pichgfxjIKJq9gKhstu8+WyAuLAvRKmCEAp3jQCtoBsCagnWI3VLWIsGRkFv0K4PoVgn\nQC+EeqFeIIpiDIwnqFcKfVTYYtiOMOVqiYcgTMMYuxPYx3kQ6cRiLYbPgI2FWk1mZBJulkGk\nq6e+3P/ik/OH1muXGeXOm2DZN6Fx52FTlgnZ7x87V/kF8E4k0uTJZVxwmrFB71OthNlHpYh0\n/thJtnVH4zVQxMpQS5qhokB8yCdKImH8CQEkINbzMkgSMJ6oz/Gu3RmI6TILQOquUlwzsFPK\n8sYk3r6MQigThqMBURCD0DJGJHYuRoCWOIbLQHQwEHBML7pM+7Dhcc4c1sw1gnDuhWkWmGjy\nTHDmMrNi+FN820KkpRFFWqS/vjsS12X59JE9WyQH8piE4BJWv644Zvaq59/89KcCP4ZSzyr9\nqT0POC3VNWb+zldw6eiEhn+VeX/VUe5Sc1sEOQNKZNuHoMEdrOM1DQwi5uJUAoMglEBnSJWg\nLcRhuJ/HdPYEpFJe1BgEvhettgfrho8VQU9cvEFLGJEAGxuGUMLNEgakItxGwYZJka4CPE2X\nB5jMmS4y9QcpGXsf/MwderqbWO0wmytTXNVTNnRseJ0T6bTPyHeGwPyJAztkxXjyLht2KWGg\n3mEo+dUqEsmJqDEiFWDysCo/p2yUR6Snipu/r3vaCcZa+6B4RkEEFyzlKzR/AzdlK4KOeKv5\nG1n2On6UKTbG0EDo5g3ekCho1yOCwIwj+PIsdlUlqdqKXv7PHt2fLGv2XjzYRgKjSmjNdeVo\n5S8r3PCesorBEWXDhZOf7Nm8cpzGI9VoVFaJaXC9cgzU/1tEOnS7unYl55Gu1L9DdftfBrtv\nE1TgZpLAJWwR9ErF15i4LfznQT0fb/Glgi2+GW29qswfuL9dZjFUzfx98+yR9155asnDQzpl\nx/twAzXxiG7QIoAlTuxw+NcKjBaliaSyHyD6NqDmifSGM701V1EiVN+i+Y7gi61D4gqqCMFI\nUHv6JQXUW/Hw4kdGtEmI65jXtanZ6OMRm1A3pnlKcFz9iKhgb5/I7DbJRtE1xMC+4HxXQWDx\nriXpAtzdzAavxAbZKU1b1u0zODkj1dstpNeSVSMykkI9QiK9YtKzUlokR4d7yESFlHER0sIA\nHnliHO/jjeS+Hl/kk687eSduOyA/eJ8vq/8etFrYg6PbwgmQjPApaEzwMbjHwCmU4A5/q/rc\nL/wdtWxlEF3+/fHXTh357hb968i3Jz74cNpz33xw6sj3fMx+48SH/9Abxz9UBiGfTnlhz6Zz\nP12e2+mcIz4bLv/82d4Xnpgztl/btHATzwZtgFVB/XrWQKtC6srHX1ciVF9pIm2y49W/HPxs\nLy5N9VBjyoYLFpzdlxhbrYTZRyWJFNAmMCCzc12ss7w/NW5atcjjVNrezkSW/t29vkp1SnGR\nZYpigbxB4BxSOm/1uVvIFGTQ8xhjiPXgJBV4I5cAkmZqMqER/Xr5oDgfF71OlgRj4hf0owSD\nIKk9glpMfpe9/Sa2SEtLTcu874m/6VX63WY6dFqbxIehngz1STt3oapLzc881iW2WdsB3EDt\nx7tsRQzULxeNaNG+aoP/n2ZU6fbSWJri4ANsODE7N3e2fS2c8yRCzlxPUSkiubIqFeHPapZv\n4dzRv5s3ToZ1KCQpXoQKfFFgHg2G0UwEPQ4MdHvM019vs2lixYe4dRVGFO7fQawr6ggYUbhJ\n86Hf/gTyCpjqecXylU8m8CfwRYVEUgzUa8ZAU5uBGmQktB02ZdnTO4sbqL+CM4Ufqkgkh+Es\n8/cKMXnYsGRxhb1rjhKprQUd7n+ruqmzh0opG/LEzD+PqYRooSHXmHWkrysvb1xYzWoxrL/S\nB/G+qOTeOx3MCPqBWgj3ZF1BtNutOSZ9tHv8hg1FvVPxPBSGwQstwEl60MaQhrpgMPJZXUnW\noAQNMe9ybWHy0O+IxiqYvjBBadeooDaxvqMdIp3/7siuZwZMSfRtDJ5WA3WKttOY2auei0n4\n5Bic6O1Z144WtDYQaaegNBYbhZ12Lt69yoZfkO48HeTd+HoQQv4gGK+mKbIgVMtVdvz9IASx\n7p0rggYBF7WQCkHvoyzUGcAX/bkc6oFRgN7Rf4rwScbw3WS0pgcMD1T3A+SikVoqXeAASPNO\nBFEFCJMshLLkyFlPqxLCVTuiUQuYsdDcWiGSOJIQK5G4gvqRtYsmDuqQ5atVFNRekNygyViY\nVkxBvUf1M/0ZTgzqqtlRurSKEanbww7Wj4CqyfGcRKSUMZb96FQ7Fx0h0q/n2b9CVDuBpVEZ\nIm2Wu1Ia5r6aThShFSvdB4Q40VbTah+wZUGsZWOmSDSx3ymN+ggEtRuh/vogI5cNUYpVWkRA\nntJIRLpHhckEWiJoCs24x4cW0EuZaqoLS91WAs7inpsfR+pc1eT0v1DERGFHNKyDGbPFpzjd\nfiRTAKVAspsQYlAS4M0N1DOa19kB2/+mf8NHbHgMxRd1PtyYKkTq29pOg/ODqsg845+VWxNm\nw6lHSp6povnbOUT6B71nUTa8iy6VvuoIkaDlnVhGYcNqfa+TVC9vpUNt1KlF7hqsQAV2BhGU\nt4QyHh8VyaAAACAASURBVBIFSgifOCKz3iJaIQTRJFUSYw+KxtEYPPkQ51HeqjwDwYT191Ac\n9OYU7Ad+XEWFDbABcgC6gCih50CXhJcKO6XQR8gqDxgNGQEQZ3kXsb8cILT3kta+dODo6Utg\ncRI1udUlOELtE2k4e7Fd8DgzqG/fgXaKy07dqywcnkdyjrLhFByzEOkYnC591REidV/A/hXC\nsWQWQ2WUDTsAEimB2TTDVu1q4WIkBFYZuzVUhWUTcANUOu7srj+3N+jBh9MHuIWAz7Hym0bz\nzWwIQJCBIII78EKQzm8iXI04EzIsAiI0CLAJIrHN+awLhGZIrZAMPU/ARIA+fWi2i5LZ+ZUg\n0lzLe3/9k5nTnFgV6L9mQvaatNtysEtlZ+Lr7hsj2ZQNfwOEURWE/Vyb/Z1wNyaMGqz6R4BG\n4Mvt+CoRSzePUcUaDUqwKRwKTJfufBMMrP9HMFKsdnyZiaxEXMLsqYXvHCQiVXQmQXrpvxhi\ntK/S8XEq4sUKOYs1X8+XR6TCZRQWfIYtAWA+JkcqKN78qgVc+pcQibZvp6T7Vk57OxcdJ9JR\nvmbw6CfVSVmZqJT5m4Dwjh5UMka1sCmqANbebKo1GrpU0BuzMimOb4IBaUBCPlaZQqHvfVlR\nwfLmKxgiCKqzQPCCeoYdGPzI0/SsD0Lys/8AFiHrZkkiPdzmmvA5+9iSj4hLLqMY5MVf2Xt9\n+9grtqLLXvvOqlJt+LcoG77UDWa/4vxg3VE7Fx0l0vWBsI/tHof+VV//XjYqOSFbO+0KlUaJ\nCBWo9IXSGTS+4EgNarVl0QkS3ACd5UQ6jlFYsgzIDSvOxVm1aWQh0q2448r+p89p2Wv4ro8S\nzPXdyTB7LlVOCkXcvVTR/P2vUTa8FyLGxoqhdhnjKJGWQtvv2e6b7vBYNVNnD5UkUtQaEebv\nbo5UPFgjgGuCt0kdpBNEtUZN2ADAEN+wnreGSx0qgPOreKUg6d3NSkIRxvZTgS1uv90KjgL5\nJpYzJII7V2jEdeEzgQgwFLQS79BZV1BgfpQMggwHQa2C3ppoBAkgpYgUjHpIkFI8YBcYXGEn\nRMYIM+dtDMfcFxNQ7aNrH92jfnzNnJloWIDAMnlXWRIhu/h5y6IXXv3A3hVH5pH+PcqG/P2r\nVu23L2pylEjxOdaDNmFVT1eZqIhIN99/cvmbrqrmtHOrfPpKi5zwuODgCB8Xj7AIkyyJRBBF\ngrFSO63AJeqw06lTSe5UMRmY0wJzB5gYTAVEiuBECuCDHQ3ni3tBgJcwvsngEr05/Go2qAQI\n4rFme7mmImgMLrnCq+x1g6j3SDP7KIjQB4J8yLzfPvJDuz0eJDC/0zGWwzu55O7qtLSEQb8c\nfvKxNy5T+ttLizcfr1TJ5R/ISN1vJ9xfbZiQLReOEkm91HqwSHRKeiyoQNlwNIFEJKq8lAnm\n79JwbbY2VAdlEpRd0PKWbScq2unjvUJ/HxUCkfFyrDWX12LFpIHCk2TP7dNVrik+qKdlJkjp\nwm9lw6PCZRSFOBQmuriIIaUDKtwjkh0Ui480ynow3NMp6bGg/HmkXzw6sbHuPw+LrOt7PqCR\njwl0tST6RGWAi+0UFFrsEG+qLJ8KbQtWAwT3aIKiITmPIDkNAgUCWAIR+iVKZpjwLdGQl+mF\nFjBVyeL9oHqd/uqugQH00nSs3XLr8I0jEc1v0Q2Trhu+ofQifFza/M3wlX7QH4P6Xhiq/aLU\nlVqgbCgXjhJpoOY1vru+RnCmIaV8Ig1LsXRTh9aldErkg35YStGwDsz/EJlssJEF2Y7ZUbj1\nSFX0JoOIsG8wgVd8llHaGB5CPZqIPwgoBTo3ImPJyoZCmnRhStAtStNExTeTB957c1Vo0nV3\n1Jr+goPoDfw+/V69o9x5JIYObW7RQawi5LYteaUWKBvKh6NEOu0NAc1zGpjA25kh3csnku86\nZXf+FTi1zeeR+Fgw3P4qfPvhat1jC1EUvWgy3yhL95SriruGhfyIddI6AF9Dgn0CBf6SEWHx\nQwkqGPFgE0p1fqFwQdtkpxokvccxCEhv9yWIOVdPQRO6ZS9sp7nfUJSMVqhhNf0vaOk6T/hB\nMX93HlwBkfJVr1KFSHvEUpa7u1/ZUD4cnkf6bRiPe+E+2IGYbaVRrrLhOfw2pU8OpQYEHxph\ng1tAbQkrVhqoyPyQBz9STHfK6uxAfq6vZaKVMUjR9Xgx3qi3CEhC/i4misx86ra+2YuvDfQl\nQ9d6BkDz/0RSKmS6iJTcvyYCoVDxHNL0S6YAE/32I6C612AhD9/HnrxQA3tYH06msxvA+zeE\nw5RObFMBkX6Dr7mygdJv4Wcn1oZ/y4RsuXCCsuHWqW//sX9jtVG+ssF1C+vSNacSwAkDLAsN\n+t+YT1JWmaICB+DKx0bWcx96+nBGaRH2eGaVB2DsEiBeFTTsRiEsRAbGLA2aOj9cD73nBF84\nTCKN6ADpyNVHbjAG3Fs2OQPQx/A8I5L2aXhaIVJjWJiOnqV7IYiuiGD8+IZ1+/r3KlfZQOkV\n/I7l4BAq1zXwXapsKBeOEOnn4nBiqso3f3fsYiWSeMsAWYM871jdrnGg0sfW4RCBpwGeVGks\nMqH0BUgnQhQY1OinRqyZ8oY89XP+3E4ei3sR1mC5ExCSMQJ1IiRlANZArsoqL6oDfTSz70PI\nZBjFidQEXeRE0qpgYRMxl9aBhvRzZFbGTRc9nyxf2UBp+kjLflzdUsVWC5QN5cIh9XdxODFV\n5RPpA3HJLUYkAXyoAVTDavkCJCsU+aora1y8Tbyr15MPiFR8YQQBHMz6bn68eTEZVf3UaxD4\nAm6uL9AFcVEQQnwkibARYuoDiL7cI63FRpHVGTVD7irWnhGgEmqtRGZeDDC/iU6sD16N6JvE\n6weW7xdzQy6Xr2yg9DXhab57VigVYqJWKBvKg0Pq7+JwYqoqmJB9Thud5J/JXreMSFtc/idM\nDSVgp6kCRVLHw8oiawDQEpohHoqNr/tTwXAguEDfKohqkAKEIP41FwhmjY96D4/xgVQCa8a6\n5DbAY5vI7R/o7h72fxUX3Copadj9dcXSKpfaoWwoB05Tf/9zuxb2cWXDL4/Ee04zWJQNZ6aG\neMiiQAhWpDa3pR7fBmBCNILFioJEo8x+nKDTyURxLxRR34X9XqJ4uRNYW6S4e2GbND6J6gNY\nzY+4XztMLDGTvENN/KOxPnkQXIMRdU9xxzrLzQazZ/rSpYHkIWM6gYy0AmXDnmid7DdpUZ9O\nUz+hN3eMyxn630rZrL+d073bLDsiiHsTsnZgl0jPejuclkJU7LPh9+8s0ShuvLdesbvfOL79\nkfkzJ/dM77J4VKv2nXL6Dh/dvOPAno1GtmwQJZAypGx3Hpz7xMO/c4PUPh3SMzrkNps0OqNh\nTlJgZIPmMx6d/8qS1nMSGj3W7+XNr7700AvdRu0J/Vj0bYreDusVBjs4h7YBMsIpQAa+UcG7\ngNzhGI8muwlIDPwJgSnwDUr0hL/oNQ3sB/88kUJMIvrbe/N/on8Cv8bkT+MixR3X23NP7T5h\ndcel4PzhsqYzuLLh3LyTlJ7O+LzyJXoniXRjUrSzrWGl4TCRzj0+fgzDUF+909JUeSf6V1pz\nfmj/c3lC7Vsd6wwUvD1k62d8gff/UPCryxR3WS5Q4CBSAW7xjZKv+5LYB8/lpY1rirLh1Rh2\n1cwjFgesr2yJ3jllw+UJusIfVnNwlEgn3a1lIFTNElM+Kkmk/GDU/eC5jSEQrIf/BdgmlQp2\n2DLVpAyKUMFae8VGroSLYp23aG6EUFYDthcgDsteT/906VBnCUI2/vEYRj6XT/7H5OU/Wegd\npZZHG3PauPC1NjuEoZ+8c2KCPJLSEg4iufl7JnngMIzCwgDYMU+eWckSvWPKhuuNAzZPSz5o\n+WE1CEeJ1Fu/ci+s2z3Zd7fz0lTpaBRjYPhy9vp0r7UTsvaASn8q4jq/8CNretQ6fQ95Z4yY\nDf2JxDrCRvQADOBZdw3L/DU0agtMp3QKrPMfv8p0omN8N2Fqm4aU5pinUopWNNORd2kJd1yc\nSLpl9G/QLN9J4FO6nXxdySK9U8qGVaafuLLhZvuG1U9AZeAokQIm0ytwiNJPTe86L1GV8Nmg\nKBskHyP8SZf+S8c/TkDRHyYVnCtsmJSgsRp+zsCboGhkUTqQDJPLDlARAQkNB7JelaiqA/gD\nSvdAhIsXz71VAF/SA+IvnwaZztEGMU1UE+SH/Z4G6CyuhB+pJLNaDwubGNvcb49IrjcYkcw3\naApXNqROd2KxlwGHiJRh7UZ+Ds7UsJWGo0QS17BHHGAH05o6LU2V8dmgTMiiZgZ4LmZxreWR\nDdji2cTivNgaAUkx+jfk9roYEBLEOiqIDeql5SGWAcIDMwgFZAZTkN8KSgkKNGKfZ3nJaVKE\nC8eW0PqCPoeCOgu3g0nUq1dgdAe0xbgDwVyyU9hLBb60jBNpSjN7RErl6u90SgdxZcOQ6mgM\nbqeywX2LZX+T/bCahKNEMs2hVPcUO3jO6Kwk0cqskFWIhBsYYATMq70tkhVKQ+NuIRISedBx\nAG1fAFUqXxEbCjhCE6WB4PCORtaf4260wpIFRiQXpPMOXnzmJhsKmZHWkwauBpQASXkS1WCV\nHwUpGTVH62lAp7CQqcKmZ88gmEa2o4NUDGQZTLY3MU5oU5JID7e5hpPZnneUBja4RmleXiWL\n9E4pG3ytrrSvodKLpJwJR4mU67uPptdj1X6wh/MSVVkiqXS1nEgFwx4+8BG5qy34CmTeGolw\nDCBabRIgVhBFFC02ANTLowknG+sDtpQ1HblvIQSeLdWt2MmW7AlUqMtt5DF9RWoCFEYBYpCr\nsJV28OyBphjz6B4E6eJM6QKV8M+sq/FGE2PiVDqzOJF++pxuUDNO7ND+mR+8jLUtgcsrV6J3\nTNnQLs+yf12qTjC0ysNRIh2R69L14N8xEXo7L1GVItLvIhiVLk6tpZECiVvd2E/M5t7rgoB4\ny925/Hs8b6G6QxaC9pDoqx4FesIYRqx+umxZgmU3QkTW2avLWMi6eHy+mhPJE5NwCm46ln1b\n6QOwukF4iPBCjACS2m8gpeqINlep14cTfNU/0G/eKJn/1yN65NNrYb0nu5yltya5/l65Er1j\nyoY9wi6+Oxtlz2ulE+HwPNJHT9BbD6kBtT/ntDSVR6SdoxrqByYZYuQ7WLfvLASlp1fQCisB\nXrgHVVTCQ6biKlLNBkpI5v5VEQ/Rp4Z4MKYLs4gnQc3BC0GiYWgnob3QWSsGg06E9nI8azh2\nfuEX/cj21W2k5+wWwWfuiUtfmSziwa+sbKSprLH2zk3IThf6P/XiNK+UPyu+1RE4R9lw5WTV\nZgYqQtlECsQaU68QeOw+ItqckCoL2v43YAt7Wczjgp5/1oPNFSspuFAfBC4REjCkZSpulrCG\nUDDoIUP/1EKNjvXzUKrg3/MD+mYHf5Ve0uoMWUssC/J+n1TPGNXXvnDhBj09NtkQ26Nfsj5u\nUOU8otA7qmx4IzfYZPthNQcHiXTmfct+pXM7oGUTqb5r+nzaEW48R1QKkRSno5VrnrgzIRIy\nwI7fZjt4N9JYlo8sx8CeKQakTzhZuYxYTEes3EMowk3hDKAg+APAB64Ige2AAjGzDfJiG+zJ\nj9zYRohgG+MERMG/DTsK3OxNxb4xvMCy2Rhp6P0qCs1XRtJ53556ja7+g56tXCoKYfXZUHXc\nUZ8NtwOOEemASzNl/zn4fue0JNFKEGmZGWpr3IniKD3XjGwXECp9VTmHrBNNFIJe8KOqIDUk\n9Wzlyl45xD0lSVKriAbLykaW687gKrRXW3lp6mW78ehSAW17JEhBnY/QJ5ub5JSFV+0oGypy\nSGwXd9Rnw+2AQ0Q6bRYsr4ZbK3D4FeclqiIi5YIg/4/4KbYXhxBZw1RYhUGo+AWR70TAMgSm\nrr388VLSZ9PrbUFLhGg3gwDupL4M7kjm3iQ1qbhdYOxZ+pAw5LlX/UGl0UQgUYV6pC/oIjSD\njvHa2d6pk0vPI1WLSHfUZ8PtgENEmgVrbYePwn+clCKOCogUBUHrRLXy4sUGraUiEVu4WAS1\nf6Wfsr6iSHgK24a1Oq4QzQM3z2ua1ReGU/oh3kLpYfysKJyh/7i4doXRTbKnwEK9+UFYXK/9\nPPJkYpfdwpt0RyLM1rA2TIM0q0TVLjoYyBtNjPRsRF1nEckB1H4iJYfetB3m+6U5KUUcZRNp\nQPLsN28YyYtXd39kWpMBGJ1cJHGH1gJ6AsDig7T2TitZoYhRlbeFiJSFGKBMBEAzgY0am6tN\nAJ8G+xyHz7VqSge2YZmW124/wHF6HKCdbILX6ngnAAryRt0Rbqaeh1vkUbpMiDjC4/SpEXyf\nLuyijT0xJxLdIWQVy37nEOmezwYLCohk7lV4ssvtW0bxI8AFSr+Hk3GAQ2kvGd7m9ob9rLej\nhKQz39lq7nRgSyxzpSurCINcAWJdI9lvliIkiNT4si6epGW/3COEb+ohCHxqoED9N6Qj9q5b\ncubmrz6PTkQ6T+quk1EdEPNQZ2JSCZ2hDUbzhS0q9w2MSOj+J8MZkSQ/WDkF76KuHZFCpL+h\nuPcFq8+GauCez4bSKCCSNKrw5FDJKemxoHwifQfAetzH4VQ0oCjaVYa3OJHywM7guzZABBXv\nrvJ1RKKJd9/cAVLC4gA0YqwI8epABLLAGKZyY+SS3DIQhC8ehv/wDsmGQQd1K9QjW8KqMVhG\nNNRVDfVAmoJ76X31ml7Qj5DFwlat6TkezXHcylhGpAZRsGSm90mq74He6B9M6VVILpb1Ffhs\nKBt31GfD7YBDRPLOLTzZ2Ncp6bGgfCJd1XC3T1fUi6NZ166dDwKfO13XaxCKus7bsrwIPPib\nQkMQ1rRmIxpw1waD4C/JwGOas5cJnyxCXNxqTiaLEWuaAzfLeWLfJjBwMyDCxksIsT7gEk1U\nYwIZUSgPpJ6qxyB1AstTQ9obEmC6S4IP2g+mtG6E5sNtYyl9D291ToneUZ8NtwMOEam9rkAg\nckLo7KQUcZRNpE927P6WZpKsvYFdceHim1oDW5Na+KPMBLpBEEAWD13TjbdLfcAjTJoPXgK4\n87YKF0zFgs3/NzIg1wY89FHgZleVwIgkf0aIQG8ZJfZ8baYZVurNswGFmyTRkJq+zvgdpX3Q\nW2pZpn9j+SBhb6mR8BLP7fwmpXwPVxN31GfD7YBDRNoKHa3BYv5KhZ1OS1N5REo3pS+grUH0\nk+rd5jr+bwKyOgES7QQYUyREKi3Rg4pPyPaG1HRoKjECjmqm4tGTfAhylUQiugCJRq3Mui9u\ntPb677e/GJAotB/uirDQ5fxXi7R+/hu+P7uroWfp6cHqBZS7RyQ7KCDSrWZQ96W/KT27LhA6\nOjNVFU7IdqqVoyEn409OpBuE9fxIqJtyBpUMWC20PsbqwHTWHRRMFkJ6hLCs9V99eZIL42mn\nkovh7ikbyoJjyoYLrbkHDe4wobtTxXYVS4SeUSdYJUIILM6wS4GbhkVJr9HVj/F39QkMSw6N\njQzNTEmrExkZHuGfmtBsyJCEVK/Izs2DDcaE1EZhBqmGvA1xbZKoTXLXucfXi2o3pW+LnsNS\nowOS81IbRya2bJrRv11ag/iMfiNeXT98SG6/KXOe7a7jc2Qiaz5MsApwFGwE1rn7L/eKupsf\nfbd664NwCgQfOAs4Go6DLhy+BRQCP4A2Gc6i8HUuv3gPbWNVNqhH5AlXKH257dkXP7nyyZUv\nTv3y5Z9fnP6ZbX796ajNV+qP/OjQK2c+Z9X9Et/Qn24d3nLdrs+Ge8oGe3BUtPp6zxCtPnLA\nO85LEUcltHaarEIbHS5WaZ3PhDsIXPDrUMEK2djx15aAlg+NhvNOnszd15l5EEw1D8s8Evk3\n1YxssvspP5rctXt0YnRiYuPRzy/u2XLMblZcoxuzj2OVtRG/LujealzhstHXwzTEc3TBR92B\nudlsZ2eFrKPKho8nte00/dsqfLWk1e7NcThw4W/VSkXNwWl+7ZyKiom0VaspFh3ofwkCGAXL\noiO5QCxErMuQCAQgdf/JHVRtL9P95uBQ7AqCUY4G7aCH2kmdcqU02WhQp4k9rtNXjVGD729D\nelpHubmciRK42Noo8Y3ZfC7WaUQqwBScPWF0PWlN5b9RnEjXewltSViky+uOJcPZuNuIxJUN\ndN94+ndA05AE9ThZck0WuPsPZf31/wSjBIAcJYSL3IL3bCUdZq+UdL64qB00EkMyITu4P6Un\nggeeNo4a5/2Wvl/A0PtRF78RbJyi1e/RdPzr106Gt7wmHJelxwdqqZvRMl5ZAWE3ojGdBiHW\njK4pIq1T7+bKhrXCvkp/pTiRHvT8jHXtbj6kcapM2mHcbUSy4RnXJ12D54R0dhWGsbGSF0Cq\ntZLVKth5M7Cmh7B/2YEfXaTRQPTQQDyvgljcGcEAbb/VMIBEHEA/UboPj0w4r3rxwcTfdpFM\n7ejXSUN6lmAhDbVZF5E46UXVgIZ4bDcVNY9VK15I9RKljEg0C6xLK+wTyXFlQ/A8i7Khf/NK\nf7UYkf6Wt1iUDZkjqpWOmsLdSqShXYe0g7ehc8+0ZPBAeot4prZ28iTe4PLOmygDVXVK4uEk\neAAVFTsXqE8+zD4HmYD4tgqm2DdGvOWxmR753SDPUJN9xtnqESK0SrwhErpdL6DF0GRN+O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/OQBnXciVgCHBsiclQVusHoE/oCQc1TDHA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\n0JN5rcBdW1VKxOdzAylVuRXjpwT3Q0iIdnSLU5DrvptKrlKOKecYkhpIls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plot shows an increase of the levels of Calcium with the increase of vitamin D in the blood."],"metadata":{"id":"jEYrqyvCsDi8"}},{"cell_type":"code","source":["#You can actually access the residuals of your model using *resid(data_lm)*:\n","#real calcicum data\n","print(\"real data for calcium\")\n","head(all$Calcium)\n","print(\"---------------------------------------------\")\n","\n","print(\"predicted data for calcium\")\n","#predicted Calcium data\n","head(fitted(fit1))\n","print(\"---------------------------------------------\")\n","\n","print(\"residual data for calcium\")\n","#residuals (diference real and predicted by the model fit1)\n","head(resid(fit1))\n","\n","\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":161},"id":"6X1wWoZnvlc8","executionInfo":{"status":"ok","timestamp":1717495383897,"user_tz":-120,"elapsed":393,"user":{"displayName":"toni Monleon","userId":"05269440626861479105"}},"outputId":"a43e1ff6-025c-486a-db55-ae58049d3508"},"execution_count":13,"outputs":[{"output_type":"stream","name":"stdout","text":["[1] \"real data for calcium\"\n"]},{"output_type":"display_data","data":{"text/html":["\n","
  1. 9.5
  2. 10
  3. 9
  4. 9.1
  5. 8.9
  6. 9.3
\n"],"text/markdown":"1. 9.5\n2. 10\n3. 9\n4. 9.1\n5. 8.9\n6. 9.3\n\n\n","text/latex":"\\begin{enumerate*}\n\\item 9.5\n\\item 10\n\\item 9\n\\item 9.1\n\\item 8.9\n\\item 9.3\n\\end{enumerate*}\n","text/plain":["[1] 9.5 10.0 9.0 9.1 8.9 9.3"]},"metadata":{}},{"output_type":"stream","name":"stdout","text":["[1] \"---------------------------------------------\"\n","[1] \"predicted data for calcium\"\n"]},{"output_type":"display_data","data":{"text/html":["
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9.44892949937279
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9.48693044822676
3
9.48131291665734
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1
0.051070500627209
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0.513069551773237
3
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\n"],"text/markdown":"1\n: 0.0510705006272092\n: 0.5130695517732373\n: -0.4813129166573394\n: -0.3540513663939625\n: -0.5398423159536556\n: -0.116380860575475\n\n","text/latex":"\\begin{description*}\n\\item[1] 0.051070500627209\n\\item[2] 0.513069551773237\n\\item[3] -0.481312916657339\n\\item[4] -0.354051366393962\n\\item[5] -0.539842315953655\n\\item[6] -0.116380860575475\n\\end{description*}\n","text/plain":[" 1 2 3 4 5 6 \n"," 0.0510705 0.5130696 -0.4813129 -0.3540514 -0.5398423 -0.1163809 "]},"metadata":{}}]},{"cell_type":"markdown","source":["Not bad!"],"metadata":{"id":"PD-_SriC7KBx"}},{"cell_type":"markdown","source":["**Multivariable adjusted models**\n","\n","Often, a significant association could be explained by confounders (http://www.medicine.mcgill.ca/epidemiology/joseph/courses/epib-621/confounding.pdf) or covariates. According to Wikipedia, a confounder is a variable that influences both the dependent variable and independent variable, causing a spurious association. Therefore, it is important to adjust for major confounders such as age and gender. The levels of vitamin D in the blood are dependent to age because older adults have lower vitamin D in blood compared to young adults.\n","\n","To conduct a multivariable-adjusted model I add other variables to the model, in this example, I will add age (RIAGENDR) and gender (RIDAGEYR)."],"metadata":{"id":"w0G-ZlNKsGeC"}},{"cell_type":"code","source":["fit2 <- lm(Calcium ~ vitD + RIAGENDR + RIDAGEYR, data = all) #RIAGENDR + RIDAGEYR use only numerical variables\n","summary(fit2)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":386},"id":"6-EqMuNEsNWd","executionInfo":{"status":"ok","timestamp":1717495398339,"user_tz":-120,"elapsed":371,"user":{"displayName":"toni Monleon","userId":"05269440626861479105"}},"outputId":"336a1300-de41-4abc-cf43-b040156f3c5c"},"execution_count":14,"outputs":[{"output_type":"display_data","data":{"text/plain":["\n","Call:\n","lm(formula = Calcium ~ vitD + RIAGENDR + RIDAGEYR, data = all)\n","\n","Residuals:\n"," Min 1Q Median 3Q Max \n","-2.50114 -0.22824 -0.00857 0.22354 2.69352 \n","\n","Coefficients:\n"," Estimate Std. Error t value Pr(>|t|) \n","(Intercept) 9.5339444 0.0143236 665.612 <2e-16 ***\n","vitD 0.0019034 0.0001310 14.526 <2e-16 ***\n","RIAGENDR -0.0653111 0.0065383 -9.989 <2e-16 ***\n","RIDAGEYR -0.0022455 0.0001581 -14.204 <2e-16 ***\n","---\n","Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n","\n","Residual standard error: 0.3639 on 12387 degrees of freedom\n","Multiple R-squared: 0.03619,\tAdjusted R-squared: 0.03596 \n","F-statistic: 155.1 on 3 and 12387 DF, p-value: < 2.2e-16\n"]},"metadata":{}}]},{"cell_type":"markdown","source":["The association between vitamin D and calcium remained significant after adjustment (p-value<0.05 for Gender and RIDAGEYR), suggesting that the association is independent (e.g., not explained) by age and gender (see R2 = 0.03, very low but p=2.2e-16 significative)"],"metadata":{"id":"d_KfR3LRs2y9"}},{"cell_type":"markdown","source":["**Stratifing analysis**\n","\n","To evaluate the association separately in men and women is necessary to conduct a stratified analysis. For this, I need to separate men and women into two different datasets and run linear regression for each group."],"metadata":{"id":"4Lg9ar0Zs9Wd"}},{"cell_type":"code","source":["allfem = all %>%\n"," filter(Gender == \"Females\")\n","allmal = all %>%\n"," filter(Gender == \"Males\")"],"metadata":{"id":"1Luf8A0is7EM"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["Linear regression in women and men"],"metadata":{"id":"f72as_9NtBT0"}},{"cell_type":"code","source":["#females\n","fitfem <- lm(Calcium ~ vitD + RIDAGEYR, data = allfem)\n","summary(fitfem)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":347},"id":"ByqIhQjetALc","executionInfo":{"status":"ok","timestamp":1717436397176,"user_tz":-120,"elapsed":412,"user":{"displayName":"toni Monleon","userId":"05269440626861479105"}},"outputId":"6f418b10-874e-4bbc-926b-5bc9837732c1"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["\n","Call:\n","lm(formula = Calcium ~ vitD + RIDAGEYR, data = allfem)\n","\n","Residuals:\n"," Min 1Q Median 3Q Max \n","-2.03557 -0.24115 -0.01084 0.22396 2.61555 \n","\n","Coefficients:\n"," Estimate Std. Error t value Pr(>|t|) \n","(Intercept) 9.2764092 0.0145412 637.940 <2e-16 ***\n","vitD 0.0019577 0.0001729 11.321 <2e-16 ***\n","RIDAGEYR 0.0005348 0.0002307 2.318 0.0205 * \n","---\n","Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n","\n","Residual standard error: 0.3727 on 6254 degrees of freedom\n","Multiple R-squared: 0.02247,\tAdjusted R-squared: 0.02216 \n","F-statistic: 71.89 on 2 and 6254 DF, p-value: < 2.2e-16\n"]},"metadata":{}}]},{"cell_type":"code","source":["#males\n","fitmal <- lm(Calcium ~ vitD + RIDAGEYR, data = allmal)\n","summary(fitmal)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":347},"id":"msqKjv7mtNxi","executionInfo":{"status":"ok","timestamp":1717436406272,"user_tz":-120,"elapsed":431,"user":{"displayName":"toni Monleon","userId":"05269440626861479105"}},"outputId":"00375e83-62c3-491f-a39f-8afa1da0eb2f"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["\n","Call:\n","lm(formula = Calcium ~ vitD + RIDAGEYR, data = allmal)\n","\n","Residuals:\n"," Min 1Q Median 3Q Max \n","-2.42787 -0.21555 -0.00506 0.21384 2.70896 \n","\n","Coefficients:\n"," Estimate Std. Error t value Pr(>|t|) \n","(Intercept) 9.6027158 0.0150801 636.78 <2e-16 ***\n","vitD 0.0016591 0.0001973 8.41 <2e-16 ***\n","RIDAGEYR -0.0049452 0.0002105 -23.49 <2e-16 ***\n","---\n","Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n","\n","Residual standard error: 0.3451 on 6131 degrees of freedom\n","Multiple R-squared: 0.08713,\tAdjusted R-squared: 0.08684 \n","F-statistic: 292.6 on 2 and 6131 DF, p-value: < 2.2e-16\n"]},"metadata":{}}]},{"cell_type":"markdown","source":["Also, is possible to observe that the association between vitamin D and calcium remained significant after adjustment, suggesting that the association is independent (e.g., not explained) by age and gender.\n","\n","In the case of females the assotiation (beta = 0.0005) is possitive and in the case of males the assotiation (beta = -0.0004) is negative, so the calcium is favored in the firs case and not favored in the second."],"metadata":{"id":"wQXQXZhzxjEH"}},{"cell_type":"markdown","source":["**Variable selection (Featured selection method)**\n","\n","Feature selection is the process of identifying and selecting a subset of input variables that are most relevant to the target variable. Perhaps the simplest case of feature selection is the case where there are numerical input variables and a numerical target for regression predictive modeling\n","\n","It is recommended to use a variable selection method when there is doubt whether the variables present multicollinearity (correlation between the independent variables), we can use different methods like step(model) using stepwise regression:"],"metadata":{"id":"LNXd5ZyMyAQ4"}},{"cell_type":"code","source":["#using the previous model\n","fit2 <- lm(Calcium ~ vitD + RIAGENDR + RIDAGEYR, data = all)\n","#we can select independent variables in order to test it\n","\n","step(fit2) #stepwise featureselection model"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":277},"id":"yqWoBKdPyzI7","executionInfo":{"status":"ok","timestamp":1717436520962,"user_tz":-120,"elapsed":476,"user":{"displayName":"toni Monleon","userId":"05269440626861479105"}},"outputId":"ea85f56e-8910-449e-82ad-e62f3ec36329"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Start: AIC=-25050.28\n","Calcium ~ vitD + RIAGENDR + RIDAGEYR\n","\n"," Df Sum of Sq RSS AIC\n"," 1640.0 -25050\n","- RIAGENDR 1 13.210 1653.2 -24953\n","- RIDAGEYR 1 26.712 1666.7 -24852\n","- vitD 1 27.937 1667.9 -24843\n"]},{"output_type":"display_data","data":{"text/plain":["\n","Call:\n","lm(formula = Calcium ~ vitD + RIAGENDR + RIDAGEYR, data = all)\n","\n","Coefficients:\n","(Intercept) vitD RIAGENDR RIDAGEYR \n"," 9.533944 0.001903 -0.065311 -0.002246 \n"]},"metadata":{}}]},{"cell_type":"markdown","source":["In this case is possible observe that the parsimonious model is lm(formula = Calcium ~ vitD + RIAGENDR + RIDAGEYR, data = all)\n","\n","and none of the previously selected variables have been removed\n","\n"],"metadata":{"id":"wD6-24iozl7W"}},{"cell_type":"markdown","source":["This model is simple selected with the same R2 = 0.03596 and p<0.05"],"metadata":{"id":"x9eJgrXLz7v_"}},{"cell_type":"markdown","source":["**Model metrics for regression**\n","\n","Regression predictive modeling are those problems that involve predicting a numeric value. Metrics for regression involve calculating an error score to summarize the predictive skill of a model. How to calculate and report mean squared error, root mean squared error, and mean absolute error.\n","\n","See more in https://www.analyticsvidhya.com/blog/2021/05/know-the-best-evaluation-metrics-for-your-regression-model/\n","\n","The most used metrics in Regression are\n","\n","* **Mean Absolute Error(MAE)**: Mean Absolute Error (MAE) is a measure of the average size of the mistakes in a collection of predictions, without taking their direction into account. It is measured as the average absolute difference between the predicted values and the actual values and is used to assess the effectiveness of a regression model.\n","* **Mean Squared Error(MSE)**: The Mean Squared Error measures how close a regression line is to a set of data points. It is a risk function corresponding to the expected value of the squared error loss. Mean square error is calculated by taking the average, specifically the mean, of errors squared from data as it relates to a function\n","* **RMSE: Root Mean Squared Error (RMSE)**: is one of the two main performance indicators for a regression model. It measures the average difference between values predicted by a model and the actual values. It provides an estimation of how well the model is able to predict the target value (accuracy).\n","* **R squared**: R-Squared (R² or the coefficient of determination) is a statistical measure in a regression model that determines the proportion of variance in the dependent variable that can be explained by the independent variable. In other words, r-squared shows how well the data fit the regression model (the goodness of fit).\n","* **Adjusted R Squares**: The adjusted R-squared is a modified version of R-squared that adjusts for predictors that are not significant in a regression model. Compared to a model with additional input variables, a lower adjusted R-squared indicates that the additional input variables are not adding value to the model.\n","\n","The only metric that the table of R, above, offers is R2 and adjusted R2. The others must be calculated.\n","\n","One the most used is RMSE:\n","\n","See the Mean Squared Error(MSE)\n","\n","![](https://lh3.googleusercontent.com/-JBio3Q_1FiI/YB2oQKEmRBI/AAAAAAAAAkM/c8KJ3wPwtMEd3Ik0nYMMdmr_pRqMF6MlQCLcBGAsYHQ/w550-h177/image.png)\n","\n","\n","and RMSE:\n","\n","\n","![](https://media.geeksforgeeks.org/wp-content/uploads/20200622171741/RMSE1.jpg)\n","\n"],"metadata":{"id":"FJ_uIsui-b48"}},{"cell_type":"code","source":["#RMSE is a metrics measure of the model\n","#As RMSE is clear by the name itself, that it is a simple square root of mean squared error.\n","\n","paste(\"RMSE for Calcium is: \", sqrt(mean((all$Calcium - fitted(fit2))^2)))\n","\n","print(\"-----------------------------\")\n","print(\"Summary for calcium\")\n","summary(all$Calcium)\n","\n"],"metadata":{"id":"d0N--EVRuCAb","colab":{"base_uri":"https://localhost:8080/","height":108},"executionInfo":{"status":"ok","timestamp":1717495565778,"user_tz":-120,"elapsed":477,"user":{"displayName":"toni Monleon","userId":"05269440626861479105"}},"outputId":"005d15b6-6206-4fcf-b94b-1ed71799cc55"},"execution_count":15,"outputs":[{"output_type":"display_data","data":{"text/html":["'RMSE for Calcium is: 0.3638008954901'"],"text/markdown":"'RMSE for Calcium is: 0.3638008954901'","text/latex":"'RMSE for Calcium is: 0.3638008954901'","text/plain":["[1] \"RMSE for Calcium is: 0.3638008954901\""]},"metadata":{}},{"output_type":"stream","name":"stdout","text":["[1] \"-----------------------------\"\n","[1] \"Summary for calcium\"\n"]},{"output_type":"display_data","data":{"text/plain":[" Min. 1st Qu. Median Mean 3rd Qu. Max. \n"," 7.000 9.200 9.400 9.454 9.700 12.100 "]},"metadata":{}}]},{"cell_type":"markdown","source":["Not bad a RMSE of 0.36"],"metadata":{"id":"60QU_9bBATjc"}},{"cell_type":"markdown","source":["Now predict a new value in function of the model 2 (fit2) and present a 95% confidence interval for the prediction"],"metadata":{"id":"TVMit4BE0MX2"}},{"cell_type":"code","source":["head(all) #our dataframe all"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":286},"id":"ni-rU72C04vt","executionInfo":{"status":"ok","timestamp":1717436553839,"user_tz":-120,"elapsed":389,"user":{"displayName":"toni Monleon","userId":"05269440626861479105"}},"outputId":"39f51e26-62ef-4683-e72b-aaf0069713df"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/html":["\n","\n","\n","\t\n","\t\n","\n","\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\n","
A data.frame: 6 × 7
SEQNwaveRIAGENDRRIDAGEYRvitDCalciumGender
<dbl><chr><dbl><dbl><dbl><dbl><fct>
1414752007-200826258.8 9.5Females
2414772007-200817181.810.0Males
3414792007-200815278.4 9.0Males
4414822007-200816461.9 9.1Males
5414832007-200816653.3 8.9Males
6414852007-200823039.1 9.3Females
\n"],"text/markdown":"\nA data.frame: 6 × 7\n\n| | SEQN <dbl> | wave <chr> | RIAGENDR <dbl> | RIDAGEYR <dbl> | vitD <dbl> | Calcium <dbl> | Gender <fct> |\n|---|---|---|---|---|---|---|---|\n| 1 | 41475 | 2007-2008 | 2 | 62 | 58.8 | 9.5 | Females |\n| 2 | 41477 | 2007-2008 | 1 | 71 | 81.8 | 10.0 | Males |\n| 3 | 41479 | 2007-2008 | 1 | 52 | 78.4 | 9.0 | Males |\n| 4 | 41482 | 2007-2008 | 1 | 64 | 61.9 | 9.1 | Males |\n| 5 | 41483 | 2007-2008 | 1 | 66 | 53.3 | 8.9 | Males |\n| 6 | 41485 | 2007-2008 | 2 | 30 | 39.1 | 9.3 | Females |\n\n","text/latex":"A data.frame: 6 × 7\n\\begin{tabular}{r|lllllll}\n & SEQN & wave & RIAGENDR & RIDAGEYR & vitD & Calcium & Gender\\\\\n & & & & & & & \\\\\n\\hline\n\t1 & 41475 & 2007-2008 & 2 & 62 & 58.8 & 9.5 & Females\\\\\n\t2 & 41477 & 2007-2008 & 1 & 71 & 81.8 & 10.0 & Males \\\\\n\t3 & 41479 & 2007-2008 & 1 & 52 & 78.4 & 9.0 & Males \\\\\n\t4 & 41482 & 2007-2008 & 1 & 64 & 61.9 & 9.1 & Males \\\\\n\t5 & 41483 & 2007-2008 & 1 & 66 & 53.3 & 8.9 & Males \\\\\n\t6 & 41485 & 2007-2008 & 2 & 30 & 39.1 & 9.3 & Females\\\\\n\\end{tabular}\n","text/plain":[" SEQN wave RIAGENDR RIDAGEYR vitD Calcium Gender \n","1 41475 2007-2008 2 62 58.8 9.5 Females\n","2 41477 2007-2008 1 71 81.8 10.0 Males \n","3 41479 2007-2008 1 52 78.4 9.0 Males \n","4 41482 2007-2008 1 64 61.9 9.1 Males \n","5 41483 2007-2008 1 66 53.3 8.9 Males \n","6 41485 2007-2008 2 30 39.1 9.3 Females"]},"metadata":{}}]},{"cell_type":"code","source":["#model lm(formula = Calcium ~ vitD + Gender + RIDAGEYR\n","#predict 5 first values of calcium\n","\n","Calcium ~ vitD + RIAGENDR + RIDAGEYR\n","\n","predict(fit2,as.data.frame(all[1:5,c(5,3,4)]),interval=\"confidence\")\n","\n","#real values\n","print(\"--------------------------------------------\")\n","print(\"Real values for Calcium []\")\n","all$Calcium[1:5]\n","\n","\n","\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":296},"id":"YQSVgP-p0L3G","executionInfo":{"status":"ok","timestamp":1717495841864,"user_tz":-120,"elapsed":353,"user":{"displayName":"toni Monleon","userId":"05269440626861479105"}},"outputId":"1b2e50b6-ab3f-4cf8-fcd4-9e501954d904"},"execution_count":16,"outputs":[{"output_type":"display_data","data":{"text/plain":["Calcium ~ vitD + RIAGENDR + RIDAGEYR"]},"metadata":{}},{"output_type":"display_data","data":{"text/html":["\n","\n","\n","\t\n","\n","\n","\t\n","\t\n","\t\n","\t\n","\t\n","\n","
A matrix: 5 × 3 of type dbl
fitlwrupr
19.3760239.3654039.386643
29.4649039.4519269.477881
39.5010979.4908529.511341
49.4427439.4317519.453736
59.4218839.4101719.433595
\n"],"text/markdown":"\nA matrix: 5 × 3 of type dbl\n\n| | fit | lwr | upr |\n|---|---|---|---|\n| 1 | 9.376023 | 9.365403 | 9.386643 |\n| 2 | 9.464903 | 9.451926 | 9.477881 |\n| 3 | 9.501097 | 9.490852 | 9.511341 |\n| 4 | 9.442743 | 9.431751 | 9.453736 |\n| 5 | 9.421883 | 9.410171 | 9.433595 |\n\n","text/latex":"A matrix: 5 × 3 of type dbl\n\\begin{tabular}{r|lll}\n & fit & lwr & upr\\\\\n\\hline\n\t1 & 9.376023 & 9.365403 & 9.386643\\\\\n\t2 & 9.464903 & 9.451926 & 9.477881\\\\\n\t3 & 9.501097 & 9.490852 & 9.511341\\\\\n\t4 & 9.442743 & 9.431751 & 9.453736\\\\\n\t5 & 9.421883 & 9.410171 & 9.433595\\\\\n\\end{tabular}\n","text/plain":[" fit lwr upr \n","1 9.376023 9.365403 9.386643\n","2 9.464903 9.451926 9.477881\n","3 9.501097 9.490852 9.511341\n","4 9.442743 9.431751 9.453736\n","5 9.421883 9.410171 9.433595"]},"metadata":{}},{"output_type":"stream","name":"stdout","text":["[1] \"--------------------------------------------\"\n","[1] \"Real values for Calcium []\"\n"]},{"output_type":"display_data","data":{"text/html":["\n","
  1. 9.5
  2. 10
  3. 9
  4. 9.1
  5. 8.9
\n"],"text/markdown":"1. 9.5\n2. 10\n3. 9\n4. 9.1\n5. 8.9\n\n\n","text/latex":"\\begin{enumerate*}\n\\item 9.5\n\\item 10\n\\item 9\n\\item 9.1\n\\item 8.9\n\\end{enumerate*}\n","text/plain":["[1] 9.5 10.0 9.0 9.1 8.9"]},"metadata":{}}]},{"cell_type":"markdown","source":["## Non linear regression in R\n","\n","The nonlinear regression analysis in R is the process of building a nonlinear function. On the basis of independent variables, this process predicts the outcome of a dependent variable with the help of model parameters that depend on the degree of relationship among variables.\n","\n","See in https://techvidvan.com/tutorials/nonlinear-regression-in-r/ and in https://rpubs.com/abbyhudak/nonlinreg\n","\n","![](https://techvidvan.com/tutorials/wp-content/uploads/sites/2/2020/05/ggplot2-package-to-plot-the-drc-data.jpg)\n"],"metadata":{"id":"8emihEqs0Frg"}},{"cell_type":"markdown","source":["## Other type of regressions with R\n","\n","\n","* Ridge Regression\n","* Lasso Regression\n","* Elastic Net Regression\n","\n","\n","See in https://www.pluralsight.com/guides/linear-lasso-and-ridge-regression-with-r and in https://www.andreaperlato.com/theorypost/ridge-and-lasso-regression/\n","\n","\n","![](https://www.andreaperlato.com/img/ridge.png)\n","\n","\n","\n","\n","\n","\n","\n"],"metadata":{"id":"PPHWmXrey0_b"}},{"cell_type":"markdown","source":["-------------------------------------------------------------------------------------------------------------------------------\n","\n","\n","--------------------------------------------------------------------------------"],"metadata":{"id":"Nw_aLkCaBxe5"}},{"cell_type":"markdown","source":["# Cluster analysis\n","\n","See in: https://uc-r.github.io/hc_clustering\n","\n","Cluster analysis is a data analysis technique that explores the naturally occurring groups within a data set known as clusters. Cluster analysis doesn't need to group data points into any predefined groups, which means that it is an unsupervised learning method.\n","\n","\n","![](https://miro.medium.com/v2/resize:fit:638/0*G1N4dzkweANLGh66.png)"],"metadata":{"id":"rIRh-iPvOiv4"}},{"cell_type":"markdown","source":["**Hierarchical Cluster Analysis**\n","\n","Hierarchical clustering is an alternative approach to k-means clustering for identifying groups in the dataset. It does not require us to pre-specify the number of clusters to be generated as is required by the k-means approach. Furthermore, hierarchical clustering has an added advantage over K-means clustering in that it results in an attractive tree-based representation of the observations, called a dendrogram. (See in: https://www.geeksforgeeks.org/difference-between-k-means-and-hierarchical-clustering/ and https://medium.com/@waziriphareeyda/difference-between-k-means-and-hierarchical-clustering-edfec55a34f8)\n"],"metadata":{"id":"E1osgb66Or3W"}},{"cell_type":"code","source":["library(tidyverse) # data manipulation\n","library(cluster) # clustering algorithms\n","#install.packages(\"factoextra\")\n","library(factoextra) # clustering visualization\n","#install.packages(\"dendextend\")\n","library(dendextend) # for comparing two dendrograms"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"mN-BVp5MOmDE","executionInfo":{"status":"ok","timestamp":1717496236231,"user_tz":-120,"elapsed":365,"user":{"displayName":"toni Monleon","userId":"05269440626861479105"}},"outputId":"f5b255f8-09ab-4560-e169-18f4eb98281d"},"execution_count":17,"outputs":[{"output_type":"stream","name":"stderr","text":["Welcome! Want to learn more? See two factoextra-related books at https://goo.gl/ve3WBa\n","\n","\n","---------------------\n","Welcome to dendextend version 1.17.1\n","Type citation('dendextend') for how to cite the package.\n","\n","Type browseVignettes(package = 'dendextend') for the package vignette.\n","The github page is: https://github.com/talgalili/dendextend/\n","\n","Suggestions and bug-reports can be submitted at: https://github.com/talgalili/dendextend/issues\n","You may ask questions at stackoverflow, use the r and dendextend tags: \n","\t https://stackoverflow.com/questions/tagged/dendextend\n","\n","\tTo suppress this message use: suppressPackageStartupMessages(library(dendextend))\n","---------------------\n","\n","\n","\n","Attaching package: ‘dendextend’\n","\n","\n","The following object is masked from ‘package:stats’:\n","\n"," cutree\n","\n","\n"]}]},{"cell_type":"markdown","source":["\n","**Clustering** is a technique to club similar data points into one group and separate out dissimilar observations into different groups or clusters.\n","\n","In Hierarchical Clustering, clusters are created such that they have a predetermined ordering i.e. a hierarchy.\n","\n","For example, consider the concept hierarchy of a library. A library has many sections, each section would have many books, and the books would be grouped according to their subject, let’s say. This forms a hierarchy. In Hierarchical Clustering, this hierarchy of clusters can either be created from top to bottom, or vice-versa. Hence, it’s two types namely – Divisive and Agglomerative. Let’s discuss it in detail.\n","\n","\n","\n","Hierarchical clustering can be divided into two main types: agglomerative and divisive (See https://www.r-bloggers.com/2017/12/how-to-perform-hierarchical-clustering-using-r/) and there are several methods and possibilities in them.\n","\n","* **Agglomerative clustering**: t’s also known as Hierarchical Agglomerative Clustering (HAC) or AGNES (acronym for Agglomerative Nesting). In this method, each observation is assigned to its own cluster. Then, the similarity (or distance) between each of the clusters is computed and the two most similar clusters are merged into one. Finally, steps 2 and 3 are repeated until there is only one cluster left. It works in a bottom-up manner. That is, each object is initially considered as a single-element cluster (leaf). At each step of the algorithm, the two clusters that are the most similar are combined into a new bigger cluster (nodes). This procedure is iterated until all points are member of just one single big cluster (root) (see figure below). The result is a tree which can be plotted as a dendrogram.\n","\n","* **Divisive hierarchical clustering**: It’s also known as DIANA (Divise Analysis) and it works in a top-down manner. The algorithm is an inverse order of AGNES. It begins with the root, in which all objects are included in a single cluster. At each step of iteration, the most heterogeneous cluster is divided into two. The process is iterated until all objects are in their own cluster (see figure below).\n","\n","* IMPORTANT: Please note that Divisive method is good for identifying large clusters while Agglomerative method is good for identifying small clusters. We will proceed with Agglomerative Clustering for the rest of the article. Since, HAC’s account for the majority of hierarchical clustering algorithms while Divisive methods are rarely used. I think now we have a general overview of Hierarchical Clustering. Let’s also get ourselves familiarized with the algorithm for it."],"metadata":{"id":"PIfa_kGXPGM6"}},{"cell_type":"markdown","source":["We’ll use a data set called ‘Freedman’ from the ‘car’ package. The ‘Freedman’ data frame has 110 rows and 4 columns. The observations are U. S. metropolitan areas with 1968 populations of 250,000 or more. There are some missing data. (Make sure you install the car package before proceeding)\n","\n"],"metadata":{"id":"It1uZXbbPQfp"}},{"cell_type":"code","source":["#data\n","library(car)\n","df <- Freedman\n","#To remove any missing value that might be present in the data, type this:\n","df <- na.omit(df)\n","head(df)\n","\n","\n","\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":286},"id":"msXBwOJsPSUl","executionInfo":{"status":"ok","timestamp":1717496995264,"user_tz":-120,"elapsed":372,"user":{"displayName":"toni Monleon","userId":"05269440626861479105"}},"outputId":"8abafab9-5d73-46a0-87f1-5ff067fa6ced"},"execution_count":22,"outputs":[{"output_type":"display_data","data":{"text/html":["\n","\n","\n","\t\n","\t\n","\n","\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\n","
A data.frame: 6 × 4
populationnonwhitedensitycrime
<int><dbl><int><int>
Akron 675 7.3 7462602
Albany 713 2.6 3221388
Allentown 534 0.8 4911182
Anaheim1261 1.416123341
Atlanta133022.8 7702805
Bakersfield 331 7.0 413306
\n"],"text/markdown":"\nA data.frame: 6 × 4\n\n| | population <int> | nonwhite <dbl> | density <int> | crime <int> |\n|---|---|---|---|---|\n| Akron | 675 | 7.3 | 746 | 2602 |\n| Albany | 713 | 2.6 | 322 | 1388 |\n| Allentown | 534 | 0.8 | 491 | 1182 |\n| Anaheim | 1261 | 1.4 | 1612 | 3341 |\n| Atlanta | 1330 | 22.8 | 770 | 2805 |\n| Bakersfield | 331 | 7.0 | 41 | 3306 |\n\n","text/latex":"A data.frame: 6 × 4\n\\begin{tabular}{r|llll}\n & population & nonwhite & density & crime\\\\\n & & & & \\\\\n\\hline\n\tAkron & 675 & 7.3 & 746 & 2602\\\\\n\tAlbany & 713 & 2.6 & 322 & 1388\\\\\n\tAllentown & 534 & 0.8 & 491 & 1182\\\\\n\tAnaheim & 1261 & 1.4 & 1612 & 3341\\\\\n\tAtlanta & 1330 & 22.8 & 770 & 2805\\\\\n\tBakersfield & 331 & 7.0 & 41 & 3306\\\\\n\\end{tabular}\n","text/plain":[" population nonwhite density crime\n","Akron 675 7.3 746 2602 \n","Albany 713 2.6 322 1388 \n","Allentown 534 0.8 491 1182 \n","Anaheim 1261 1.4 1612 3341 \n","Atlanta 1330 22.8 770 2805 \n","Bakersfield 331 7.0 41 3306 "]},"metadata":{}}]},{"cell_type":"code","source":["str(df)\n","summary(df)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":275},"id":"6NjEh-fmzAHI","executionInfo":{"status":"ok","timestamp":1717496974793,"user_tz":-120,"elapsed":466,"user":{"displayName":"toni Monleon","userId":"05269440626861479105"}},"outputId":"f726f3fe-8a6d-4cb7-dbf1-2f1b50b716b1"},"execution_count":21,"outputs":[{"output_type":"stream","name":"stdout","text":["'data.frame':\t100 obs. of 4 variables:\n"," $ population: int 675 713 534 1261 1330 331 1981 315 305 739 ...\n"," $ nonwhite : num 7.3 2.6 0.8 1.4 22.8 7 21.6 20.7 0.6 32.1 ...\n"," $ density : int 746 322 491 1612 770 41 877 240 147 272 ...\n"," $ crime : int 2602 1388 1182 3341 2805 3306 4256 2117 1063 2285 ...\n"," - attr(*, \"na.action\")= 'omit' Named int [1:10] 3 16 17 23 30 34 42 47 100 101\n"," ..- attr(*, \"names\")= chr [1:10] \"Albuquerque\" \"Charleston.SC\" \"Charleston.W.VA\" \"Columbia\" ...\n"]},{"output_type":"display_data","data":{"text/plain":[" population nonwhite density crime \n"," Min. : 270.0 Min. : 0.30 Min. : 37.0 Min. : 458 \n"," 1st Qu.: 398.8 1st Qu.: 3.40 1st Qu.: 266.5 1st Qu.:2100 \n"," Median : 664.0 Median : 7.30 Median : 412.0 Median :2762 \n"," Mean : 1136.0 Mean :10.66 Mean : 765.7 Mean :2733 \n"," 3rd Qu.: 1167.8 3rd Qu.:14.82 3rd Qu.: 773.2 3rd Qu.:3318 \n"," Max. :11551.0 Max. :64.30 Max. :13087.0 Max. :5441 "]},"metadata":{}}]},{"cell_type":"markdown","source":["Agglomerative Hierarchical Clustering:\n","\n","We can perform agglomerative HC with hclust: a)First we compute the dissimilarity values with dist and then feed these values into hclust and b)specify the agglomeration method to be used (i.e. “complete”, “average”, “single”, “ward.D”). We can then plot the dendrogram."],"metadata":{"id":"UyNGB8P_PbTY"}},{"cell_type":"code","source":["# Dissimilarity matrix\n","d <- dist(df, method = \"euclidean\")\n","\n","# Hierarchical clustering using Complete Linkage\n","hc1 <- hclust(d, method = \"complete\" )\n","\n","# Plot the obtained dendrogram\n","plot(hc1, cex = 0.6, hang = -1)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":437},"id":"HspVdhLFPTIG","executionInfo":{"status":"ok","timestamp":1717496944300,"user_tz":-120,"elapsed":470,"user":{"displayName":"toni Monleon","userId":"05269440626861479105"}},"outputId":"7c7e6247-2590-4a1a-99d7-26e45311d479"},"execution_count":20,"outputs":[{"output_type":"display_data","data":{"text/plain":["Plot with title “Cluster Dendrogram”"],"image/png":"iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAMAAADKOT/pAAADAFBMVEUAAAABAQECAgIDAwME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OF09LTk5mJyK8M2bzLPru2MntEMPNa3xA5uMp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\nxLV4ojp56G/F695G8NPyKk0GQR7Yp2J3JsNwLFnHLqIfz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gQQJEAAQQIEECRAAEECBBAkQABBAgQQJEAAQQIEECRAAEECBBAkQABBAgQQ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\nWnsaggQIIEiAAIIECPg/Mu91btzFthIAAAAASUVORK5CYII="},"metadata":{"image/png":{"width":420,"height":420}}}]},{"cell_type":"markdown","source":["Alternatively, we can use the **agnes** function. These functions behave very similarly; however, with the agnes function you can also get the agglomerative coefficient, which measures the amount of clustering structure found (values closer to 1 suggest strong clustering structure)."],"metadata":{"id":"ql7mgNnYPeyJ"}},{"cell_type":"code","source":["# Compute with agnes\n","hc2 <- agnes(df, method = \"complete\")\n","\n","# Agglomerative coefficient\n","hc2$ac"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":34},"id":"GRFqMZjEPh96","executionInfo":{"status":"ok","timestamp":1717497007051,"user_tz":-120,"elapsed":373,"user":{"displayName":"toni Monleon","userId":"05269440626861479105"}},"outputId":"a5f0b8d4-529d-4fe9-b2db-feb0999e285a"},"execution_count":23,"outputs":[{"output_type":"display_data","data":{"text/html":["0.949549058639527"],"text/markdown":"0.949549058639527","text/latex":"0.949549058639527","text/plain":["[1] 0.9495491"]},"metadata":{}}]},{"cell_type":"markdown","source":["This allows us to find certain hierarchical clustering methods that can identify stronger clustering structures. Here we see that Ward’s method identifies the strongest clustering structure of the four methods assessed.\n","\n","Nowadays Ward's method is very popular: Ward's minimum variance criterion minimizes the total within-cluster variance. To implement this method, at each step find the pair of clusters that leads to minimum increase in total within-cluster variance after merging. This increase is a weighted squared distance between cluster centers."],"metadata":{"id":"ia5Ii8LJPrH1"}},{"cell_type":"code","source":["# methods to assess\n","m <- c( \"average\", \"single\", \"complete\", \"ward\")\n","names(m) <- c( \"average\", \"single\", \"complete\", \"ward\")\n","\n","# function to compute coefficient\n","ac <- function(x) {\n"," agnes(df, method = x)$ac\n","}\n","\n","map_dbl(m, ac)\n","## average single complete ward\n","## 0.7379371 0.6276128 0.8531583 0.9346210"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":34},"id":"1L44HZoTPt1u","executionInfo":{"status":"ok","timestamp":1717497020538,"user_tz":-120,"elapsed":365,"user":{"displayName":"toni Monleon","userId":"05269440626861479105"}},"outputId":"7ee53c32-99a7-41df-ed09-ec019e9f49b8"},"execution_count":24,"outputs":[{"output_type":"display_data","data":{"text/html":["
average
0.949142929549995
single
0.948484323129533
complete
0.949549058639527
ward
0.9679934634633
\n"],"text/markdown":"average\n: 0.949142929549995single\n: 0.948484323129533complete\n: 0.949549058639527ward\n: 0.9679934634633\n\n","text/latex":"\\begin{description*}\n\\item[average] 0.949142929549995\n\\item[single] 0.948484323129533\n\\item[complete] 0.949549058639527\n\\item[ward] 0.9679934634633\n\\end{description*}\n","text/plain":[" average single complete ward \n","0.9491429 0.9484843 0.9495491 0.9679935 "]},"metadata":{}}]},{"cell_type":"code","source":["#Similar to before we can visualize the dendrogram:\n","\n","hc3 <- agnes(df, method = \"ward\")\n","pltree(hc3, cex = 0.6, hang = -1, main = \"Dendrogram of agnes\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":437},"id":"Uawghuh0PyQt","executionInfo":{"status":"ok","timestamp":1717497109666,"user_tz":-120,"elapsed":507,"user":{"displayName":"toni Monleon","userId":"05269440626861479105"}},"outputId":"0d6b083e-df5a-4e73-a256-872b25b932b8"},"execution_count":25,"outputs":[{"output_type":"display_data","data":{"text/plain":["Plot with title “Dendrogram of agnes”"],"image/png":"iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAMAAADKOT/pAAADAFBMVEUAAAABAQECAgIDAwME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P4Z7wa2Ky2MF8/RDmaC2HzaZNQA8JZ16JmRc/Recp5aTw+5nTg67Ka1W1/BOXCiN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\nFSNc+Yvh4cnm01b7UIxhk7pqWTv1oVL2coT4K7llJuXxdnvWEp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A+VzRESm5c984lssB7ldYx7/ZF5e2z16oOldJHH7tcxkTHbog8559p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\nKXPDqxfG4u8AAAAASUVORK5CYII="},"metadata":{"image/png":{"width":420,"height":420}}}]},{"cell_type":"markdown","source":["**Divisive Hierarchical Clustering**\n","\n","The R function diana provided by the cluster package allows us to perform divisive hierarchical clustering. diana works similar to agnes; however, there is no method to provide."],"metadata":{"id":"jaEcK7iFP7rd"}},{"cell_type":"code","source":["# compute divisive hierarchical clustering\n","hc4 <- diana(df)\n","\n","# Divise coefficient; amount of clustering structure found\n","hc4$dc\n","## [1] 0.8514345\n","\n","# plot dendrogram\n","pltree(hc4, cex = 0.6, hang = -1, main = \"Dendrogram of diana\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":454},"id":"blhR3h5FP8a2","executionInfo":{"status":"ok","timestamp":1717497120621,"user_tz":-120,"elapsed":475,"user":{"displayName":"toni Monleon","userId":"05269440626861479105"}},"outputId":"11b4a58b-2097-42f3-f919-72915ceeb262"},"execution_count":26,"outputs":[{"output_type":"display_data","data":{"text/html":["0.950483606737722"],"text/markdown":"0.950483606737722","text/latex":"0.950483606737722","text/plain":["[1] 0.9504836"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Plot with title “Dendrogram of diana”"],"image/png":"iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAMAAADKOT/pAAADAFBMVEUAAAABAQECAgIDAwME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IfMGa86shlUb3KyxF4kNEexFhjmR1REgiWqTDpps0KAaDLPGRaCVdBY649mzne86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\nBAgQEiBASIAAIQEChA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with Dendrograms\n","# Ward's method\n","hc5 <- hclust(d, method = \"ward.D2\" )\n","\n","# Cut tree into 3 groups\n","sub_grp <- cutree(hc5, k = 4)\n","\n","# Number of members in each cluster\n","table(sub_grp)\n","## sub_grp\n","## 1 2 3 4\n","## 42 51 6 1\n","\n","df %>%\n"," mutate(cluster = sub_grp) %>%\n"," head"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":342},"id":"18qV0REsP-2V","executionInfo":{"status":"ok","timestamp":1717497298832,"user_tz":-120,"elapsed":353,"user":{"displayName":"toni Monleon","userId":"05269440626861479105"}},"outputId":"031fb20f-312d-4167-8ada-11f6fd4e00b1"},"execution_count":36,"outputs":[{"output_type":"display_data","data":{"text/plain":["sub_grp\n"," 1 2 3 4 \n","42 51 6 1 "]},"metadata":{}},{"output_type":"display_data","data":{"text/html":["\n","\n","\n","\t\n","\t\n","\n","\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\n","
A data.frame: 6 × 5
populationnonwhitedensitycrimecluster
<int><dbl><int><int><int>
Akron 675 7.3 74626021
Albany 713 2.6 32213881
Allentown 534 0.8 49111821
Anaheim1261 1.4161233412
Atlanta133022.8 77028052
Bakersfield 331 7.0 4133062
\n"],"text/markdown":"\nA data.frame: 6 × 5\n\n| | population <int> | nonwhite <dbl> | density <int> | crime <int> | cluster <int> |\n|---|---|---|---|---|---|\n| Akron | 675 | 7.3 | 746 | 2602 | 1 |\n| Albany | 713 | 2.6 | 322 | 1388 | 1 |\n| Allentown | 534 | 0.8 | 491 | 1182 | 1 |\n| Anaheim | 1261 | 1.4 | 1612 | 3341 | 2 |\n| Atlanta | 1330 | 22.8 | 770 | 2805 | 2 |\n| Bakersfield | 331 | 7.0 | 41 | 3306 | 2 |\n\n","text/latex":"A data.frame: 6 × 5\n\\begin{tabular}{r|lllll}\n & population & nonwhite & density & crime & cluster\\\\\n & & & & & \\\\\n\\hline\n\tAkron & 675 & 7.3 & 746 & 2602 & 1\\\\\n\tAlbany & 713 & 2.6 & 322 & 1388 & 1\\\\\n\tAllentown & 534 & 0.8 & 491 & 1182 & 1\\\\\n\tAnaheim & 1261 & 1.4 & 1612 & 3341 & 2\\\\\n\tAtlanta & 1330 & 22.8 & 770 & 2805 & 2\\\\\n\tBakersfield & 331 & 7.0 & 41 & 3306 & 2\\\\\n\\end{tabular}\n","text/plain":[" population nonwhite density crime cluster\n","Akron 675 7.3 746 2602 1 \n","Albany 713 2.6 322 1388 1 \n","Allentown 534 0.8 491 1182 1 \n","Anaheim 1261 1.4 1612 3341 2 \n","Atlanta 1330 22.8 770 2805 2 \n","Bakersfield 331 7.0 41 3306 2 "]},"metadata":{}}]},{"cell_type":"markdown","source":["It’s also possible to draw the dendrogram with a border around the 4 clusters. The argument border is used to specify the border colors for the rectangles:"],"metadata":{"id":"FS944YvNQRoK"}},{"cell_type":"code","source":["plot(hc5, cex = 0.6)\n","rect.hclust(hc5, k = 4, border = 2:5)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":437},"id":"LySTL38wQPK8","executionInfo":{"status":"ok","timestamp":1717497323993,"user_tz":-120,"elapsed":909,"user":{"displayName":"toni Monleon","userId":"05269440626861479105"}},"outputId":"26066238-a027-47c1-993e-2f0b06f6ab10"},"execution_count":37,"outputs":[{"output_type":"display_data","data":{"text/plain":["Plot with title “Cluster Dendrogram”"],"image/png":"iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAIAAAByhViMAAAACXBIWXMAABJ0AAASdAHeZh94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"},"metadata":{"image/png":{"width":420,"height":420}}}]},{"cell_type":"markdown","source":["**K-means**\n","\n","K-means clustering (MacQueen 1967) is one of the most commonly used unsupervised machine learning algorithm for partitioning a given data set into a set of k groups (i.e. k clusters), where k represents the number of groups pre-specified by the analyst. It classifies objects in multiple groups (i.e., clusters), such that objects within the same cluster are as similar as possible (i.e., high intra-class similarity), whereas objects from different clusters are as dissimilar as possible (i.e., low inter-class similarity). In k-means clustering, each cluster is represented by its center (i.e, centroid) which corresponds to the mean of points assigned to the cluster.\n","See in: https://www.datanovia.com/en/lessons/k-means-clustering-in-r-algorith-and-practical-examples/\n","\n","We can also use the fviz_cluster function from the factoextra package to visualize the result in a scatter plot."],"metadata":{"id":"_PopOspdQWmO"}},{"cell_type":"code","source":["fviz_cluster(list(data = df, cluster = sub_grp))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":437},"id":"91_pvVSnQXpr","executionInfo":{"status":"ok","timestamp":1717497425724,"user_tz":-120,"elapsed":1216,"user":{"displayName":"toni Monleon","userId":"05269440626861479105"}},"outputId":"1a22b625-c6f1-43fe-c712-73556deb0854"},"execution_count":38,"outputs":[{"output_type":"display_data","data":{"text/plain":["plot without 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7l47JyXqr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\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J+zMxD83Rf43ed/zWL9r71mXnZGxwY6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CqjoCRwF0e/dnWeYAaHVvLUq+pKHv407P\nztKU6wF0eLazDJ+dMA+AJ9ycbp6xfPRLcaNztO2iNyuqZoY2pmQ8+q7H94bHq4aixTGHP+GO\nMSl3iSf/bpFlz5GGG1lGP774NY49uwrLCN8SNuRnjx4RZh1hhHNm0Kp7o2LhtRM++8du5twZ\nLhRLKX3qqacSExMNBkNubu6zzz5L6UlfV+vXr1+0aNHXvMNvI8LBfcYnH+PrayKLllGNRji8\nn4QGolHi7nL9qy8O+r3Y3m6uoY5rqOPqqoWD+3TvvcU11gFQEwcaVinZeUpWDijEA3v0r74k\nHNw39ECMx021OgCgEA/uB6UkGgbAdrXHfYTkeO8TghPNkAFAqwUBicWGrkakqJJ6wvUiF5RA\nUcAQKopUM9DSTbElAwAhjMcdVzv22bguQ69QPFPNyB7vGDUxNL3EnT/ON7k0OC7GxLZn7vcI\nPlAgFuvl2rdm7taqGgBXJD51Q87biZaJKqGEkB5DHwCn1t1l6PWI3i5Dr6zXALAKefHjtpJj\nAJOqOVWhK2LRec0KBVWTU2LjJ6sJCQBYlb3Us2p592WpwaS4tTSld/y06gzBc7yhsapIf3gS\ngHKonJKB00LA6ISkuEyZlk+w6UcP/rPqSwAQkLDk7Pbt0/BWQpgU42SLNt+iyY0p4VN2pVI5\nKnsH70Zj/QBEPgGAnk8iICHJPnR+OGbHaW68MzIl814Q4o10LCr6w6LiZ4dmzsXjyKmGyQPi\nZqqkqFKaaToAV6h2bv7jVm0RgO0tP6NUVanS0b8zwzw93Tytw7MzvkKHZ0eaaXo8Zj0x486r\nxn0Qt+riWLQFsuJ7IkVXNargdxmpce3256Vp17d1Pefsb5MGriVVDR9tvElVI+OLX+e5M/dd\nG+HbyYhVN8II/woMZ9i98MILDzzwQGpq6i233JKVlXX33XdfffXVsZNthX9xqN4QnbOASDHh\n8z1nnMDV14h7d4h7d4gH9nDNjYo1UR49FgDjduneelX/t5e0b/yVbW9VDQYqCGAA/oQCI1Fl\noihUp41LinFN9XxtFZFicnYO290ZN+NUs0WaOAVAdPqsuAqZNHYCbElsfc0QZ1Z8rxQA29s7\nOKDGvbAqZXu6ThyUqgAoL5BYjGo0ANi+XgDmLTvzj8kAsg67ATBOOwZVLAl5bfR7EqeoRC3u\nzytwZwFIZLNtQmGIBDqM3ZzCmqNGABxlAOhj+uUtC9hwBEA8aAjQIBec0z1Fw5xwDGtYMwEJ\nx5xiSBUVYdaH/ry39+jsfgAmyWApWWyefh2XkBmfbGaSrXY13soYgErUCBsFoBIlyA2YZSKf\nwDEam64UgC/aPnggT7h5X9uvAej4JBUyAJac+P5zh+rsgaOnv7Od3l2Dt7t8ewCkGMYB4Fl9\noq601384KvsGJ3T07wCQapp6+jqD+MJte1r/h0Idm3pTr//zVvcmq66YY05ooIUlF6W0zbPl\n7aPLBv+tr7kZQCjm4BndgsLfcIzWGaj6rOG+Pv+hqOzLsc5LN83s9O4EEJE9jmBVbsLAD1BZ\njRzo+P3rhxf+aW/x0zvTnt6ZWmd/BwClqp5hbkwwz9RrCci1tnwCrPX6buvoro1IClUrm+7y\nBQ6lJ68ShZFms986KFAbkV5w95996ggjjDAspwRhz0tMtru7+/rrr09JSTGZTPPmzdu/f/9X\nX/N0hgvFPvfccwsWLNi4cWNcSezVV1+98847b7755r/97W9fpHD/L0hswhShupJra1E62uTj\nJQhxonMvknPyho4wbqd2wwcAqFYnZ2QyKuWqKkAIEwgAoDxPeYGRJACha1ZRQaAch2gsVjyK\nr68FIfzRw1TgpTkXUpZle7o1n60noRA1miOLlrEOOwkGAPCVFSAnBf1Uc4JuzcvgBABybp5w\nrFJNTGLcTjrYrSo2JJErGgVArVbS28OEQgDMqnVq7/iscFZMAC8hJnJCWCaSRGKxXDEzwAcF\nhRtvLxVk9nDyMVPUGBEVTZQVDu5zFVqc8tHNOS23VF19Td1l0swLMjr2JUYSZvZMjLLRGM/h\nuIoXBWQ2lqOf08ac0NnkiGgTinrDlWmRJJ+BAnBOyu9i+wDke3MCikOLVBzvNVOd3ZeYtSCp\nvIejnExkAG3mrhJ3vsLQHsOA8yxueE3M+LdN9Xf3+g/X2t+xaPN9kfYa+xqFxgCkmqbq+BSD\nkB6K2QHqCtU4gseanB8WJV1W71gHICjZtXwSAJ7R1TvWSbLPqi/xhluP9LykFWyZ5oGG1RMy\nfrCl8cflzQ+NTr5G4EzuYF1V3+pkw7hM0+xhLiSNaO3xH3A11pWkrDSI6Qc7n+707ZZPq0vI\nNM8alXzdKYMiZwDAs/qSpCuq+16v7nvdGawxa3PN2uxMy+ztzf/PE252BqspVXOtA59N62tu\nbXFvGp9+25zEn2t5G0D2tj3R5NowdFlCmP9JS3s0lX7iD6x2ewMBNYVGOvyVjFDa0v3bRPOF\neu25amaM8HXTKsW2B4Ob/cGu2Eh18wgjfC189YDs5ZdfrtFoPvnkE6PR+Mgjjyxfvry1tfWM\nqW5fheEMu8bGxqeeempQH/b73/++yWS68sori4qKHn300fO7j+8whEQuvkS3+gXhwB4lNf30\nx9nOdqG6kricoCpRVIAq6ZmRi5YQWdZ8/F58BSUplbX3kJg86LEjTofmwG6iKCTgQx8HVZWz\nsrmOdjXByh/ax3V2IBwGISQmCbvKCVUH/WfxmCwFOR6BBOP1gKp/Kf1bt773Pyr+baibVmJj\nT015gQu/+CNhdXxEs+VTAJ5wyx9nvFDsyb+6dnmJIzuTT/QzXi2TwEOlNAqwskA4iZa488qc\nRQyIVbb65kzrkN8Kqa5Ga3uMSGEuwsmcwBu7WXtFUo0xpi84fGBudAYlFBTr87YuPmzR7Kxo\nmr61CJMJoFWMUtkYEj7hG3NGG3KY8W409Rjs36tbrhL1A+l/JINEgBJ3/vPOJwsTlkUVH4AJ\n9rJLmue9Nvq5t2Z0HD8JpCL5WLeh16PxibIQ4sI8q4sbXlZd8bSsH+/v+M2RnhdAVZ41mjSZ\n/aGGuOFFCLkg/xdbGh6IKt7y5p8kG8bPK3iCAdfu2RaJ9R/p+XOxbSWlKgi5IP8Xhzufq+5b\no0Kx6cdMzribZQY6Ttv0ZRcV/rayd/X+jt8qalTHJ5fYVpal3kiGVfIVGOPC4qeP9ayu7v1b\nJOYFFEegqsC6rMm9Pj4hHkeWVcmm/8KEzgzz7GN9a/RCqj1QYTOUxjej4RM6PDtcoRqjJjMu\nXxaI9rS4N+UnLplf8KsTF4NyWgPF+MYIucxkvMxkXOPXOrz8R9a/2mORO3w3b667PZzz9lJT\n8hmVQ0f4BqAUx6LR8kBwRzDklBUAGkIWGw2XmQx3d/We9ekjjDDCF/F11Ey43e7s7OzHH398\n9OjRAJ544ok1a9ZUV1dPnTpcMOdLcJYGxYHASZrWl19++e9///t77703Ly/vpptuOr9b+e6i\n2JKlKTOFfTv5IweHjrO93Yy9l6+rpgajmprO9HbFY6Oq0QiAbWlifB4wLKhKQgE1KZmyHAkG\nwHFEljVbPyUcL6dlcN2dTL+LEsJ4vACYgJ/p76cMoRotkWOQYkSVT87UH+xZfLyvSkoq47QX\nKOO70NNsbC6LFDJuJ9Xpuc72FlOXQhSFhHvDR7Kz87j2FiJJlGEaCkMACoPFAPanVGzK3bEi\n8sNEL0lrgRhhAdQb6kvdRZ/m7jiaVHPnkRuF5XeRHjsbisUE2Sf4OJVNjaTk6ue08DWBqGt9\n/pYpfeMKIqMAtJg683xZKxuW+kR/rdVZZr5sQ8Gad9kNgiKmrd/bu0AHINOflhXKSveKJskw\n1nDl5ty9AGSiOLX9AO46ciNDyeym0ly/4smJuSxY3nwhgGLD4gJHx1HDIbvWSYGwEG7nurP9\n6ZP7xr5TvH58+p2Dhlde4sUmTVZl72pXqEZWQ5GYpyTpqkHDy6zJSzKUdfn2Xjlm3eApXTrq\nxR1NP3cF6z6P/MGsyaFUMQjpF+T/9xddEon60qE20ynMzz/x0ILCJ+vsaw93PwfAJGbOzP1J\nfPxQ1x/rHe/KQ6piOUaTpB/b5z8clHr1Qmp80BNubnC+PzrlaoOQASBBVyxy5gzzrHrHOmeg\nekvdwzOzf55hmtHp3ekO1Q3GYVXEABjFzMHF+/yHu7y7Aaj0hNP0FLSE8ITfOWrKzmDova5f\n5trvaev8f9fqfz4iU/YNM2jPbQ+GXLICQMsM2HOXmI16hgFwTYI5PpllWYPB4PV6h1txhBFG\nODe+itPOarWuXbt28G5XVxfLsllZWcM85csxnGE3a9as559/ftWqVTbbiRTpe+65p7m5+bbb\nbguHw4POvH9RQkHGN/BxGRsznqup4muPKSknEo+4+pr4DRLwM7EYGAKGKAmJXEuTNG02EwkB\nUC1mxu0moRBikpqSGp2xRLNnB+nrIZSCqkw4HK+KBSGM3wsAsRgEMTZ+EldbhZCk2mwkECKR\nkwJ2BJDmXSRsG7j44oUdheHS7aZPGk0tZY5CyjDgOMbjabK0JUTMEie32zfmd0wHAFC5dGyT\n5VUEUWTPBDC9d+IU5WLG5SCqAiC+n1FkJuC8rGnhpb0rSCSgfvCpGvLcSa+NsbJGEUEIVAqO\nDa74jw1dD9TSXcWhMhKNAshlJlCLSvr7jbCVepM4Jtvr27Kyfqlk1nExKUs37QfuZ5JddQBC\nqRZ7YVrqQc2NVakqUTiVHecYfTD1aJO5LTGcUOYs9pfmXmCbvuJIKxACaL5UFsme1tHdaNc6\nSz3F81qm8ipvjBmiiYbrxt1PT+67OrzhNSfvsVNGRNa8sPj/4re3NT90jhfIV2Fc2i2dnh3t\n/ZuHDk5Iv3Nz4/2bG/+jNPl6g5gejyMzjDgh/QfxCQQk1Ti5rX8zQPVi6v62pxloM8yz97f/\nNqp4ZuYMWI1GIdOsya2zv5NummbSZHf79lf2vDIu7ZaK7hdq7W8VJl5i1GSeuqHjsITMM+jn\nlVz7CVuJnj/7xSlbgkvLg8FMgV/y98uUjXDuqJRWR6XyQLA8EHIrCgAzy15tMV9s0i8yGLQj\nnYVHGOE88Q20OHG73bfddtuPf/zj1NTU8774cJbZL3/5y5kzZ5aUlDz77LPXXnvt4Pjvfvc7\njUbzwx/+MCnp7FV+/8RoNq0/fZDt7QYgFxRJE091rupfe1k1JcBkIS4nkWKKNYkHSCAIIHzF\nNfR4y0CVFxhATs3gejrlwmKutZlx9BFVpaIIhiUxCVJU+HwvKFV1euLzKVk5XFPDKccStm+V\ni0q4hjoA8ULatC7GZDE0W9opyxJVpYEADMYmS1tmIE0S1fpM51SXwkdUQeWlun2d2j0cJ8Yr\nCShBJ1O/a/SObm13jIsZJH2RO29e53Qt0YGqJBh4bfR7HtE30VG2L7UiykYf2v9vYNiOJNdO\n2/autj9Slk9XCxANxYPFTL+rR+94Z+LHYS6qMDK1Q2MSQ1xEUDREUXPe3x+G78OC3a2mTp8m\nCJliPDFHTXccveat0R81m9sBbMzdsSV7992HbzJVtwat/nUZezoKO2NsTA9LFn9B0GaAggtb\nZ5hjltdGv+cUnVrR5jz6awpVL6ScotkaVbzHel8dqs06OumatOMlDnFJWUewUlbCOj45O2F+\nacr1HKM5LxfPWeEY7eSse3c0/2zooFVXvLDw/471rT7a+1JMCWq4hEzzBWUpN8S1YuOkm6a1\n9W8xa3IWlvzmk9p79rb97/j02yOym2WErISBLEBCmEvLVm9rfPizhvtZRsg0z1ox5k0GXId3\nx67Wx1UqT868+6w7XJT/qDfw+dzAU7eWXPBWKO2TwIBM2RStbpFJd4FO99VlytZ6fc85T9QB\niAyxsewojbjIYJii+4beiEEe7LbXRyPv52V/w8cdtOe2BYL9igrAwrJXW8yXmQ0L9HphxJ4b\nYYTzylmtuq+eaVdbW3vppZcuWrToV7/6Qv/CV2E4w27KlCnl5eUPP/xwMHhq8s0TTzwxd+7c\n++67z+FwfB3b+k4gTZutpGcMHREO7mM7TuoLShSFr6li2lqYoB+qynj74Y1/UVElI1MuHMU1\n1gLQlG9SbMlqok1NzwQAQuKVsHJ2HtvVAQAMS00m4vVCpWAIVBpvYkwkie3tiS84kF8HAJSo\nimJJYHR6JhQkskwJASGF3txDyVW9Fo959i1EikZ2vt2v8V7QPdWXlVBP327RNRVJuSpV7XqH\nxMZMbFq/xmOUDO2mjjW5H6QGkpe1LDDEdD06R3n23vYE+21VV7MxqEkpPMSonjsqtE7vnBjl\nIpE0qzNw7NX8d60Ry6K2C2jp1Aqyac2o96+uvaTIk1s5TvpA+yZL+Jld47N9Gevzt/aL3hen\nrLtn/3WgcE0uWiP/JMIEL2yfJbDG+lFqe2iXR/QcSa5d1jx/w5SaJuXAcvmu9GqvTwyuK97Y\naexNlGyL+y62eNljY+Uj/vUsIwJ4fuIbt45eG2z4OBALRlQpO3GBgU8VWNMpmq27Wx53h+uH\narNub/nphYW/TdKPOWMrYGeoekHBk+f9QipJXlmSvPL08QzTzNM/PhJ0hac7FIeSk7AwJ2Eh\nAEEQLh79uw3V9xzpfnF27s+mZN07dFqibvTKce+d8twbJ+0YvH1J6epTHr1izFtD77IMf82E\nT+O3LwY8ivKB1/+iq39vKLQ3FLJx7EKD4RKzMfUrO/AuNRmLRAFAhKrtkrw7GNrsD87Sa3+a\nnCSes1nTJ8s3tHW9npP5pRUX5hp0JaJwvlY7KzFKPw+H9wYjO4Mhr6IAsHID9tyFBj0/Ur42\nwgjfTTZv3nzNNdc88sgj99xzz9d0iLPEUmfNmlVeXn7Gh5YuXbpo0SLPadr2/zqoaely0aih\nI3JmtvG530FRSDAAkwWAWP4Z29URKymNTZ4mbt4InVaxWLnOAeMvVlIaN+wYp4NxOgCAEFWj\nBQUIASFUe7zbhaoQu52AKIk2JuAjkkRAEQkDIMEA1epJeMD4jo2bKCQmYetGtt8tTZyi2VVO\nGRZUpUZTUX/eoeSq2sniNLOFAvVjVAB5nqzDYg3NQEAIibIAkGZTB4Blaf+rO7qJgm7O3s1D\nuNZxm87lU9Izs7s7CSEbc7ZXjo1MOCQyzj5kakNK45zuBWMcBc9OWF0ba9ZbdazKXF97qSlq\nioybm5d6xYu18w+mVhZ5cndpPmYYdqLl+zk1rixfxuKWue+XlvtUZ6OltcBf6M0wBTp9AIwx\n8+ievHrDetFm4hgdhWqWTBpqBJDTBIuS9kb+++36Lq2suarnRnO/ykZiPFtKjeZDnlcH3w6v\n0gNggnv8vM65gsPbcusyQsihrj92eLblJCzFZAcGAAAgAElEQVRUVMkerMhLWFJkWwHg9cML\nr5nwSXXfmnjF9xe1Au7x7RuaIfctxyhmXljw1JbG+3e3Ps6z+vHpt319x7Kw7CqrZZXVciQc\nWd3vfcfje8PjfdPjnajVLjfrZ+v1XzpvY6JOM1d/Qtzibpv1BVf/Wq/vCYfz0ZRzDRocjUS+\n7PEHuNR0QiPuq6/2RUiUHgyHt/vDu0LBkEoBWNkRe26EEb4hzjEI+6Wddjt37vze97732muv\nLV269Es8/RwZrpzt1ltvDYdPbc06FI7jBtPvwuHwbbd9jV8b3w20unhhrHC0AgAJhdiuDjkr\nR5o2S0lNp1YrCQaIdKLlLDUa1MQkEBKdv1AaPRaAmpzChEMAJfEWJEM+xwkICFiXI3rBhQCU\npFQ5YyAqRDk2vOTSeO8P4ehhbN0IgO3sYOJmN0NAQXzeXG8mr3Ktsf0AmH5Xm7c8OZRolAwT\nHWMNMX2fLt4WhDZZ2nRsQqo4xqqmSazUo7dnGWcJQgKAuvHCjqzPi/vzAOynHwAABQkFAZTa\n83Qx7YMH7rzt6DUdhq6kcGKDpQ3Au113eWKd9zc8eG3dZVFOcjFdRYZFGsakEEUmCmG5FHYU\ngFZLD0BZyrDgAQSSRIWoJcGyoOoKK66kkFUharw3Ch+MhpLNIZuGEppJS8QYQ6MRmSjljt+0\nBLfHT8iihAe27P6RpAYB1JpqtmbtcZWl42TNVobhNZy1y7u707NzUJt1TOoqm65smFbAjkDl\neb5mvmaMYsaFhU+JnKW86eHKnle+gSOO12qeSk+pLCl4Kj1ljEY8FA7/d6/z+tbOF12e89KJ\ngyP4oS1huk67MxCqjJz4a2qIRn/ea7+itWNJc9uq9q6X3Z7I8VLxh3r6ft3nAnBdW+fS5nYA\n/9Xdd1N7V00kektHd3wEQFUk+lBP34qWjiVNbTe0dT3jdPuUE5p1D3bbL29pP+NqX50opXtC\noV/3ua5q7fxZj2NTIBA3lP+Wk3FsVMGzmakXGw0jVt0II3x7+BJ5eOFw+KabbrrvvvvGjh3b\neZzTI6JfneF+RW/ZsmXGjBl/+MMf5s2bN/wq5eXl995770jhFQCaYEVXB2Pv5VobFVsKAKob\n+KEv5+YLTjvr6AVAj/cBVrU6BpCzclnCoKYyVlzKKSrrtDP+gfa2bHfnwNIDEhNE/GwDCGEd\nfYMHJYrMOh1QTqpnJJGwcOwIgHjVAijlVS7Hl9lMKsjmd/leZ+uUtkl9YwAoBj0oGiytFDTI\nh+w617je0YZdLwNw64IArM0eAJSQdf33T+THGKMGAFQdaFVNRQ2h0MV0lba6w2m1i9L/m3qf\nl3k4tO74Rl5vv36V5a50L/VzQQpa699Qiw0Y9HXGACDI+piYkr923836le8XbvzUvHbrFD5B\nsmZppwuMFoBK1BZpb/yC9XJuhcoA6vmK+sIKFJ60FICN7l8niMb4OSt25R1N29Jp612A8UM1\nWwnIBXmP721/Ymfro/E49usVC1eOXSewxsFWwG2eLae8v6HYdy/3wChmzS/49damB7Y2/ich\n7JjU738DBzWxzKAD702P9x2v/w2P9w2Pt1gUlpsMCw2Gc4+inpGVFuO+UHh7IDhWIwKoi0r3\ndfVlC/x9NmsCy1ZGpDX9npqI9GezGcC/2xL/0u/Z4g/+Ki3FyjIARIaEVfq0073EYEjkWACH\nw+GHehzFonB/kjWBZRsk6RW3tzISfTYjjTt5p6ev9qWJqvRQJLzdH94ZCoVVFUCmwN9gNF1u\nNk3TjVQZjzDCN83Xqhu2e/fu5ubmRx555JFHHhkcfPrpp+++++w5zX8Xwxl2Bw8evP766+fP\nnz937tybb7558eLFGRknpZR1dXVt3LjxlVde2b59++LFi7dsOfVb8F8WyrHigT3BS75HjSau\npZEm2ki/m+toAQaS4LQfrqM2Wyx7oKExkWNcZzsAEDKg7qV8YdeJeKxQtdoY94CRQcUhieQE\n4HjEYmCYgeZ2DAuoSlIy29dT1J/baGltV49qDJCYWL43G4A5KNrC1lZzR5/eadc7ART15w5t\ncUxVhURCALWFrRe2z46LdJkkAwAQSuQYAWEo8QmBHn2fUFuNNECR4yvMSvxRs/yfBzSfXY4L\nBxdMjqRM6i1JCib7tIE6S/3M7skaRVQZpmd+WYX3dTEizuucUZvaQRjaGijnKZ+vmZ3lT4+L\nSQAQPEEmOQaCYk/BlL6xNdbGCX2jD6fWV9iOWPl8d6xZULjlzRf9teyd/8/eecfHUV19/9wp\nO7O9aKtWvVjNktx77zY2BtMhkEKAhAAJSXhIfyAF8jx5gcATCAlJSAw4dGMDtsFNcpebZKv3\numrb+85Oue8fK8tyN4lNKPv9+I/ZO/feuTO73j0695zzA0AThku0yLjbUjEQqLKoJ8MYzVaD\nIn9l4V/d4aYdrQ8mnty7tdcvL/hTQg/jIqWAP0f8s3rJbRN36OQ5C3J/t7vth7vbHqEIeaH5\nxkuPvEKUy9lyOfvfVvNHwdB6j39PKPyM0/OS2zdfpbxWo8o9FbX2SSlkWADoPSWE86LLoyCI\n39ksiaJ6ZXKWJeCPLu8+f2Aay9hoSkeQAJApo0ej4jyieJtee71WnXj5ktvHInjSZlYRIzMg\ngBdc3opQeIn6jPKh553tExGSpGPR6KFQbG8kknArpsvoFUl7LkmSLzSLFy8+S5f1KnExwy4l\nJWXr1q0bNmx4/PHHv/GNbwCAxWIxGo1ardbv97tcrqGhIQDIz89/5ZVXbr/9doJI1ikdQZgw\nlT56iKk5HFuwRFZ1QHZwD2CMlcp4SbmsuQEEnoiGoTdM9nVjhRoAFG/9E4Q4IEKSMVQ4BABY\nJhu7aTsWLFegaIRwO4EkE/Yf8ntBOLXJhQHiPCA4LfkliZgkExu1+b7srVDRJW+XUwyFqQy/\nHQBQNLKubfkzk//arXEMKodJTHTq+nq0A3P7pmg4NcIQYIJklxMAVnQtDNEhEpMAwEhyAHDK\nPaM2qIpXgChqnSKygkhBSlQHAKkeHdIgL+0NMEENr0IAclFZOjzOGDWkhSyvpR30M6HUsBVh\n4Ik4l6J24uAAPfj1+htnD84IZZsdaue7st/XGVvKnUWjqq+yiCQPCkgDXnVsk2bngu5p9pC1\nUxg+df+QHs8zRHWJH0gRiTm+jN0WcIZqNWw6nKnZigBtb3lw9MkhBA1Dr03P+K9LlgL+XPD3\nQ3NHj/Xy3Hk5T1S2P7q95QECkeNM13+aK2FOVTlu5eKv+wKveXwfBoIfBoIJB94ilVL+Cb89\n5AgBQCIKLSLhOo5bqFSyBIqf+t6cLpf/EbzVodC0CwukjobuBUWphYvPUSpUY5YxS6l4weWt\nicXOMuz+ZYKSdCgcqQxFj0YjAgYAyKDp5fqkPZckSZIrySUCmgmC+MpXvnLbbbft27dvx44d\n1dXVTqfT4/FoNJqsrKyJEycuWbJkzpw5JPnlqlwVnzw9Pnn6eU9FV66FlWtBkoieTqq9VcjO\nE3LyyeHB+OTpfHEpAADLyo5VSVo9EfSBhFE4iGUMwhImkJg3TtZUR/o8kkYjqTREKAgAYmra\nyG7sqbrDKBoFioyuug5TNLtjKxHwIwnTzfWnF4FGlGFPh+id+rXTcCpzJKVH7ZAL8vR4DoVH\n3jgFL7eGTb3q/kGlKz2YOmVg/LGp3LDbnRG020O2Tm1vlOHlHH3QdowVGHMkBQBCVAAATppG\navXxBF/gzXHKPVSMT43YBhRDmQE7AGyUnsSA+5QDrxRv/E71XQpeEaEie+1VN7esAoDsQFpl\netXG3G0L+2YesZy07WzzZtcDDVGKa0pprWYbPKQfA44TcemUVRdgQpaIhcI0YOQkHSQmEzF/\nBC8CgJfvBoAWeV3LlLrEnf+j9O3EwIHgEYpQAIBVM3Vn68OeSLNdO6Pbu+f0Y0MAAL3ePbOz\nfqFn8waDx96pWyuKXKLWSapmVqdna6IUcGXHj4Ox/oV5/1Pd/+Jw8ATGok6eO9H+LYPisyix\nlXDaAYBRWTQ/94mK9h9/3PIdipDnpKz49BeTz8h+bjE+ak7ZNsaB9ye3b7ZCsVSjmCSXX3oK\nAAAISCIAaAgSANyigDHsCoV3hc6OVhmOXzCqDwHoT22kukQRAM5yvxkpCgBcwr8bFxgUpUOR\nM+y5ApZZrlYtUyunKy73fpMkSZLkMrmsTDWSJOfPn3/JSLskpyEIbtlqxWt/k1XtE7NyAUBK\nOVXkGSEA4MsnibZUFAzS7c1UeysIAgIgO9oRgYAgiFAISRI/rhi4GABgWha95jr5lk2I5wCD\naLPHp82QNDq6sS5RIRkTCJ2V5nJmnDWSpNFYvXxv9gH7MUoiF/TOHNsnx5dxxHYiTvBTB8tM\ncevi0CSBP4gBL+mZ/Urxxn8WbJzRP7FN15XlT2tKb0sPpt7eeB0AzOif9GHOLkBAIIoW0IK+\nGRwRm9877Y2C998at2Vu/9Qid16c4Hs0fRpODQBZ/rQGYwsC5JR7JQAEiBWYDl3vqs5Fs/sn\nH0g9zpFxWpCdMNcreLmgUWlpYyxWn+vPIPDIHW3NrpjdN2X60AQJx7u0DlZgqqzV2f70Vm0H\nnBKfzeVLsvv1OzP3YQCFpMrxpAbYcA90+GPdCekwXgg6w7X9gcMAp4IX8SnLGYEn0uyLdiAA\nAqhc46q4GGgafrNp+C25zJgoBUwiOi4G9nf9stB8y5S074a4wYPdv97b+djq4vUkoq/QZ+jf\n5R9V885tNCrHz8355Z6On21puvuaor9nG5Z++guDMTJlDp5/xxd42evfHgptD4UyZfRStXKV\nWn1JmbJE2sQ49vTTnqWU36bTju1DkIRVrbnQDAih0Up7pz4CZ5DYNDlDd/mT4BfFw5FoZSh6\nNBJJ2IYFLHOtRrVWoy64sBMxSZIkSf5NvtzSEVcT0WbnJ0yhq49IgQAAyGqOxuYsxMqRCC1m\n7y4hPTM+ZwEKBADj2OIVQBDsjq2S1oD8AWBoTJJ0cwM56EAch/i4fMtGhBEAAQTElow4WlAo\nOHIgwei27HlWYk0FliUGHIksinxf1n77UZ4QcnwZGDA3eVpXx+txFZXrz9hvPwoAtcam/bmN\nMeG5O+i11rg5LWi7q2FdRVrVh9m7ECA/E5zeP3G2d55D25vptXZqexEgwFA1D+OWY+XOImVc\nmeVPW9u2rDKt6qPMPRiBJWy8rem6hMlV7hkvF9mgLFSZfihOxGlRNs6XNWG4hBVlfXL3HMeU\nCcMlFekH9tqPcGRchlVanMYKTJY/ffRHd1Dh3Jy3/av1N97SfO2Q0rkr40CVreaQ7TjCBACU\naW6pC7wlAI8wYggVJ4V1YG3VdwmEQCIaA5qf8yRCRLpu/jHHHwQpNjbrOAHG8FHzd1haOzvz\nFy2udzq823gxTCE5L4VLLV8dLQUcF4PjTA9k6OYDAEvpc1JW1Q687I92GRT5V+gTdCUZddoB\ngEU1cW7243s7f7Gl8RvXlmxI1829+Nirip2mHzKlfMdo2BeOrPf4twRDf3H7/uHxz1IortFe\ncAMUY3jLF0AIFqhUAGAiSQKhOMZFZxpMJEmqWYbjzh/SMBYTRSGA4TOdc8PCedx4l8QpiIcj\nkYPh2JFoVMQYTtlz12s1+f9qQGGSJEmSXD5Jw+4qws1dRLU1UT2dYmYO2d2h2PiGZLJgigQA\nyZpK9XQR2z4gfJ7YgqViahr74XuYoiWFivJ4onOW0Q11KBQm/D6sVGGKii1eKf/4Q8ASYAAu\nBgxLDvbTzQ2JC4lpaYBIsrfrvMsgfN7Ijbcr3nw18TItaPvZoQfhlEOCOXp4b/lhD+u713PX\nzw49+EHOThJTky1rZJjtTdlOYGQPWa0h0zVdi/9Q/vKUwbL5fdNZQo0EMVOyPjvp5aAs9Ojh\nb9ES3Wztfyvr4L60o48e/Q4SoVXfWezOm983gxtfytTXAoBfFvz1jP+bHJixpHkWAPy/KS+l\nKacvYO/rd7wsF1gAqDE39CuHsv0ZBBAcGQcAE1MQ8bQrsKLW2Px64fsAQIsUTwo8Ifyl9PWz\n7xOJAJCumDbE1fbipjW+mXbjvXGGItxO6vCeGnOTM1/j8B+QQCQBaFJp18x0+A9cV/YOTSgA\n4LjjhVbXxjXFG2hS+W7tWqtqcoqyaKbyZ4m5w/zwlsav+WOdYy9o00wZPVbQRgCICW6Az4Rh\n98/qi6XiW9VT5mQ9vq/zF5vr71g7/p9p2tmf2sLOy4hMmUo5yAtv+vzrvf7KcLgyHNaRJACE\npTNcaQKG51zu+hi3RqPOoCkAYAmilGWqo9ygIFhP6Rx2cPHNofDdMsYMAKfs9gtlJCkJVMwy\n1VEuIEqjzsK94TAATD3f7vC5szkFYW84UhmKNsRiieUm7LkbdZocWdKeS5Lk80d/4FDj8Jve\nSCsGSSkzZ+oWF5pvHtUc/yyTNOyuIphhuEUr2E1voUgwunwN3dFKDA6QQT8AEIP9gBDhccWn\nzhDt6YjjSI9LNJmo3m6+oFiy2KChDokCwEh4nGQ0Y1aOwiEAoHq6hPxC+thhIIiEl47PyiVi\nsVHDbiS+DgAQYLlSMhjouhoU5zBBIOl0XS4ECANGgK5rXRGmw6ooAwCrOxYDAHQGAAIA5SFZ\nxC8LKQW520bpY9o2feeKrnkCySEMJBDfrL31hLmhKaWj1Flg8apvia35MHeXpFASwcDq9sUY\nsERIFaHnl8M8ANiWXQEAAeSiRZonhIeP3S0T6Th7LCpXdWh7zJEUBS/3y4Ml7flykW00tBGI\ndESPWXTFqfJJHb6PpgyWHraesKgm9EWPzuqf1Grq99Du2cYRNQVHz/vtipbE8QTdHTuHHltf\n/I7OX+cVHRHRI46PAwDyIwAAPPIEMg1LenyVDt/+LMNSjHGvr8KimqigTYFY7+XUOkGIYMjT\nG38EIgFAwhfMZf6PM9ZpBwA2zdSZWT/d3/mrzXW3X1f6Rqpmxn9wbaNYaeohU8oDxpS94fCb\nvuC7fj8APD3sessXKJPLsih6SBQrw5FhXpivVH7HaBgdeF+K/mHH4Pf7h27XaVJpqicubPD6\nGZL4IUWBwANAoqbJ2z5/mZydIj+PHNm9Kbof9A/9aGDoFp1GQxLNXHy9x18mZ2Yrz2PYjc6W\nKZNFJPFAOJaw5wiAMjm7TK28WafNkn1WNuWTJEnySekPVO3t+LlRVToj41GKYPuDh2oH/86J\nvkn2K1ya5GqQNOyuLvy4IiqvgGprJvxebsYcAKBra2Q1R4EkE3mssprjQm5BQjeC9HmxUsVP\nOlNkNhpGGBQbX0c8DwCYouimBiISIT2uRCAdZlkpI4toaRrpjxBSqfiyCdTRKhTnUTRMDEQJ\nRx8ghE6lUIwGkyGMAIEpqjdF9U6V3xTSAoAAUogJH85pzXGn5jotCCMAyO1i7kW3NRk6ABAl\njmxbKnn5LMfkxLFMlOX7sr57/OsIBwAgQsfUcaVPFpg4XJLoQGCi1FVY5M4FAFqieJJ/teg9\nW8Q0v2dGKhUHgIxg6gH7MS2vpkVaF9Os6ltu8+kJQF5lpDz9Xk2/47i1qS96FABsIUufKQjI\n0x6ucHLNghTTEEpaonhCCAoDafLJtwTv38S+1MMdS1yaFukJzuKOHM4daRoMHEvXzwcAm3oq\nTSqr+1885nhelKISFjVsNieMlA/8XNc6uZC77izbLk07Z1bWTw90/WZz/R3Xj3/bop74aS3w\nEhAIEg68fIb+zZALA/TE4z3xOACwBCpm2QdSDLPOtLfGMbJn06zrPb6/enxhCetJYq5KcVeK\nXkUSnAAAsFyt3B+OfOAPVoQif063nXvREpZ9OtW63ut72unmJGymqRu0mq/otedVvJ2oYK0B\ncpM/mPgfRQBMVcjXatVrNGobnfxSTZLk80GA693adH5hBU4IkATD8Z7awZcTLSTBtDo3DwWr\nz9s/JnjP2/4fIfkddNWJLV2l7O2WHa8S7RlYoQCKAgBJqRIKx8uq9gEfZ3ds4WbMBQAQRG7B\nPEzRAIC4Ec0irE+JTZlBetyyo1UAgAQBvG7a6xlNfY1PnYnJ0++jpNESegN17DCKxwEAUzQS\neKxQigoF6R5xOJ0RWIYAMAKW0UdHzD6JEHScel7K95mOgwiPxCdhVk5HpFJnAQB0axyUSNvD\nZhfrjdKx9KCtRz2QEbR65F5DVJ/or4orwnSMxjIF6BN7Vus6V5PC6SI+Cl4ekAWL7Xcd5nfP\ncUwFAKtqiozYHdYzOjfc1LIalc2s0/TX9b98S/v1bEuEiKvNkDWEOkUcF1l6UfvUVwo7Y9zw\ngtj1RgffaXFXoR0AoBMMACBjUkJ0BADsqCBft0Zb07DffCgQ4wCAIEaelTNcK4gRDDA944e9\nvj2DwWp3uGF3+yOL8p76vNc6SVhvMpkMIXTxCLN03bxpGbHDvb/bWHfTutJ3zKryT2uNl8X3\nTCnfM6UAwKhMWUSSqiNRwMCDdJZMWZ5M9kureezwsdn6WpJ81m4dfXlWzwTFLPNbm+VCi/ld\nqnmAF971BypD0fpYDABIhCbL2bVa9Vqt2kIlv0uTJPk8ISNVgsRFeNeFOiBERseYaxiLGKQI\n74QLJFTRpDKhQv4f59KLwBhHIhGl8jyBzP39/Q0NDUuWfGJhjS8Rokj19Qh54+j6k7Kjh7h5\nI0V6+fLJQkaW7PB+QEC4nEz1YQDAClVCkQwACN/I50lKMUkWm2Sx0XU16JRCZWILdaSD4gw3\nEqZIWLGGi8WY19ejWBTrdMjtAi4WX7ySPnGM6ukEACEjixoaBIEHRIDAA2CI8xRJgygAwNbs\nyrXtS/3Vb1o4s4QwAeSp7UsEgDECWiKNRAZATJM9x9hYDwCGmJYn8evj3r//xF0RKsaKzEHb\nsdn9U/wlmTShh9oaAHAx7urU2hXd8xMziTI6L5TvJI90mJoShp2gYs1MIScFZaJuUDWsTjdq\nsNIRGGyy9E3qyhp7j/4C+xHPh4xA39mwGunNnklZ+QTq6qsbkA/4h4+p1NmhTD0MAC3Rt9et\nGFxcZgljZZdsQ9F7ACCdMi1rHC+RBCtIUQLRrkhDlmGJjs067nhhIHDIpCwdClaH44NK2Ygp\n4It2tLo2JWqdXJHPxWeHbMMyAOlw71Pv1d2yrnTjZ9OcLZezT8nZxyymjf7AP7z+49Ho8WjU\nQHrnqxTXaNRXe9OzK87vCYcrQ5HuOA8AJELTFPK1WvX1Ws2/VqA4SZIk/3H4vIPCJy8XzF/0\nrCS/YBr+p8nFDDuM8dNPP/2b3/zG6/VmZmY+8sgj999/PxqzMbFly5Z77rnn06mk/HmFJOlD\n+wivG5ssVHeH2Js7eobqaAWMxfQssqeLcPQBQQAXSyRGJDJhE91E24glITFy8jKkx5EgQt0J\nkucTVhrhcgLGSMLs5rcBjZbCQ0JGJtXaPBL/TRCRW+9i9u5OROnZQmYACJOxkCxMYgJJJCvK\ngCQxRSBBRBhSQ1aAGADIGkeK56l4RYeuh8Y0AEiEBCJkBO0A4HDuVpKmTFADgJtxuxSekXsh\nEK9mi7z57+g2CfRIKmLUrDEzxZzkB9BFJkxQA1DAAkAf0TYJsgZnF4rUbogDAHBy1K/olfH0\n+rx/Lh333MiTNqRBdKBP77EDkIwGAEQSH1goqYju+oldbcGdesniJYaGgsdSFOMoQu6NtqTp\n5vij3bUD/4gLwRzDcjmdctzxwmCwZkLqfTvbHt7Z9v1i8+0qJjUQ62kc3kAQTKLWyRePbMMK\nQYod73v+3dp1N5S9l/KZrMYHAOpTMmVNMe4tf/AVr2+jP7jRH7xSMmVnkbDndgcjCX0LGULz\nVcplauU6rcaYtOeSJPmc88SwKy5dYetlqkL+Wdj1uJhh99JLL/3whz8sKiq67rrrWltbH3jg\ngYqKig0bNtB0Mij4E8AtXy1/ZwPyuAAh5kClYEsFALruBOF1SxpNbMYcVsJkXzdIEsKY3fmR\nUFI2atVJZquQkTVybDKR/oQbD51Tcus0hN8H+yrOeIcQOjXm9M+emJpGtTaPzKzR4DGbVtOG\nygEgK5jmZwJsXEVJFADwfFAmIQDoVjvSQqkYSZREVqZVsQJT7ixkRdbHBEavBwDpQRsAGMKa\nemNtJswCAGPEMLt/2sgiRcy6g2nIkKvLnNU/KdFoOtyeNiNLxMckhFM04wEAuQYgYSkCmI61\n08VRYDAAcFIIY8xRcY6Kv9F319h7jXNuAIiJAQCQE7oq70sEUBamcHnXgjbrkFcz1OHZqmJs\nNs1UAFDQJoM8/+TAyyqZ3agskbAAAFHBZVCMW5L3bP3Q+pODf+XFMEvp07RzSyx3jNY6+eKR\nb7xOksTq/j++V3fTjWWbtWzWf3pFF6OQZX7OMv9lThkrU/Znt3eBSvXvyJQlSNhzu0KRvjgP\nAAxCy9SqazWqlZpLV9dLkiTJ5wgrRX3bqL8iU20PhveFI1dkqn+fixl2f/zjHxcuXPjxxx9T\nFAUAr7zyyn333fe1r33t1VdfRecLKE5yXkSbPXLXvfSRA3RjPYpGqO4uAEBCnJ8whS8sBoqW\n1GoSAFgW8zwikOzgXnSqnlZsyYrRUsOiyUK3tcDZtYeBGugTJREkcUQcliCAYWNr1rFvbwBJ\nGvWnIjjjTZPGFG7FMhYARIt1bMEUAhP6mA4ABEIkMITJiExSA4CS0L9c+sbtjddREjnfMV0C\nqU85mBFKLXcWThoqAQACSADgxxXTLQ0mZck85UKAWgBI4XQcNQwAmCR8RWna1gGSg2s7lzlT\n4hAFACAkseBEHIklBMQoxMiH/f7aXTAOsqEcACI2PRWLUxRxa/qGD9q+BXJID9rm9k4P0xHI\nL+X0SjIaN9T3ygVZ2CxJBA8A2ao5ZZpbErcjYyrY8ABoYF7OE1b1JF8scae42HJHseWOkecA\npwvS6hV5c7J/eaH39NxTmfolmfrPd0xCgfkGDFJN/5/eOXndjeWbNUzGf3pFl2BUpqyNi//z\nHJmyhSql+pMo4iTsuZ3BsIMXAIA9ZeL0Q4sAACAASURBVM+t0qjVSXsuSZIvIkqSmH1KV/Df\npIHj4Gzhm/8YFzPs2trannrqKepUUPCdd96p0WjWrVuXn5//2GOPfRqr+6IgabTc4pXx+UsV\nL/+R8PsA4/iEqUJm9tg+8cnT6UP7UDgUvfYGoGnFG68AxmOzIkbcePiU/YEAAMRUO9VYT9ed\nwEqVmGIkncPRpdfIs3Oo40dAkgAhOJ0Jm5BuGDHusOxUKVeSJDxuKRauS+tL8yrM7af+5sA4\ncQ1SInmSfz9/z12114IkpfjlZvVEWdp46GzDGAggBCohTYsAEBAEhcmnpv75QdW7dEsDEeOi\nrYdoUG7LrlzROV8m0omZt/D/twoW6pDGMz5T1t0GAF1Wb9agPlSczg77ZcEYAOhPdn5kr0YI\n6XTFAO6IzcALLMJY2ecigEAAAhJzAuk70vfnpE9RAoAKNOYUfX2PIxBhUlQAEBP9o08Pa3X+\nMS+VtAkBisSHx74FUX4YzlSS/bJRaL5JkCJ1g6+8c3LdTWWbVUzqf3pFl0VeQqbMlLItFHrD\nG9gZCj/j9Lzg8s5UKm+2QTkjW9Lefe6oHbmZGEM9x1WGwnvDEZcgwhh77hqtWpUUv06SJMnn\nkEskT4RCobEv165d+/vf//6hhx7Kzs7+6le/ejUX9gUEU1Rs+RrFm69IeoOQnnnWWUmri9z+\n9dMvjWbk9SRC7hItiRJ03PzFCYuQqdhO9fVwC5aOGH8Ys1s2jQ4Xikuh30E5eiJ33aNY/9LI\nAsYk82CFEhgWE0TtBKHkUJTc/m7AclzTlQfAJGYDBBgAMA5YGLmbN9pmh0u/rnjjHxCPLz2W\nh3SRxIwSIRqiun7loC1kBQDRZpc5em9uWk0b2gAABXwxRUzFK92MN0pF5QILgBFGN7deIxJi\nXKeCjjptWBmiw34lBwChtBR22A8AiIvt0H/Qp+rPUy6RBw0AbgAQZTRw4GndhtIQJVKDSqeP\nDfSoB1qa719R8II33nNcuWkxm0dG4wrKKqe0fdHqqXoRAQkAnE7RKfSOPh+aVKYoigeD1ZwQ\nYKgR52Wvdy8AWDVnlpv5kjHe+lUR841Dr79bu+6Gsk1K2QWzRD9ryIgRB14/L7zt8//d668I\nhSpaQ1kX2Jl93uWpDEU8oggAcmLEnlujVSuS9lySJEk+z1zsK2zWrFkvvviiy3VGMvCDDz74\nve997+67737xxRev8tq+gIgZWXzReMLroZvqL96TmzQNSSK78yOqu5Mc7KfrT9AnqseG3Inp\nGYAxffwIikSIQICp2o9AOu9UkbvuOauF7O5UvPIX4KIoGknrEHdnHCQwnt8ynhEZAOi1Bvev\nktUaWkUkhhQ8HxoiMDEnvJLZW4HifFVWo0PhQK5BAMCk6FAP1RlbPs7cByCJSBKtqR1WrzVs\nJLvaEw4/J+MFABKITq1DxSsAULOuvU/dT0ikzBdMCSsRRh9n7emWd9aYG1rEfWFhGAN+z/WD\nGnN9frx0iu703w+YJABAySsAQM4rSCDWF7/rZ/08wbcEP6pwPekgmuW8HGFAgIr118RE3x7X\n045odU/00Dbiz8boGbEUE+z3Spiv7PhRr69yKFTdOPR63dD6hJLs5b+hX0jKbd8sMt/ijba/\nc/K6s5yanwtSaeohU8rh/OwN2RlrUgy98fPnsW30BzGCW3WaDZlpbUX5r2Xab9Frk1ZdkiRJ\nPu9czGP3m9/8ZubMmQUFBc8///ytt9462v7MM8+wLPvtb3/bZPryblr9y3CLVlCd7XTNMSEj\nC6vUF+ommS3RZdfITh6XHdyLRAErVXxRiVA2cTTCTsgZh0IhuqOVbmnECiVfWCLa7MyenUg6\nj/hBwrYjO9vYfRWnN2cxAMJqZ2wBnslrcHzOwhjn1B6urdGfaHDXCeOEwxHTgp5Z6SEbhSlN\n1XFJq+fmLbbZ5u8e/u2W0N4bm5Zaw6a0oE0b1dQbW14p3nhtx1LdsaospPMzAUZkEhcaR8zc\nurK9zdudEbQTmAAACUmvFW2aMTBRBLy8e66EcJuuO062nVQBeHdm8qtNkGUgM65pnGUxznSf\nURYIA0CKsmhFwQ0AgA5XHGU/7kxxClKkPrgpXT5tenAeKw4lNlwnGm/l4tH2UMW+2DMa2lai\nWweOql71wGikoVFZsjjv6drB9Yd7nxYlTkGbC4w3lFi/glDypx3KUr/JS9E21+Z3a2+8oew9\nOW249JjPGBRCK7Xq2zLT2/2BvOraczu8mZU+Rymnk+HCSZIk+WJxMcNuypQplZWVP/7xj8Ph\ns2MCn3zyyXnz5n3ve99zOp3nHZvkQmC5nFuwhN26manaH1u8AgDiU2bEp5xH0EkyWWKLV15w\nIoT48sl8+eSxbcKd3xw95hYuPas0rZidh/fuPnM1cNxcvz1r72rr/8tX5yirBgBgTeOcVZab\nRIsNaBqlRsjeHgj6h1nn61l/jfARzUBqoXrVlNQXyEImCgAAm/vv54PULc1LPy5v6EGNEkgm\nImch800rOY5qbaK6O21REwAcsB3bk1ZFi7SEJHMoZY5japTk/nfqn4CmbrSvz3j/CMnFn539\ndmzh0vXeV4dirZvyGoz8ydL4vZCRG8owAcCK0A/6W17+a/rzwb6AgjQWIXOZq6hZ37nA+Giq\nfAIAyINOgKHEnRGImKi/rUxz8+i9NimrAGAkzg8AAFKUxQtyf3vBJ/wlBgGanPYggNTm+mBj\n7Q3rSt9l6SuTO/bpk5qocnc6EGHkaKHqygRNJ0mSJMlnikvE2M2aNauysvK8p1auXLl06VKf\nz3cVVvUFhy8ppxtqye5OqrNdyM699ICrBAKEEYVHHFQoFqXamgGAW7hUSDsdAujo3zyUNjy3\nb/rd1TcMyocbZykPuJ+PiJ6Fph8lOpCY5qTAO4UfT7J9f6Fisp93bBn8r/f4J79u/wAzLNXd\nedz/BjCQGbBnBtOUccWHOTudKvfmnB03t1yji2mcpHv0WnEpuN/1bJF6zVTD3fJDBz+wbNzj\nenqN7fckogGgc+j9PZl77ahgQso3ReBrw38ZVLjglE7rWTR6t3b6D87Qf0dGjPx+O5SDJCa0\nESVoz+2e5GwQoMn27woS1+XZvqn+tutL35KRF3Qwfw5A5zlKkiRJkrM4b67VWezIPTtK/jPF\nJ5O/CAaDonjGTh+VFNL5F0AotvQaxcsvMkcPCLY0YJlLD/k3kG/dTLidkZu+gpkzLoQAAYJx\n8QndbocWomzDhyBJotnC7N5OpmdyC5ay27eQg/1eq79T0zcTTVHy8px4htV0z2CstqSKkwtv\ncQuWyo4cuHVwPIJSj1FiixaRfT0pte0P+G8M0H4hso8qWcQZdYs6JltDq/qVA/awTSbKCrw5\nAVmwRd9Za2pWCHLylGUpASY56fqua0wughAbeYVljjD1w+ydvKNeJctmPKH6SNPXh2+yRewI\nOB/lKyWufbXoPQA40vvcmvyXzrprBWVwRKorhf8pUK8kENUTPTRE9pQ6Cxnaz59HKfQ88FKk\n1bXJ4TsQ4HoliWNpg16em21YYdfOGu1T0fEjV6juxrIP/p036HK42hfqcG853Pv0gtz/taon\njTYihKanP4Kx2O3d9V7tLdeXvvUFruSXJEmSJFebxsbGRx99dP/+/RjjCRMmJALervhVLsss\n6+joeOihhyoqKs7dkwWApPLEv4CkN/Az58r27WZqDo8IxV5pRpNhE1Fl5IBDyMo5txsp4FUd\ni5n2YQQEkKSYkU0OD4n2dAAQMrOJgf5pA+XTBsoTkyCElK/8xZjHx4FDIT+7/QO+uMwZqrWE\nUwwuQqzYAYC5OQta4hWyo+1Fxzuitgk4f3yHY0uMiBf6ciWQEAAlkazAzBiYGMizVtMtlETJ\n/BEAkJB0S8s1tM3kyk6hQjF9Q28JV7QtazfV1mT1RGNyvDqyxKkNOafnSyThany31FV4Xfuy\nV4o2nvf2M9XT55t/WO/bdNj7koDjatI6SX5T+QCSZBdUBhyLL9pe2fGzKO80KUsLTOsogg3F\nB/t8e/v8BzL1S2ZkPpJItv3CgxAxI+NHGKQeb8V7dbdcV/omTXz+djCd4z+jWhpJkiT58hCP\nx5csWbJ48eIDBw6QJPmrX/1q1apVPT09avUV3gy5LMPu7rvvrq6uvu6662w2G/lJan4muQjc\ntFlUUz3V1iJk5Y5KxF4pmJdP5yxjjBFCZH+vbM/O0cbRHIIWQ88HJe+Ntt/SvCYfsl6M3x1s\nDQECmAl3n7i1Q9/dauj8Wt1NDtXAP0rewQiK3HkIA5aw7PhhUSMBYCBIcqg/su42LJfzQdmx\n1KNF7lxy0MEXl7bLOLG7XyGwm/K2lzmL5/dN6182Oe1QT33s4x5jFwD8PXA3TAAAIIBIiexK\nZeYV2q4j4oK+sc8SMg/OyKPkU5imJrGrtrZcLDIbtjXfD6lgjpgs4REp923N968oeCGUYUpE\n4yXqW6QrpqYykwBgt+tJZ6y50Ho9km0j3ZeOCuXF8J6On8UF35zsx9O0p5NkJ9q/faj7yW7v\nDp08u8h8y2j7ed1dXxgStp0oco7AwQ8avnpt8askcXV9zEmSJEnyxcPv9z/88MP33XdfwpL7\nyU9+sn79+vb29gkTJlzZC12WYXfkyJGPP/541qxZl+6a5PIhyejyNcoNf5Md2h9bc/3YWsRX\n5WqOvnPaMGDI8trABuWKtbYGX1ynzAzYXarA1LQHAKDkQEzpl97Kfa9d1zNlqAwjnBq2/qjq\nOwIpiEgEgOj1N9Mnq6F3p4gkUqYEUZBvfhuJQpGaaLXKAQDFYgCw6kRpSlBz8hr7LQeMZhcD\nAHlbGjACQiYBwDhf7tLhFRovQoC9TKBX49gmf98h1l+v+hYAqAR5Yq1DufItyndLmLWjq/fL\nApZICiOcp0pZSHC+3X7fDWl/ZJDudCtCklZLOIdRNILlF3M7tbo3R3hnWerdY606ACARPT3j\nv5Qyq0k5/rwDPZHW+qFXnOFaQYwqaHOGfkGx5XaKYAFgZ+vDnmjL9ePfpgj5aP+TA39rGNqw\nJP9Zo7LkImPPpT9wqHH4TW+kFYOklJkzdYsLzTeTxMijqOz4cTDWv6zo6WO9zw/4qzEWdfLc\nifZvGcaIwDYPv9Pifi8ad8ppY65xNXPR+DkCUbOz/3tf53/3eCs2N9x5bcmrJPq3ZLuSJEmS\n5MuGyWT64Q9/mDj2eDzPPvtsYWFhUVHRFb/QZRkTSqUyKyvril87iZRq58sn0zVHqbqTfPnV\ndPZgQLFobPX1zAeJjUsEABgDP2WG7niVQpBnquaUDfbEXJxMpJ2y7pmb1JG77mGpLQic7bqe\ncd7sFZ3zT02FD1urJw+VAoD8jfWRO+8Bx24RiVQ8hll5fPosFAyyNYeubV8CAFRjLdnbLcQZ\nBERep0LF6zFEEWAJMAEIAPJ9WTc3rxIUDIk5jKBT1z1lsFzLaV4req+NrLGADmGkb+jN7BON\nRGC2acqEEwaNePjb/J2nAuAxAjTD9O3xDQp202GEcVyn9JRn9Soazv/AtXrCOUy4XWLaxfSy\n+nx7ESLyU9ace4oi2Amp9553VDDWW9P/oobNmJr2PZbSO8O19UMbXJGGhbm/Q4CyDUudvbWO\nwMFM3aLRId2+3WrGblSWeCLNO1u/d6GxZ12oP1C1t+PnRlXpjIxHKYLtDx6qHfw7J/om2R9I\ndCARHRcDlW3/PT719ompD4a4wYPdv97b+djq4vWJNJQ21wfV/X+0aaZOtj+IQWx3vX/JenUE\nomZn/aKy86c93t1bG+9ZWfgXkkhqRidJkiTJJ0MURaVSyXHc/Pnzd+zYwTBXfgPksgy7O++8\n829/+9vPfvazK375JNy8xVR7s6yuRszMkXS6Sw/4l8CAESDS0XdKiAIDoOiNd6A4B8eqcnwZ\ndPtOwPmkRADAgHK40H1Grq7DFO7tHzBHUl4tftcWstSYGywRU743O2FxiBRiRFlF+v4jtjoT\n7JipXGtg3KZwCgB050i79K8tai638sxr1OOZWSVL+uYq3LF3C7dPdU48bqnL82f2qvsP2U92\nqroITMQobtJQWVrYDgAV8Fr1RDUj0IgT6yejk+GKQZnjgP0YgQmMgECEhteUDuQDwEnnK1Xp\n4uzy+w94/xSWnOkD6Z3qLgB4p+/bJKJvSVt/+jnodADQ795TFzt+EXeXJ9KqYqxVvf9vOHji\n4u4uhEiMxYTIW5v7A4pULMj9XULKwqQqIwm22vHHgUBVqmZGum7+Mccfer0Vo4adO9IY5gbK\nbF8HgGrHixcZe9a7GYz1mVSls7N+wVA6ALBqprjDTZ2eHaOGHQDExWCR9aYsw0KO41hKn5Oy\nqnbgZX+0y6DIB4Cm4TdZ2jA3+1cEogAgVT3jo+azq1ifC0kw87N/U9Hx43b3lm1N964seolA\nydypJEmSJPkEkCRZU1MzODj4hz/8YcGCBYcPH9brr3AxqcuqxfrEE09UVlbOmTPnkUce+e05\nXNkFfdnADMMtWgGSxFTtg381DSUuhY94//rP3q+80D7n/9qm/l/D/DeWNrToO0c7RO/4BiZJ\nsr8XACJ33SOlZ0o6PVYoJJ0+LodcX2a7rsehGiIxGaGjHbqeo9aTLR89DAAYJAQgxgPpIZtA\niApBftxSZ4oaJMASwn462B+rieEAAJhixpXOtQD4be4Xw5oQAAwrXRuMz8flFIkJmUQXh8rq\nFcebmZMA4FA4NMhIYqLKWrO++N2pjvF3NFw3dbAcAHrUfbRAMIJMy6lsIeOQ0nUkq/19/Fxc\njIwLFWHABEYSSBSSm0OGyvRDPCkAK4+TfFVofbZ20ULu5mva5uXJZgLAYstPl5l/DQBurkPE\nHACIOn2vur8C/x0AZmQ8Ojfrcat6cu3g308M/Hn0WRFAA+CY4A3GHBKI83P/J8IP7+18TMQj\nAgYJd5eGSZuT/Us1mw5YanVuBIBArMuqmkwRrCjFE/9smukA4AzVAgBNKu2amQOBI7w0osbb\n7d2NEMoyLOOliCtSd5GxZ1FgvmFR3tMJqy6BiknjxdDozAns2mmjxwraCAAxwQ0AnOALxfvN\nqrJRswwhlKq9LL0NkmDm5fw6RVHU5v5gR+t3MT6/2EmSJEmSJLkQhYWFCxYseOONN5xO52uv\nvXbF57+sP7iffvrpHTt2AMD+/fvPPfujH/3oCi/qSwY/rojKK6Damqm2ZiG/8JMOd3LN7/U/\nGBKG7Oykibo7aEIegeEm77a2QneR+ppl1l8RQAKAZE0l+/tQPI4pihgcEMaNXMhrJPMGMzbn\nbe/TDHzrxFfadF1hOvJR9h65wE48WiIgyRo2Tw5MBww8wdMiZQkbfWyAEWUYSQ3G1mZX3Syw\nSQiXeAokZEq3f/8vncuHdIGSAThpbKIxs8j8Yx23h8DIEFJKRoHHUQBY17pCF0KQCrREzRiY\nlBlIk5BkD1vnOqYldh4RoDQontaf2ZjS7uBqSKBub762qtChJHqAjwlENCYFCtwzglTAoRqW\naCrG+0rUawvUK1TuYR3XJhcVAKCj0xIxdiypFaQoAADD+pUxS9Qys+TnDK2HM91d4fjQ+w13\n2NRTAIBE8nzTtRFuyKgsuoi7q8X1buiUqw8D7vbt6vbtOusNivAj6RqZhiU9vkqHb3+WYSnG\nuNdXYVFNVNCmQKwX40uMHYsocc2ud3u9e8P8AC9EAOGRPwnGmFkIEQx1ul5fotSfhEUAiPIe\nAGCpM8QkFDLjZX3aAGhCsSD3t7vbH2kcehMwsXTcs0mtjiRJkiS5JB9//PH9999/8uRJhUIB\nAARB0DSNroL4zWUZds8999wNN9zw8MMPW63WZFbs1SC2eIWyp1N27LBoz8CK88T1Iz5ONzeS\nvd0o4EcCj2WMZDAK2bnhTMum/odiovfa1GdylYvYnduI4UH0zcfnpvzgw/5HG4MfpjD5U/Vf\nBwAUDgHG5GA/lsmQwIu2tMTMusx5TO/unx58AAMGBK36rrSg9YbWVYmzFCburh1J/9RxmjXW\np7c6Hh1SumxhEymRRYue2tU+S0neRWCUiNujEPvtzF0yRyVAh5fxZ/tS8w9XI6wCgDJXiSZz\nlht2AUB60BbXKQBgXcvKfF+WjwkEZEFjzPB6wfvze6fn+jMRJmaIq5T8iNNRAkGSUXNaxhVP\nXGE61tKp63utcGNrmlsf1DlUQ2qv4FTBnEpaThwTZRQAoDGuT1tlfYqRimhHPupFMGd8a3+s\nlJJORYipmDR3pImXIs7QydFRccFn18yS0ylwhrsrP+HuytAvGLsLmaqd7Yt1AUCadlah+baz\n3juGUo2sRD2VobQ9voosw9LhcE2U95Sn3jfa7eJjx7K/65f9gap849oJunsYUguIqBv4W5//\nwLk9Lx+Mz6NEdyFoUrkw9393tz/SOPw6gcjF4545NxAwSZIkSZKMZerUqaFQ6Gtf+9pjjz3G\nsuxzzz0XDodXrFhxxS90WYadx+N57rnnUlOvcEmOJKNgjTY+ZyGz6yPZsUPc3EVnnSU9Lmb3\nxygSES02obAEaBpFI2RvD7O/ItYIQo5vhvVbucozRlFIttzyKy1lt7MTRy7BMADQ1b7Bgwam\novF/i92TPjxvZsq35LY0BqH45GmOtrczA/YOTY8uruGoGCOwABgD2ptedcLY9GDNV9u1Pa/H\nrwcTAIBgT2cc7q0d92PAlEQCABAEABz3vmrd15Tvy0pcVMnYPphWt/hYjkKQD7L99qNRtwkA\nIK6Rk+G4QlDl+7Ja9J1vFpyuu0sCAQBqTtnp/rgMj4S16WXZ7dMM1lpnU/9rm0o7fKwfABqZ\nY4m6JlgSEACXPw6HeVWPEwAEVy8Y4OPBxyKi5560/w3haGIrlgpz1q7Yq8WVA23vSCBAokoz\nAADs6/zvoWA1AAwEjwIABry95QFeitxQummsuyscdwJAKNY/9mkn3F0IkCDFjcoLpjgRiMrU\nL2xzfcCL4W7PLppQpGvnAIBCZkKIuPjYUaJxV3+gyq6dNTntwdFGXoxecuAoiT3cmOAZ2xji\nBi5/BgCgSdX8nN/uavtB/dBrNKmYn/vEJxqeJEmSJF829Hr9jh07HnnkkWnTphEEUVJS8v77\n7+fmXnn1qcvaQykuLk5qwl5t4pOmSfZ0qquD7OsZ245iUWbnR8DzsSUrYsuu4csn8cWl8cnT\no2tv5MsnaT2wrnVlmfbmcyekkXyu8eGEiCoAxCCMAZu8ytLYNM6sL9Xf3hDc9I7jXpEhRH0K\n4RwyxS0O1SBHxTFgjhAAYFDp4sj4HvthcyQlRnA5/vRybzmBaACIz5qPGWZJXTkAAB8HgOjK\ntdHla2RRyPdlxfQjTsdmTVs9sT+RANs1y/7anIOJctahLDPJCyCTAYCSly/vmKfh1AhQdiA9\nI2AHgGW5z9im3wenfNRGZpzSVPJ2yfZK+6GQLJQetAOALWTJ92UDQLemHwB841Jdk3LcE7IB\nQMXJASBLNWu64d5ArtUvHwk+Y12Bd8dtdagGTZA2wX7vxNRvG1UjhUsmpn4rU78IAIzKYgSE\nSmaP8E7pVFxdAkGKnej/EwDQ5Ble1YS7S8tmDwWrw/HB0XZftONI7zOhuGO0JUu/VMJCf6Cq\nL7AvXb8gURCOIliTsvSSYxMk7FEFbR5t8USah0MnAUC6vIg3OW1Q0KaBwFFRGhETFjHf5z9P\nlMXFYSjtorz/p2Ezavpf2tvxi086PEmSJEm+bIwfP37r1q2hUCgQCBw8eHDx4sVX4yqX5bH7\n/e9///3vf/+ZZ54pKyu7GotIAnBKZ+yVl5jDB6JWG6ZGdgrp+pMoFuVmzBndPB3tHy+b1Nn7\n7jhPVmwwIKaepw4Z4fOyH72PtfrYkpWeeDeLdIaICscEfsKEqWFtWXNc7uUotB5RMvB5VZLs\nYGY7AKREdZq4CgAQRgCQ782+pXl1YsLl3fMlnsOEpO6sRNEoUgPCICScYc2N8fKJBMZhOsKx\nFAuAAHjOc3fjrazAAkB5o1ZdfoeTeR0ADLXdAMBLYS/rt0QtCl7eYGpb0jM7NWxp1ncUefLS\nt1WHMk2cbsR+igjuiOhxxzvMUrosJnTougDAEjEOqF0AoKSMQWHIy3emyPIwgQDAywYAYJxq\nGYN0tsr6VKOhWxOy7W0QPH2tk7rGebNvalnNWVNkTl/nnU9sbr83HB9QyiwMpZvrmDqscHv0\nMD/311ub7hOl+EfN9yloEwC0uT840vM0J/oBgKHPyF9OuLtyU1bXDPxpZ9v3i823q5jUQKyn\ncXgDQTBjy6MYFAVqJr124B9xIZhjWD7aPiH1vp1tD198bAIFbVHJUru9O03KEiVjdYbr213v\n55uubXFu7PJuT9fOVcjMcCnGmdbV9P+psvOneYbVEuZbXZtYSj/WrLxMGEq3MPd/drb+4Ljj\njzSpmpH5X590hiRJkiT5TPEZ14G9HC7LY/eTn/ykpaWlvLxcrVZnncNVXuGXCNFkjk+ejsIh\nuubYaCPV3YlpWsgdd25/CaQqazUAUD2d555F4RC7cytWqmOLlkdROC6FACHAGIkiUBRbsV1O\n6jbnbT80YVi02JAkAkCrvmvsDAnzThU/LQ8qEnh597zlnQtQwA8YG0LK1LA1QkcBIaq9Rb59\nK8dIfy7750l6PwAYYgYJYSwJQVkIAFQ9Ll/TthpjY1CLMUKAEC0Qb437MKwGLae5tWlNsSf/\nvbyP6owtEkUSnKBt6RdYGgBUgnooWovd/QCQ4TSs7FsmYQwAek43zDoBgEQMAESlwOg63QoP\nAGA4w4PlnJQbSNMBwLDcc9LU4i20E4LItR5KlHBLuLtKXOMkisAI1Ey6UVlEEjICyQZDNQDg\nj3ZYNZOXjXv+LHcXBki4u9Rs2pK8Zw3yvJODf63s+FHj8D/TtHOX5j17lr5qtmFJKO5QyexG\nZcloo0Ex7nLGAgBCaG7O4zp5zuHepys7fuIK183PfbLQdJOWzTo58NceX8W5n4RzKTDfWGb7\nRojrP9Tz29rBf6Rp5ySENCQsz9kKGQAAIABJREFUXM7wschp06L8p5Qya1XP7470PvNJhydJ\nkiRJkivLZXnsCIIoKCgoKEjqLV514rPnU80NdFO9mJMnGoxIFFA4JJktiQi2syCAGNb4AIDw\nec86hTiO3bENU3RsyQosk4Xi3QCAEYTv/CYA0I11ksnCzZvf1Pd4VGkpL/4x2rpJ9PZ7Gd/Y\nSRpSWrdmV8gFJitg18c1PaqB45a6PF/mYeuJpd1zi8S8gIpnQPl24TYNlTaTW0531NV7GiN0\nxISyAKFx3qwqW/XfSt+gRdnSnjkhKnzAUGWQ5Qwtm+kEQt3tjMSiwwp3zWwqXTG9s+p3hZ5c\nL+PHFIUkSWIpgZWxLjekQhqX0Uo2Hfa8pGSUJ81Njal9WMQ6TlttaSgPTa1WV8WlEMAZOaGk\nSAFAQ+ADE12QciprVVAyGtKkj2n9TPD9nI+YaOVM+6SaaEOudUWb+8Mu73alnzdG7bxypFwk\nAoJA9NJx/9ft3XGw+7eT0x5KCFGMdXdl6RbzQggAJdxdekXenOxfXvwtLrbcUWy549z2i49d\nkHO6rpCWzV6U99RZHVYW/mX0+Nx5MvVLMvVLRl8iQMWW24stt4/tc+uEHRdf+YVQ0KZFeU/t\navv+ga4nEJBT0h/61+ZJkiRJkiT/Ppdl2O3Zs+dqryNJAkzR3LJr5G+9JjuwN7pqLfA8AGD6\ngvJNOjZXIETgz4ydF3jZjq0gCdHlazCbELA6I2mRLxrPF42XcBxGswfUWpnLyYiyGBUf7dat\ndZCYMEeMH+buIiUyM2Bfq/vNy+x3GKTcnXFA0GubLX2+uO8G218Oel7YIbxCZAs63ohlQE9d\nJYiutD5sjOiGFR6Bir6fu1MbU5UNF9Xbuvx8r57OFBgZxE4vqTUreCj1bZbUxill5w0zAcBc\n1UoOOAEA2bIWy2+p9b8dj7cIUjwuugHAHDHM9y93zizpcDn8wulAtFCGaYtivTvqtciLWoIf\ndxL7S9A3Rs8iQDe1XLO55IAT9Qqk2GzqvKP+2o3STpUm9eTAX0uCZRgy4yoGLpqKUGC+UcJ8\nm/uDQz2/ldPGvJTVaiZtX9dj/4K76wuDUmZZkPu/u9oe3t/1K4TIyWnf+U+vKEmSJEm+pCQL\nx3/mELJy+aJSurGWbm5IVJtD8fiFOhfJl1BS3IM8p0VJMMDHW5FziJs0eYP7vozI1NnG76op\nq2/kHAAAEkW6sY7uav5B8B5WZBD8NdFOYOJnh07nWsZITkTSDS0rFacEWz1mOQBMJlYvOmjm\nC0ta8J8xiHb5xBvtL4EkKV/7m8sovpj3wsh4kkSYIABhjHQR7X2t34gqoMbyTF1g41zDd3Ut\nffcO3e7Pt7nTswEgm5lZ0Kmd4Juo4VQEf3DEDsWKOy2v8jRsH37MypYsNP0YAPa4nuqPHr+x\n/VqRZcKU8Rrr77oi+w64nz/ryazN/l00EhFFUdlaf3PL6t9P+3ui3RxJWcY+TB07IBQUo3Gl\n+prdpYN5DRa8uvAfqes3dup6p2Y/IqdNY6e6qu6uLwxqxr4o76mdrd/f3/k4TSoSihpJkiRJ\n8jnlUCTypi/YysUljM0UtVitvFmnkV2FsnNXnIvF2BUWFj755JOJg4vwaS31SwS3eAWWy+nq\noxCNShod8riQeH5vUJkwDwDamfoTvjcSLUgUwDMspqRQJ45Inn4VbQMAhlAxpBqDGBV9AMBU\n7qCrjzg1wXfyt7UsTIuuXifYMwAAzveRbTZ0jB4PcfUAYGESwWGYoTQxKSDgGAAkauRGJR8A\n8Dh2VLXv70VvDKpcIpIwYAKhvcY9BJIZeIt1WGY+1CIf8gMAECNXnFObsbB3Zrui5fiEkGNJ\ned/S8rBNDwAC5va5f++OtynIlLGr4rVKOhwjo+c3eSWQ/tyw+rD7b2c/WCkAAGE5BwRBuJ2c\nQcNZ9ONdBQIfZAc9iihZY2qQPklFt8thd9sj79SuvbJzfjZRM+kLcn9LU6qK9h81DL3+n15O\nkiRJklyCOMbn/bcvHP35gBMD/MBs+JnFNFHO/t3je9HlvVB/6V/UjboqXMxjp9Pp5HJ54uDT\nWk8SAAAsl3Pzl7Db3mcO7xeycmQnj1PNjXxx6bk92ZZ2AOg2+5qcT9QF3rkhvlhPkLtn9rV7\nd99Zs+qO9ttQybrE501PZ2Fwbey/fyZ753iHw2VB6+1/sjHjU+3LJSCQMFLXI3znN5WvnI7W\nYgTZEeuJKBXLSluberivMfCBymTOMC4C9CYKhfOUCztCFftcz07Rfx0CHiVAmIkCwK7hXwe1\n/cXu/CGFk0ciJVEiEvfZj/SGhv4/e+cd50Z1vf3nTtGoSytpe++7XveCbXAv2BgMGFMDIaQC\nCSSkECBvAgQSIJAAvyRAIPTQq8GAjY1x731tb/H2XqVVL6OZue8fs9au18YFbGxA34//kGbO\n3Ht3JEtH557znFSPeV7daFnrayjT5h4Id0eqWeSwIdHQ6fGk6jfkVfrltQnhFWm60VMjKQZo\nPun8vYdxjjAvLjTOHfyH+zPsgtNnrW5T9U0GE5V8ALRU3xBYp2NtTCSYQpNFJfhayzXzgjOT\nMHy16+Hk7JQsV2LIndiTtH9yV/qEyBxzR4uooQcT6kOdLze4ViwoHeoUfmkyE6bZ9SeRn/r6\n7jnXjPmmxv+suvyZ+X9fXfu7VTW3MYQrSbr8TK8oTpw4cY5OXURcUN98DIN9ofC+0EDO0Ide\n34de3+lf11flWI7dli1bhjyI87URHT6ar9jPNjfIWbnUYOT37KRGs5Q1qAybUs2Bcq6pQcrO\nmzPyWof7jVr/Kq/UZSKJ1eKGZPtoz8TRKRurpK0bIufNACCwJkJ8Wsa8qfvx4biina0ba7lu\nkv1nDBjW2ct2deBQvl3g+z8RVq/k2poBQKO5sOCldT2PuFpeTcN0myZvQvqNjMakOJLYzvZS\n4Uqvrb3C92G55+3pHVOTUabNHAO874m2jusacX7TtCmtE14Y8ZbIih7GV9SXdzChfkTa4vox\n5wJwespzD0DdHSaKAoAYLAtSHz7oX9ES3Obr3GNzXQQgWTt8csJcm2aohKMvL8Vc12Wp7Yya\ndEgdOM5Iiq4vxBnYKcxlnymv9ImNQBIB4YhmnPWGRFXQQ1EqTfurTETTvN1uL5jAp+e3Jmjb\n2z1FGVZjSaNrFQBZjpyql7LAvvDEjV/fPef4Rmc3Cbr8aXkPrK27Y+XBWxjCFiUuOtMrihMn\nTpyhnKvXiyfTn70mEumR5Il6PfvFm7GWs6M1F6En9ofV1dUdPHjQ5/PZbLbRo0c7HCfaWfK0\n4vF4otHDJGStVivHcb29vWdqSacQps+pf+Fp8Hxkxmxh/RoS8MvJqUpqGuUFhAJcawvjdsnp\nmZFps2Kid4daiv0iHA7Lsqx/5zUSCooTJkdLytRTwWtuINGo/o2XKIF43kzFaGZ7u7iDFXJa\nJl91AICcmh6ec4H+rVdIJPy/2Tvbg7t/13Qv090ZmTRFu2HN20UfWwpmn2e/RepqrC7/R4W9\npk/rCXFhDoI9ZC4Jjx4x4a873C9scj7xvZ6b8xoFyJLIyA+f89T1+xc7RKtH439u+JuzEv+Q\noh3BhkVxwzsrctb7dGFQxRI2ljlLCtMuV/QmrdNrrut8o/gDr9y1gNyy3rS8U6qiimwIC2rP\nif77A84kGpKCCWN8E5NMYxWe5YIR0tn07+HP5sujpg7/0/8Ofp8nukX7p2QEspoXTQZgPdj+\naeSfvSb/CP+ozeY1lBCWERLFlPmV49MCyS2Xz/zU+0Cnd4c6Pkv4K0YtA9AT2H+g6xVnoEpS\nAqAocFwyPOX6TY33u0IHF4/4YG39Xb5w+8yCv+1u/0+3by+lslWXPyb9JtuhKN3q2ttVy3rn\nJ9taHh2T/vMO3zZnoCoqBwhBgf2S4SnXC5xZNY45dl89aKfRaAghkcgp81BPit7ggTW1d1JI\nC0qez7Of+p45JwLHcVarNRQKBQKBM7KAswGWZY1Go8fjOb7ptxe73a4oSl/fUPWA7xQJCQlu\nt/sEv/HPNk6Hy3HglYhyivNuYJ8UTCtOOMWDnjzHL55Yvnz5HXfcUV4+0EaTEDJr1qwHHnjg\nnHPOOanJ/H7/M888U15eHo1Gi4uLb7rppqSkoWKqJ2LzHUFJsEcnTdFsXMPV1YYuXsxVV3At\njVzFPiJJVNAqdkdk1FgpM/uoiXEqUlEpv3enZudWOWUgqMWowTkKzeb14Fg5OSU8az4Yhmtq\nIKGgWoFL9XryxQ5BlIbe6vxRb3ZfmbNobE8ZJ/MBPlCR2bla/2lTV2SU+UoA68zLHZpZppC+\nwdwM4MP8z36+71qJDPw3ahX3rilamhiyT6sfR8dP6/RuWyesCnYH57bODCeaO6cOo4FVoVDz\n5+FnR/jOH5/302hX/Vb5uT9uufWDKQf1pmwACqSA2FnPr621vnlpQ2BYT76k16zLqZOJkpN5\nCctocg1Tq33L+wRPqr8/Fc+bncTUCxHaWccfSAomdhi6xqb/rKrj1beLP/lJ6y879F3wKAzD\nK0pUx9tTTRMlJewMVq6pvdOmLzIKyd5wC6VKrXNprfNDhnACa3EFq1nCi7J3efVPZUVaUPp8\nOOre3PSX9Q33XjTs5QOd/6voei1BVwTAFayp6f0AwO62JxmiSbOco+cdnlBTg+vTnsA+gbME\nIl0+sY0AIPhmfvYehkNfNj3/gbV1d31S9eMLS1/Mtc09/jVx4sSJ83XhrxVOdUI1rKPDxzc6\n/RzHsfvvf/9744036vX6H/zgB+PGjTMajb29vevXr//kk0+mTJny8ssvX3311Sc+2eOPP+73\n+++55x5BEF577bX77rvvn//8J3O4QtuJ2Hx3iEw8j6s+wNVWSzl50bJR0bJRx7YPz54PIKZp\nK2Vm8Xt3Srn5itWmngLAtrcpJguJitHC4ujo8bFrFbuDbW0WJ0wGELroMgBo3xE7K+cWVKe4\nq1v/dQ5mV3uXdeudcxrPm9gxJmZQOvefyzrvrPYtH2u5dqTl8nLPO/8a/UKGJ6XL0A1AZqQV\nWeur0rsgY03PwzrOqvFHrcQyp3GKVhLweV3qhT/p7mrcm3KwaNwd/esJM2E2nJv1Y4d+CgBt\n6pj0rkwABjYxzzgzNm+OacYnnb/fUHxQN+1aBcre9ueNJDlVPwJAkWlutW/5x3mr16Zv01S/\nOb/4SUXgQonmcCiSm3S5eVdlh6HLG24qNMzZLb2+OmnV3oNbrbr8FOOYdu+2nITZB3s/8Efb\no1KAY7Uz8h/c2vywO9QEKDkJcxtcn5YmX93g+nR9w702XYEo+/LsF9Q7lzmDldnWWXn2Bfs6\nXvCEGpvcq01CuoY1UKqsqvmllrMCYImmJPmKqu537IbSmQWP1PS8v6vtSZu+UKIhAtBDzWu/\n0Zl2KomGEVNz/7yu4U8fV9xwUdnLOQmnpXlOnDhx4nw5OIucOP3UhPO9FUKgVji+3dfCsRy7\nurq6W2+9ddy4cUuXLk1JSYkdv/3226uqqhYtWnTDDTeMHz++oKDgRGbq7e3dvn37Y489lpub\nC+Cmm276/ve/v2/fvlGjRp2UzXcLlg3NW2h47QXNlo3hhYsoe3LyNEqCnRqMbFsrKI0F9tj2\nViUxEWKUbWsdcOwUhelsV2x2qtcD0K78hHH2YtrQAa+pujhVFPaN772uYlG6PyXCRRng49zV\nnMJOfLfvsnC+rPsNVYyZxX8qNS+s8CwddUBKCST/fcLTPiFQ6M6d2Dn21ZL3L21flNSro4R2\nG3pXZm04t23cjtS9HY1LRFY6p2Nkwq7l5ojBL4Rz03UGNm/WFqZzijuYatWs+vhC11QAc9Ym\nyMz6v098warJnq5cVVqjDOu9USNzin5ns91JkwJBXnql/mqGcFdUzPtF+PpXS5ac3zQty5vO\nVKzrMfRZ87lWDTa5nmRzGAA1PUvOdy+CFXuM2wkIywgd3h0AKrvf5hldt28PgAzrFJ41hqU+\nSiUAze7VAKq636FUkqkYFdIAtHu3AdjS9NCWxofUxMHuwJ5ApEPDmXyRdoCyjDbfcVF5xwsJ\n+qIRKT/UsObdbU+9W77wgpJnd7U96Qk3yYpID+kNEoAe4dvFdoRlJazj7WnmyYP3cM9Okk3j\npuT8eUPD3R9X/PCS4a+rCs9x4sSJczbAMGD1J9Ri+/hD8adkmFPDsSJhTz75JMMwS5YsGezV\nqZSUlCxbtowQ8uijj57gTDU1NTzPqx4bAKPRmJGRUV1dfbI23zWUtIzoyDGMz8MfKD++9RHI\naRkkHGL7nOpT4vcxPo+ckqYkp7B9TkT648ZsdyeRJCkja/C1l6U9dUvBYXUzEpH5KEYcMGzI\n2P7siDd2Ju/TyJrzm6YuaJwZnjr9pfHLmnQNwrZNjMuVph3NEG6wLrJEZK0kXFZzQTAzySW4\nnRq3OWK64uCCDwpXNJraRTY6rnP43KapXq2/aoLuQJFnWHvWpM4xACgDAFuLG/fbDwJYMbKi\naVbxzMS7HE6uaEMvxPCy3NUrR1c35ygZrcJ1FYtKDfMpVVjCRUlUK2kvq7lgv6PqmZGvv164\n1BTWzd4/jFNYgTUZZas9lDC+c6Su16/eaYD2Bqo0nAkAy2j7ZVyAds+Wjyt/4AxUAmDAT8m+\nB4CkBClkAIoiEpCI5CaEpZRmJkzXaxIB1PYsBWDV5pl1uQA0jKHe9SkAgTXLiphqngiAQtbx\nDgCyoopFg1JQCgoQAkIg0/4U0i7/7s9rfhuVAudk/npm/sNFjssaXJ+urrv97FdFTjVPmJTz\nB1kRlx64rsO7/UwvJ06cOHG+5RwrArRq1apLL700PT39qGdzcnKuvPLKFStWnOBMXq/XZDKR\nQQlhFotlSErvcW1WrFhx8803x56+/PLLkyZNGjyCummbkHDmsxdPJZdcLjfW8fv2aIeNgM1+\nXHNCiMHQvx9Li0qUmip9bw/JyQNAm+oVQFdYQiIReedWY5+TKSoFoOzfQwFtYanObAagcBwl\nxHz4Yz2jDwJ8lDSUsI1yK4B1mVsndYzRStp/jX3R73kSDNan+gt7rzK4nSQnp7z6rRG4WH0t\nWcpyCqeThBU5ezLzhhsrlZW563M8GdNbJ43pKkvzJddam8/rGO/nA68XLSGsRjaIW8o0N++9\nFoBWq1vae28Prc7UTAOg2E1sqn132xMXN4wVGenJvH8rAntR1q1ru55P9yvnN00jjbsUhxyW\nfQDUGSvstQB8Gt/uxP3TWyclhmwdTE9SOOP6/Yv8vH/T2I7+ewUASkTyAJCVge4TlEqE6Rd3\nZhmuIGP62gYwhFUV72SEKKhJk+iLdALo9u8WZT8Av9jOMNzFo5/8cO9NXiAkOSEBQJt309vl\nC9SRZSXKCkEALNHIVARw3aSPDEISgDe2L/aGWmS2J8FYAuCz2ud4Trdw9L9VvzMfU3Q63aa6\nR3siWwuTLjjGO0EQzvzuQJn5QkHLram658OKa66fvCLNOu7rmVf9JNFqtRrNF/Zu+dZDCCGE\nfNs+Ek8SQgjLst/xm8CybFy57EsQ9bKSn9GlRY9vetZwLMeuvr7+uuuuO4bB2LFj33zzzROf\njJyAZPOxbaLR6ODKJkmShqTfqZd/23Ly9AZcuEh67UVl7Sp20ZXHqJZQGXwPSWa2wnG0uZGZ\nMAmA0twEu4MxmWAyEaMRzY2keBgA2txEdHomJWXw4OTwx4eyv+BP4tABI5/oj/ZEeDHIhPx8\ngCGsiU/2iV4A3a69JjmXMgpDGQAEUIhMKAFQa2luqH3oksueaKl9yx5KAGALWZcUrdBJwqX1\nc/c5qiRGBg0BCHKhClvtuK4RFHJE9sXKCao9n+0ILdFJ2tTAvCpbXYgLQca7DbcCCCSYz2+a\nluvN2O+otnD9YeZaS79MESXUo/EDMInGDkNPKIH/68R/AUi1jsWhHrk8q+MYXSjqMgkpvkgn\ny2pkWaTA1ee8/9z682QlSqkSFLsBMIQ/JGVMCBAUnepd0mtspfZLdze/CIBSqcdfod62HPt0\njtXVdi+3GQqmFf0BwKrKP/nCbaGoemG/56h+DQPgWA2AoNhLCIlEvb3+qlzHDIEf2HjNcUzf\nVPdou3tnUfKCE3wznEEKk+ZTKq+pvu/lzfNuOG/l1+bbASCEfNs+EE6S+B1Qid+E+B34EkTa\nuVAn/+1x7Hw+n8ViOYaBwWA4cTEFq9Xq9XoppbFvGo/HM+Qn1HFtLrzwQpfLFXvq8XicTueQ\nETiOG3Lw20B6lq6gmKutDuzcLhUeR+3WYDCocifqU21yKtve6u3poRynb22WikoCHg8AITmN\na6r3uN0kHNY7e6L5hX6vt/8SSWIoVWOlsceBUACAwiAQDQNQFDVeJYX5MACFyh6x3UD0AGrd\nqz/bcz8AjjVQgIIAVGKjCqEhPmSMGD7e/ytoQQkF0GRpBZDDjAfg0wQRa18L2qNzAWjy7NJo\njbG/zsgm8ZpEYwQAvBo/AAJY+WyP1OYT/ABMESMAj9QJQJ0xdq3CKAAYyhCQ6KGCqHBkICSc\noCtUPa1E40hfpFOW+8tpO3vq1fw3iUZe33oZAEnp36h1h5oo0L8lSuEKNBj4zP6lajLWVv/N\nE64H0Nq31aBJAeAONpKodXPzA75wG4Alu38KDGy5vrLlQlVmRZYVAP6Az8N5POEGAD2+6hc2\nzpLkkJ5PykqYUZJ0JQBPsH1wSDsmrYIzLXdyJMnaKRMyf7O95R8vbpi3eOQSh6H0dM8YlztB\nXO4EQFzuBEBc7uS7xHH891P4c7+wsDAajdbV1alPvV5vS0tLaWnpydp8ZwnPnk95jWbnVhIM\nntSFUkYWKGU729nuTiJF5dQM9bicmo5whOnrY9tbAMjpWcccZihaxsKACKzJwmdMcfwSgJ61\nqacYMAwlAETFh0MFAYM/ThiFYYlGJgrQ7971RQ4CoP1WzAUpf9OyVvXKPrGJY7SxN2KadtT8\n5Ad0rC02Mks0fdEmhUpqUJASyhOtQztQ08OQo+S1eqL9kTxveEB53Btp9UXaANQ7D8sxWFb9\nIzUNDgBAdHwiS4RDg3OEMCZtJgB1/lb3WvWUP9LKs3qzkM2AkZSIJ9wIgFJlbf2dNm2BuiqG\n4Wy6IoE1qZfMyH9obtHQvrfucBOAqByckHHbzPyH8+zzqnveXd9wDwac4H4yE6YVnowe8tdM\nnm3+hIxfR6S+9/Zd5gx+p3Nn48SJE+c0cRzHrr6+fssXU19ff+zLB2Oz2SZPnvzEE080NDS0\ntbU99thj+fn5w4YNA7By5cqlS5ce2yYONVvEKTNIVNTs2npSF8rpmQCYznamq4OynJKcrB6X\nUjNACNvZxna2g2FiDt9x6Y5UAdAweoDwRGfgHGWmSwCYuH61vFHdJZnew1Mz6dBfCERWBjt8\nQSYEwBjVA0jXjbbymZQq9pAVQERRW7j0j9Aa3vlO2499Gh8lMEdMANTsNAAm0QjAq/ErUJyR\nhthcCo1i0K8UtT5BYPp9KXmQllE46mKOVt0UlQecaQJQKsaqFmRFBKURyQ30N8ylh5aq4SxT\ncu7WclaGEVhGozphKaZxkhyu7HlTXVVBwoWzCx8jpP9/okWbbdXlDZm9pvs9AHZ9caZ1eqJx\n5LDka0em/qjHvxeAnk8cbFlgXzgy7SdHrv/sIc++YHTaTaFo73vli1zBmjO9nDhx4sQ5FpKP\nkQKnfgv7xRdfJIQsWbLklI+M4+rYPfjggw8++OCpmuyXv/zlM888c++998qyXFZW9sc//lH9\nrt2zZ4/X6124cOExbOIAEMdN5KsruIY6KSdfzjjRABs1GBWrje3qpFpBSU4Z0EzRCnKCnenp\nYl1OOSmFak60XLs5tBVAWPEBoJBlKvrkLgBBuX8HXCMLwUEboAA1RWPievALAXOYYdRIHcCA\nBeDT+INcqMCdzVCmPbz3nbYfE1Ee5iwEBmJ96iZCRPJEubCTtLcZO3I9mTpJG+L6d0VLXQUA\n6i1NMhUtmrSh9+HwPYhzEn683vk4AEDhGJ2khAgICEMVGYCaXXfk384yGgI2LA1sbCWZxvT4\nditUOaRSMrDiiOT1RTpnFjwCwBmoWllzK4BO3w4tbyuwX9zq2RiK9pamXM0yA/UNzmB1rfMf\nzkBVVAkAqHN+kqDLd4YqBdbS7du3ovoXQbFrYdlrqeaJu9ueAqBQ+Y09c2YXPp5oGI7Dt2Ij\nkqe8/cVm18aw5OIYrUWXV5p4Vap5wtFe0q+V4qTFFMqe9qeX7L/i8lEfmoWTCxXHiRMnzteG\nHGSoeIqdkK6urjvvvFOn053aYWMcy7G75557Tu1ker3+tttuO/L47bffflybOABASPj8i/T/\n+6+wbVMoJTXWSey4SBmZmgPl1McMViQGIKel81UVRIrKxcMA7Op7ZW3vIwCuC12aoiT/s3a8\ngU28InJ+Mg5LtSwynr/b/Yo72gwgIDk94cbXmu8D4JM6DdADqLTX9uidLO3vmkcB1mAbPIJP\nG4hF4CgUABrWtCOlfFrrxCsOXlif4RZFz+SWUR2GngJ3tqj4wrKXgvqFAIBzOyc0mprrLI2r\nsjZeV7HomspLtqTtCnLhjEDalJZxzaa2als9Q1hPtIMgpp9MjFxiUO6LbVwSkD2eNwAwIAqo\nTMMEhIIyIBxrEGWvnnP45Hb1noNShnD9ITpFsRjyVekTlW7fbgBR2T/4dQJoinFcp3/nqtpf\nsUQjKxFAUf2+kqQrRqX9DECbZ1PsAru+pNWzGaAbG+636YvOyfz1no5nA5GObv/uNXV/oJRG\nZA8AV6gaQKyoVstbLdrcL3rR19be6wpUjUj9oVmbHZUC9a5P1jX8v1kFj6ou4JmlJOkKSQnu\n7/zfO+WXXDHyQ5OQeaZXFCdOnDhfE7/4xS+uvfbaV1999TSNfyzH7t577z1Ns8b50siJSeLY\nczTbN/N7d4njJp7oVRlZ2L+XyLKcdth+q5yWodm/F4CckR07ONJyhY3P4Bh5uuN2T7QlIvsk\nqq3xf6Zj+0vlzVwqAIH2UeooAAAgAElEQVQ1ibJPraLliRClER1rRVQEQIkCoNRZENuBdUdb\nCAZUplO0w4PyYYnMEcW3IX07S9lRPaW5+zOdur716Vs0slDgzlYIZCoSYI+jstiVN6qzuKQ3\n55mRbS2mjpeHvTe9beKC+lkahfMJwa1puzekb1cItfGZIblvUICOzkq866PO38eCvxTUL3UD\n0EX1AT7AkX7VupKkKxtcKyHDH2lXLTlGK8mhWQV//7z2dwqVZEjeUMPglWdYpmYlzNjR8pgo\n+wkYCoUlGplGCNM/mUJFLW/LtE5vc68PRLsru98qSlykO3wLNQbHCjPyH+RZ4/6uVwBkJ8yu\ndy4DkGE5N808aVfbU7ISBmG0bEKKaUxx0uVdvj1Hf8UVscu3u8CxoNBxqXokzTKpouu1syf+\nPTzlBwqVK7pee7d88RWjPlQrS+LEiRPn28177723a9eul1566cw4dnHOTsQpM7iDlXzlfjk3\nX7adUK2Qkpgc+P5RUq+U5NQjj2fpJrIXzI2g3xHrnVfzcNPlaX2jr8p8CReNDwHoewXA3MR7\nCk1zUYgE4EfSzU83zM7Qjb8o7x90x3+3p5QD0EnC5+Ma3OK6GzI/eLP5B28VfwSAJYJMIwzR\n+KWO/Q6pKqlJLS8tNS2s9i1bnbl5c/aBHMNUDasLRlPsB0UAE5Jvcsn/DkpOjcH2Ytk7Vj7L\nL3VJNALAaQm8ZvrAwDoCcq+JS/VJ/Yp0URqOKIG3iz+O/VECYwawz37QmaFvC9WnakcpiHaF\nK1iGB2ARslnO4ApWtXk2s4QHYBTSfZE2DWsmhEgIOQzDx2f8alvLPzhGS4gADDQETLWc4xfb\n9ZpkMRRQo48KjQBQO1hMyro9O2GOatnuPSw5MsM69WDPe+rjKbn37Wh5vNb5kcNQxrNGADre\nHoh0lCVfW+9cRkAkRcyzL6h3feqPtF86/O3YIF/k2DEMr+VszX3rkw3j0yyTGMIxhBuecv3R\n3x9niJGpP6JUrux+893ySy8f+aFe8x3tCh0nTpwzjiISGj30azzM0EMNKajIUAViz4CzxOqU\n/r0fBqzu5BpX9PX13XLLLS+99FJMa/Z0EHfsvnlQjo/MvVD3zquaTetDF156XFm7r4hDUygw\npqDiGnJ8s+upT7vvplQ282n5hhmx48Hrf0ratpNghyvfDqUPgEwlkfYXH6iFDp2RvaoIHIWi\nRrkcmvx094X2TuWDwk+rfP0O2VT3ZQpDfXrR7WqTqegR2wC4owNFrGqeX0DuxUCNBQD4op1q\n6l6Md9p+ohbdhqQ+AF2RA2rfCC/jAeAJN0k0TEE9cqOaIafWxoqKnzlU1qDGumQqxoROVHY0\n/4MChY5LMq3T9nW8BCiDU/n2tD9nEjJt+mJXsNofaVUPfnDgGpbwBYkXA+gNVtY5/+EMVElK\nAEBA7I5I3lijMLUvBc8au3y7A2JnbFh3qL6m94PS5Cu/4EUDAZlV9NCG+vs3NN7LMVq7oSzV\nPCHPNk9zqPz2LGFk2k8kJVzT+8F7+y5fPHKJjrcd/5o4ceLEOdW4NhnCnV/oEXWvMh55kLA0\n7TIvYU9CQeY3v/nNvHnz5s6d+2WWeMLEHbtvJFJufrSkjK86wFfujw4bcVrnCspOUfFnaQa2\nfZ1ifzX0/OS/8kRbH1y3ve+FwZcwhAWY5tBWuyafQnm/7WYjl+SJqm4NBaBn7BpO7xIb9KzN\np3QCoKCKli/wpF5ddfG21L0Kg5KevCxf+ubUXZudr4dlFwXVsuaI7FMlUTiikaioZrPpWTvH\naEVlQKvMIeTnW2ds7X4Bh352ZesmN4Y2gcAVbQBQZJrXE6lyRerVEg6OclFQo5Dmj7QPqoGA\nhhhUxZa1dXd0+nfpNUkhsVf1RHW8IyL1KVSmACGMUUir6n6TEEIp1BEMmqRQ1BWK9qypv7M0\n6ep65yc8o4sqIQCTs+606PI6fTsAbGy436RJG5f+i3bv1mb3Gl+kZcXBm88v/g/6bxYFYNZm\n9oXqVtX+hgGn0OjBniWV3a8xjDA67WfHeOHshqJLRv6v3bW307e9w7djb9vTlV2vzch/JEGX\n/2XeB6cHAjI24xYFSl3v0vf3Lb5sxHta/jvdHiBOnDhnBMcM/1GPO9caIi4ubdEpUIJcuXLl\n8uXLDxw48NWHOjZxHepvKpHZ86lWx+/ZSfy+41ufDCINhGR3SHb7pe7W0I6POn4rsOZJ9pti\nBiG5D8A46/cLjLOyDefOTLwzSSgZMghLeIVGnZG6sOwhYEpNCwgZCKHZNLkMOAJi5JIYMABc\nYkNfivBO4ccEZEHdrMuq5+VECj7L3rA2Z0dQdlJQQpgETQ6NuVyMCYCBsQEQFV+mdryoeGPj\nJ2hytnY9x4JlDynYJetHqG0wABCQ0ZbvaRhDrJYigoCWt80vfoYQQkABqKuVaH9tb2+gKtM6\nTc8lMgyr7reGor0M0ZiFDACUKhVdr2VYpuo5dWecApiUfZddXwoQUfJ1erfl2uZFD7Up80aa\nrbq8Ns9mAARUVPx1fcvbvJvVawNi14cH+kNxoWg3AIs2Z07B/9l0BYFoV1T2V3a/nmGZOrfg\n/3jWEJaPpblKQByG0uEp188t/OfcoidE2V/RdbqyOr40BGR8xi/z7At6Avvf3395WHIf/5o4\nceLE+abx/PPPu93uoqIih8PhcDi6u7uvv/76xYsXn/KJ4o7dNxWqN0SmzyayJGzddHzrk2FF\n1z3/qZ/+n/rp/22Y+3brj73RjvnJf3VoCmMGGbpxADTMQGjawmUg1n0BAEDAXJb+lAyRQgnI\nvVW+ZeOtNyxIe1g92xTc3CMeVKC4xEa1PNYZqe2N1PQk0xeHv/P3CU/vvji9fKZlS+ruQX8w\nFGUgm0F1LjlWAMARbapulEIHznqj7QB0bELMm9zmfEYt6QDAEJY5/J2vQEkyjKRQKKUco8Wh\nWt3RaTeqBpISbnVvcAYrGKI5tBwU2i/Ksc1Tn+bZ5uXY5qiOIEs4AK7gwcnZd41IuR4Axxoq\nu9+y6/ultgscCwFkWM8FkG6dsmj4uyzhCYhDX8YQAYBClRl5D10+8qOWvvUAUswTEvQFU3Lv\nSzNPBCEXlb48LuNWLW+joF2+nUd9EfuCtevr7lM3lFVs+iKBtUSks7EDAQGZkHFbdsKsbn/5\nhwe+J8pH/+kcJ06cON9cnnjiiZqamj2HcDgcjz322NNPP33KJ4pvxX6DiY4Yw1ceYJsbuKZ6\nKXuoqu2XZpLtxnTdWAAADcruttDOpR235RtmXZD6N9UfGmm9QkZkW9/zn/XcL8p+gFIMTTKg\noDv7XlJdvVGWK8cn/JB776VVUz8bYhaR+8NsCol2RQ5oGKOWsYRk98ruP6vlEYfEhwmF0i0e\nAMAQDUW0yHh+tW+5T+wEgQIpSSjN0I1vC+0AQIHuyEEAat1rbD2xx+qqegOVYMASXm3npVMM\nIdEJIKqm0FEKYFdbfxMIh3GY019BQaNy/4YvASp7BooYDvYuqXMto/2Jg9BrEvd2POeNNNf3\nfgKgzbNJr0nKsJznDFYCaHB9yrNGUfICECVfvWuZOsjo9J99XvtbdY1d/t2uQPX+rpeTjCMz\nzOepBumWc9s8m/Z2PFuaeJVEI1Xdb1J69NRdvZDY7t7a668qTlps1KTKitjsXhOW+sbYbzyq\n/RmHEGZS1p0USnPfmg/2X33piLd4Rn+mFxUnTpw4pwybzWazDaQRMwxjt9tPR7e0uGP3TYaQ\n8PkL9C88rdm+RU5Npxrh+JecAA5NYZZ+UuxpiekCh5D/efdDmZ5zRlquAPBxx2/rA+tHWa+e\nZrxNyyQQwqzvebQxuHHwIBINd0eqE4Vhzkh1TfcH5X1vmoYZSg8WIA0AUoSRIy2XA9jc9x9f\ntD1NO2pswvUA+bz7/pDsBkDAJPA5uYYp3ZHK1tCOucn37HS95Io2AlCoyBJNtW+5nrMHJScA\nJRoBQKms+m5zku7uFPfvd7+nYQxq4p1WFliJU4gS0oQAWEPW5dU/pzqqQCGHHKMu/95MeT4A\nDWealvvAhsY/h6O9I1N+VN75PAP2nMzfVHe/X+v8cMi9KnAs1LCmiq7Xkk1jPKGmYLQbgEKl\nqBxaWPrKmro7aP/GLolInsihQFR5x4uxEbr9ez3hhgR9ESi6A+VGId0bbqKUrqm7nSXaNNOk\noqTFGxrv7Qnsk+QQISxDmFbPxtrepTpNYqHjkhTzhI0Nf1bo0AbVAmu5oOw/e9teOND5v4jk\n5RmDWZt1Xs7dmdZpX+5d8TWg+nayHGnzbv6o4vqLh706WLo5Tpw4cb5+WJPCBE+u9PUE6ezs\nPL7RlyK+FfvNRkmwixPPI6GgZtf20zdLqnYUgM7wfgB+qbs+sD7POGNW4l2ZuomJQpFDUyBT\nCcC8lL/ELmEJf23m65ekPaZhjYKsWVA/Y3rLxCmtExhKGEr8UqddKHAIhRcl/52AyDRq5tI4\nIgTlPi1rZcBckfHcBSkPlJgWqPEzK5c5P+WBFN1wAHZNvurHMIGwQVSDOmTZwZt7xIMACJAk\nlAqsAYCoBDiFBcDJvCBrtNKAlyCxkkQGNo4BeJT27a2PgoBS6jCUqpl2RkFtjEZMQqZhkBgH\npWpEDzU9Sy3aLADdvr0lSZc79KUEYAgrKUG/2F6cuEi1L0pcbOATu/w7VFU5oybl6tGfJRpG\nEiDDcu6i4e9JckimkfL253hGC4CAybdfzDG6Fs+6z2t+HRS71C6xKaaxADEKaVeMWjYr/+97\n2v7j0JdcPfqzZGO/FPPMgkfUthMAzNrMaQX3XFL21pWjli8a8e7swsfOZq9OhSHcebn3pJkn\nNvet/bDi+7FmcXHixIlzRuD0Cqs7ibrXs4G4Y/eNR5w0RXEkcrXVTHfXaZqiMbARgJlPBSAj\nCsDEpsbOdoUPqHugyqCmqww4PecwcSnzkv/i1PWtzN7wSd5qp7Yv05tGAb/c7RLrATSE1lHQ\niOL3S50KJADsoSQ2AC6xrjtSAcAndeJQw/s+sTFmE2UkAAor+zT+qBIiA7PHenIMaMGIbH9Y\nSyE0wAfJoHOCLDCU8YQaGLBROeCNtETkgZIUCtkvtjHk6H0+Njc9BMBuGFbouFTgLABhGW1Z\n8nWEEMehHg+JhhHecKs7VNft3wUg0zoV/bXDbKdvd0DsdIVqKOiU3D9nWKYCIAw7PuOXC8te\nE1gLhZJmOVftEjs19/5RaT/r9u3p8G7t8Zcf73X7RsIQ7rycu5ONY5r7Vi+r/KmsDA1GxokT\nJ06cYxDfiv3mw7LhuRfq33hJ2LwudNFlYNnjX3JMmkNbY5pwohLoCJfXBD4z86mjLFcCMLOp\nVj6z2vdxum60hU9vC+8p97w1ynrVbvdrld6lhcY5Jj518Gh5hmnndIzelrqHV/iXh7+b5U1X\nm3dt6n1CYI29Yi0hTFQJahgzT3RGLjko9QLUKdZ2R6pr/Z/lG2fU+j9f3fNQiemCiOxnwFNE\nJSoDCHMRiZUAgBKZlViZk9mhAXOJkQAEhVBACFKqsGBlyD7BpxBFbSYGgFM4nWxwsy4cqplY\nXt0vI9Lm3QiAgi6r+qmsHCV6FNMQ7Pbt3tT4QMy1HZ5yfVh2r6i6WZU+2dr8ECXIME9u9WwG\nEJa9Le51FArL8AqNrqq9TVFEhrC+cMv+rpc1nEn1ZhQaFRWvWchKNoxSZ19bd5crfBDAztZ/\nq7J2qiSeTV/iCh28tOwtnh0QvSxve2lP23MzCx6JxfO+KbCMMC3vL2vq76pzfrK86mcXlP6X\nIfFPqjhx4pwZzpZ2PSdM/OPy24CckRUdMYYv36WpKBdHfNVv8XLPQE2AwBjNfNok202jLFep\nLcUIYRamPba6+6GV3X9mCZ+hG7co7UkWXHNo6wbXvxTI4xNuGDLgrKZzW00dPfreYld+t76X\noUQh8MkdPhk8o8vWnTvScrmG0QOY5vjtqu77I4pvdc/fkoXSmYl3MmC7IhV+qbvCtzRKwwQw\n8em+aDsFlViJUEIJVYhiChsjfEQ+fF6NpBE5EYBCZZZwBj7FG20nIApRACgD5RR0ZHRqn7t6\nX1IVpRSAUZPqi7QCaHavA5BoHD485QeNrlUNruVq1FDDG6OSjwI63p5unljr/IRltM3uzwkI\nQBUaFWXfqoO3BaM9Fl22J9QUlUMEaPVsAmEIRZPrs3bPVrOQQQg7O//hNfV3AqBUrnN+UuxY\n7AxXO/0VAEKik1LqjTR/Xve7IbfUqstzGIY19X0+I/8hLWfzhOs3N+1r7vs837EwZtPg+swo\npCYZR3+ld8MZgmWE6XkPrK27q9b50fKqm+aXPM2Qr/qLJU6cOHFOFl12VJMoHd/ubCLu2H1L\niMyYw9XX8Pv2SFm5+LK9SsYmXDc24brjmjk0hVdkPDfk4PVZ78UeX5b21GFr+/6NP3x5YNM/\neP1Pl3f+odL3sTliuqjgmcGWVj5zcfphRwAsTHlMfbC698GecDXnCeo4XVATPM9+a3Nwa0to\n2zm2nxYYZg2+5EDHG9CAgPAKZ4wYCSXzi58E8GnXH12RenPE7NV6tZKgEEVko0JUG/DWj+0d\nsS+pyiSk+yJtk7Lu2tX2b3+kLSJ78mwXnJP1WwDuYD2AHNu8fMcFDn3ZR5U3BMWO+cVPC5x1\nfOZvKOjyqp/4xQ6rrsAVqPy44gcRxZdnu2B85q/e2jsfAKUgBKDKxOy7chJmA1hdezsAu2GY\nQmWW1cpS+KJhLwNQ/bwYGZZzS5KuUR/vav2XN9IyI/9hgTPW9C4FYNFm6/hEk5ChYZ+ocy2L\nOXZ9wVpPqGlMxo/Jqf61GVWCDc5Pm91r/GJHJOrhGMGkzci0TCtOuvzUxtU4Rjst7y+r635f\n0/uBd09zl3/3Uc3shpLrxq4/hfOeIO/tW9zt33PT5Lqvf+o4ceJ8DYTaeX+lEO1jKQVnUPTZ\nUWNJ5KT6TJwp4o7dtwQqaCMzz9cufVfYsgGLvrDT1JkieP1PBz8tMs2v9H0ssuLy6p+rLteX\nYHPvEwzDs0So8X+arZ/ME90QA17mNP3VFVAnMnGpTrFuVt6DH7Tf4jCNANAa2jE/77Hdwb8G\nNUEAOt7ui7SFJafaXkLL2do8m1rdE9Msk1Qp4Dz7fIe+DICBT4xIfQJnVccnIFZdoSfcNCP/\nIV+4dWXNLSzh2zyb0swDHTvUEGGmZcrgRVKqyEqEZw1Dwo0A9JpEQhhJER2GfgE8njUQwsSe\nxmAZTY5t9sGeJe5QnVWXD6DJ/TkhTH7igiOEaL4SkhL+7OCvvOHmbNusfPsCltGGo32tnnV7\nO57tDVZMzb3vVE4G8KxhZv7fVtfdrnp1ZcnXJhqHD7HRnqEuZAWOi1JM37A97jhx4pwg4Xbe\nud4gOKSESUHC0XA779mnlSPEOjZ0ppd2fOKO3beHaEkZV7GPqzuI6grknEVto44kW38uQxmR\nE/XiUK2yKt+yXe6Xj3oVx2hlRQT6LyGUzEv5iyfavrH38Y29/5yWeLsqs7e8+uex93WYD0dY\nUWEUCvp6yzVqsgRPtA5NYWdkf5KmhAGzuuYORkPqLc0ArLr8bn+5QmUNZxKDPkqpWZu1ofFe\nltGqTWMlub/pLQVVaPTT6p/7xTZJDoKQ/kJZqqhSwHo+UaLhDY33ggKkPxuPAm/tvfCaMQN6\nfoQwHKMV5QABXt89Z/ApjtEmGkaoXWINmpT+eamyveWxI7vE5tsvPNizpN61bGz6LRS0uW91\nqnmcQZMUiURO7rU5Js19n3vCDaPTbixJuiJ2sDDxks1NDzT3re7y7z7l+Xw8a5ye99Cyqp+G\nJVdA7JqT9vipHf9LMzL1h2d6CXHixPlKKBKJ9B7dCwp1crxVNpZGGA0FoMuKRrq4YL1Gl3X0\nci45dBZl4sUdu28V4TkXGJobyZYNSEkDrzn+BWcIlnCCpAnx4SgTPWrQLt8wM0GTPeRgbeBz\nj9wSe6qLajfXPTC/+Emv+fJy79s7+16ckPAjAPOLn6wJrtzufJ7qtUHZmSSUJGiyOaIFSHt4\nT5/YCGC09Xurev7SK9ZQKKr0ye7kAymBxDrlQ7VSXJUCBuANN5+bfXdV9+veSAuATU1/nVXw\naIIu3x2qlxXRYSgtS/3e1saHedao4YzuUH2Hd1ubd0tY6huTfmNWwuw3ds8lg/6/kyOlnAGL\nLs8ZqDjqjRqdduOq2l+vqv3NsKTvGYW0oNgjK+FO/64ju8RatLl2fWlj36rRaTf1BiqC0Z7x\nibecwEtxcoSiLgA2fdHgg2pPsDFpN+sGBc/avVsqu9/qC9ZQKAZNUrZ1dknSlSzT/55cW3+X\nL9w+s+Bvu9v/0+3bS6ls1eWPSb/Jpi8+clKBsxQ6Lt7X+WJj32fr6v80Le/+o67t/f1XeEJN\n80ueWlF9izfcfMuUNgDVnR+tq364w7OLUtmszSxOXDwu45aYPN6S/Ve5Qw2XjXxnff3dre6N\nCpUdhrJpefclDwrFdXh3bG1+pNO3AyAOQ9k5mb/OSpiBw7diw5JrS9MjDc4VgWiXhjXYDcPG\nZ9yanTDr2CMAaPdu3dr8jy7fLkkJGjQpubZ5E7Nu152hGGScON81JB/T85nxGAbOdUPzmo5t\nf5YQlzv5VkHNFvG86QiH+W2bz/RajsXru+foJAGAyItQY2yH0+XaVmScN+Qf4/YlhKxHjjbc\nclm2fnKNf2WVb9ng4wHZCaA7UlXt+/SA94MD3iUesd8vTBSK5yTdzYClgE/whflIpjft/Ibp\nhmh/ODA34fzYOJua7suyzZ6YfQcASQlWdL0aEntF2csQdlzGrRnmKXOLn0w0DPeGWwDsaP13\nUOw5L+fu7IQ5b+yei/4eFocg/X/+4HX2+iu+6JRVl2fSpLGEL+98bm39nYFoFyGc2iX2yPuQ\n71ggSr5277amvlUazpSZMOVIm6+I6njt63ghKHYPPq5hTYd7dVvX1/8JwKSsO6bm/DnFNG5f\n54t7OwayJ1nCi7J3Y+N9WdaZC0qfn57/t2C0e33DvfIRSssqHKMDoOUSdrf9R9WXOZqNNqoE\nVtfeMSzle3OKHgdQ71zx6uaLAZxf9MTCYS9nWqdvbnpofcO9gy4RIlLfJ5U/Lkq87PvjN106\n/C2/2PZR5Q0x/bwO3/Z3yhdGJPe0vPtnFfwdoEsOXNXgWjlk6k8qf1rV/dbYjJsvKXttVsGj\nHBE+PPC9du/WY4/Q4l73bvmlouSdXfiPRcPfGZ12Y0XX6+/tu+yo9ddx4sQ5tVjHhKxjv/Cf\nZXRImxZl9Qphaf+WCwEAy6jwMa7iTadFyvhkiUfsvm2I4yYKByvZ+ho2O1fOyDrTyzkKqu/C\nyxpOYSOsKDACpxz/fTjY+QtzEQAKo3h47+st11yT+fpE203uaMsu98vlnrcUSAbOUWa78IDr\n4yLjPLVKN6L4tvc93xzcAuCDjl8maHKGmRYq0RBhwcm8TGR9VLcua4tfEwCwr+P5DQ33DJ7d\nri850PUKzxqjsr/ds4VltADy7f2VCmYhozhpcbN7LYALS18UOHNfsHZz4wMLy142atJigyzZ\nf4VZmzWr4B/q05kFj/TfDdJfWkEACnR4dqgbspIS3tL0oDvcMD7zVwX2hQBW197uCh1Us8oI\nIThcOzDLOnN361MNruXd/r25trnsFwjvfRVSzRMKHBfV9n60tPJah354knFkomGEw1imOl4x\nfOHWROOI83LuVnMQU8zjnYGqBtdnY9MHgoii7CtKvCXLOh2AlkvIsy/Y1/GCJ9Ro0xfiCyhM\nvLSud+m25n/ISnhU2o/VgxxjUH1KAhIUuydk3jY6rT+hsy9Yl+2YeunI/9GoHkBWwswu366q\n7rdn5D8YGzMsuaen3VjouBiAnk8sS7luc+ODzkBVknEkgI0N93OM7rIR76medJ593nNbR5d3\nPJ9rmxsbQVYirZ4Nw5KuGZX2E/VIrm3u9pbHCWGOPcKGhvt4Vn/p8DcFzgIg3XIuIWRt3f+r\n6f1g8E53nDhxTgfFlwxNyx5M1SsItyN5ImzDwOsBgpZV6KtE4UVaTnuM64415tdG3LH71sEw\nZNGV9KnHhe2bQilplDtLX2ICmCOmPp07IATNIfORG7LHrasIsxFBFliJAeCNtvilTgKWUnm8\n7YcSAuXud1iiaQxuSBSKjFzSRucTAbkrWSjtilRm6MYHpJ61PQ+zYNSVcJyuPqHp3NbxvKyp\nSaiPyJ4hc62quc0kZFh1OT3+/ZnWqa3u9Qzhm/o+SzSUGYSUnsCBut6lhYkXH+x5v7FvZaZl\nql5I7PBtd9ZWlyRffmSr1i7/7jV1d5QmXT0y9UcA1CS8/lQ8ABSVXW/4o12tfesismd4yvUF\n9oU4Ah1nB1Dd806iYWSKeTzP6DlGm22bVdv7EYBc2wVf/rU5JuMzbsuxzW1wrujy7TnQ9SoA\nhnBp5knDU2+wanNUm+KkxcVJiwdfZRQynMGqqBIc3AE21Tw+9ljPOwCEJSfwhY7dvo4X1Ac7\nW5/Y2drfxrcs+Vo1PqdSOEjwZVzmzbNH3BUKhQLR/g6/Vl1+p2+XKPs0rClmlmMbKKk2CWkA\nAmInMDIqB9o9W/LtF8bioxyju3Fy9ZBVMYxGzyfVOZfl9M7NtZ3PMjzLaCZl/x7AMUYIR/u6\n/XsLHBeqXp1Knn3+2rr/1+LeEHfs4sQ5g4heuKuRUILciwYOKqcyXfn0cpZ+68f5SqSkSeMn\ncds28eW7xbETzvRqhjK4PuCTzjuqfcvz0y8tMp4/2EZVpwvIPbEjUwv+zBGdwBgBfNz5e0+0\nJcc4bdKhlvY73a/wjP5c263reh/Z53lnYcbDAq/f3PWsiUve2vdfBmyUBjJ1k8Zarl3d+7em\n4KYR5sVJQmmVu7/7lqgEDJIhw5+i1saGo+4jlx2RvFouQW23erDnvV1tT2q51G0tjzIMl2Qc\nNT3/QQZcl293ecdzlMolSVfOKfrXgY6Xj9qqlVJKqUKhDGy8UlACAIQCBHs7njUJ6SnmccWJ\ni4+adgYg1z6v1WocliMAACAASURBVL2xtvejpr4184ufUR2mfPuC2t6PbPrCBN1prJ5x6MvU\n0uCI7Onx72/zbGx0rerwbZ9T8H8J+gIAshKp7n2vpW99INoRlYIgNFZZEhuEEEZgB3waVaZu\ncADySEqTrrIbhoUlZ3nH86LkL02+Ot9+gVk7EJYmIDo+MfZUUsLrDj64r+Vtd6hRlHwApVQB\nQActgyGslrMNGkFdhgQgGO2moAYh+dh3g4BcXPbqp9U3f1x5A8/oUy3nZCfMGpZ8jZazHmOE\nQLQTwOCALgADnwrAL3Yce8Y4ceKcVtTPIWHg8wn+NngbAQBnxV7rcYg7dt9OpOmzmeoKvqJc\nzsmVbY4zvZwvZIbj9sbAhr3uNzJ156gCyFXd70CDgCYI4IP2Xw42zjfMnGj7GQATl+yJtoyy\n9leGRmmoV6zO1k1OFIoXpz8LQMMImaYJm7ueTdOOHm39HgVd0v6LrvABp77hguSHVB9iefXP\nTTAB8At+ABqF92h8Zb3FuxMrVFnjIYiyd3Zhv6JeuuXcXW1P2g3DFmQdJh18QcmzscdmIWNy\nzh+O+lenmMZePfozAKNSf3JSt0vdvVURWMucov8bYkDAYdAe8elGYC0ZlvMyLOdlWmesq/9D\nde+7k7LuALCx8b5279ZCxyWjrT8VWAsIs7/j+VbPpq84nU1fnGE5D0Cyccyq2t9Wdb2ZYhqb\nb18QMyCEGaxjvHT/D+qdK8Zl3Xhu9h91vAMgW5oerHMuO8rQR4OAwfF8TZUk48jrxm3o9O5s\ndq9u6lu9of6eHS2PLxrxrsCavmiEfkd3SC0NoTgUt40TJ86ZQrBCa0NvOUzZEBLga0LXdiRP\nROdm9OyBrewwn+8sJO7YfTuhHB+Zu0D3zmv8lo3yBReDnKVfFXrOcY7tZ+t7H93reXOS7caY\nUokuqmVp/5tzbFp/WM7ADQRjCEgs3hOU+iiljcFNjcGhrkNdYE1tYLWkBFlGo1Blfe+jLBES\nhSKvr1YgAqHk0GgglPQJXkfIdlSvbgg63gEgJPXGjgxRKjkjUNDyjucEzqIKIJ9yFCq1utdT\nyNkJc4acSjVNUFPcAITE3nbv1nTLueMybo0ZROVTKf5kEjJn5D20uu53a2rvYAg7POX6I238\nkY5654qS1IvnDXssEOjfihXlwInPYtCkEhBfuGXwwWC0R5YjJm3GEGMCkmoen2oePzHr9i7f\nnrf2zt/W/Oj84v984QhCOgHxR9oOX3MbAJOQfuKLjBMnzqmHoOh7aPwY9UtAWJhzUXI9CAtP\nHVpWgipIO/WVaaeSuGP3rUXKLYgWD+OrK/iqA9HSobKuXwUSFfnqSraliXg9RIpSjaDYHFJu\nvpSbf2wPUrtqOdPdGbzmhsEHxyZcV+X7qCGwNs8wLXaQVViN3J/7n6EbjyMgIMzhNd0ZuvHT\nXefn7vVVTbZ0Obzb+14KSx4Dl5SuHc0xurDsbgvv8kbbNYxelAMhPhTmwsaIiVMGAjxuref5\nkW8eY/1v7JmjRtrUQEsssqLuqJ5B3y4k9vaFaxtcK9q9Wyfn/CEm53FqYQhX0f2GL9JqErKG\nKJ409q2goAm6AgAKJAB6Pil21hWs7vaXA1DoKdvGsOryZuQ/srrud5/X3s4xuiOT0hREAVh0\nAxu1Xb7dqoTNiQThALCMJsU8rsW9PqYjKFPxtV2zjELK1aMHCmN7/Pt2tv57cs5dlkMphsmm\n0VrOFo66jj1Ciml8s3tdKOqKFRTX9CwFkG2bhThx4pxR9MkY9qOhB0fdejTTs4+4Y/dtJjJ7\nPtfYoNm1jauvJQEfEUXK8TQhIZpXKOcVUvbLNN9kXb3C6hUkGJSTU6WSMvA8CQXZlmZh4xq+\npjI843wqnJxXwYCdmXTX2y0/Wt35VwvM6sFFtfPSAsn/Hf4GTqCKwsDZGTAyFY1cMuAzsPYd\nriclJTwr+a4UzciY2Rhcu8/77j7PO7IYMEXNPq03zIeNkQHdkD6t97irVX27ULQbgH5QOteZ\npSuwd2vTQ1qNfWLW7dnW0+gWTMj89dr6u1bV/CrDMjVBX6jhTKLk6/GXt/u2mIT0YcnfA6Dn\nk42atKa+VV9YWaJJOu5EQ+tLjkaCLn9a7gNr6+5YefDWI7uZmTQZVl1uecurqaYJAklu927b\n1/HiyNQf7mn/b1X3WwX2i46Muh3JeTl3v79/8bvliyZk/YqAO9D1alDsml346GETadOb+j7v\n9O0em3GzRZsjK5GDvUuC0Z6pKX8+9ghT8+59d9+iDw5cNS7jFi2X0OXbs6X54XTL5LzTVvgS\nJ06c7wJxx+7bDBPwg1AoChPwicXDoBFIMMC2tghbNsg1VeHZ8yEcq277SEg4JKz6FLIUnjNf\nTh30vTj2HM2+3fzeXcL61eE58092nenascWmC6p8n+SlXaJA2eV+2azNYEPkBLuNcURIFEo6\nwwci1AugJbQ9xLtG2S9vCWw3MklGLqVPbKz0LR1puXKEeTFLNAl8dqp25HttN1mE9Nk5f1rR\ndXevWOM1hlxK34lM93b5haWJVwFIMU8A8PruOQRQJUvOVNAuJ2H2adp+HYJdXzKv6Knqnne6\nfLtbvRsVKnKM3ixkjUj9UZHjUlX0hBAyNe/PO1v/fYzKkuNOFKsvObaZw1A6veDBNbV3rjj4\ni0TDiMGnCGEuGfHKuvr/98mBXzCEz7Cce+nwNxlwLZ71Gxv/olBpXMbxBZzTLZMvG/H+lqa/\nram9ixDGri+9ZPib2Qkz/z977x1fV3Gn/z9z2u1FukW9F8uS5d67jY0NhBIMAVJI+GVJdtPL\nLxuSbAiwSSAhIZu6myWF0MPSA6Ea3LstyZKrem9Xuvfq1lPn+8eRr2VZrhgM9nm/zgvumTMz\nZ87RBT2amc/zGVvHzKV/YvrrO9t/vqvjl0klyDP2dGv5NZP/Uuq99vQ9ZDnnrqt+cWfHg+sb\nv6VoCbuQMzPn3+bmf2vsNkEDAwODc4VQ+hHIaHsqwuGwLJ/gaOp2uzmOCwQCp2pyOeB0OuPx\nuBqJ2B75HySTND2dGegXl16hFBTpFYT9+/i6fUpBkbj03NSAsHcnf7BenL9YKas4+appw9tc\nZ1vyiqvU7BwA5rdfJ9GIuGi5adsGJhaNffKOcUuxbFeHcLCeDAVANc1q3ZG2cUd2XanzqvqR\n//ty89fcAarYLL1LJ3vq2syDI4RSyW0bnlaYTLNvCvyyJ7Hv1rwnXI29rsYeNiFJZmaHb5vE\nM6ub5jw/ZcNhxwET62IhfJz7gf/woDAcIbIcMyWDGUJvhUvilfbE9rbY1oWeL/PEtinwcwos\n8X5rc+Chkx9qQhjCeW2VK0p+QQjzVM0qQo5vg7/oO+1SCIJACLmwKcU+nAzGGjY230WhXTP5\nr0Xpx8OrOY5zu92JRCK1x+4yhGVZu90eDo938Lms8Hg8mqYFg2f1l9ulSlpaWigU+oj+xvd6\nP7whgB9CjBm7SxZ+3y4SjYhXrFUKS6yP/I+we7ualUMFAYA0dSZlWS3t+H8qJ4swAOxwgNtf\nww70EVmmNrtcWMy1t1KeV0pG91eNq6B4/QC4jlZd2DHBISKKph2bwBJKiPXvfwOYVB4GtrvT\nvOEtarEQjqOSxMbjiyIzfVH3+in7jg2KsKKc+9Z+omkURHZa+GjSv/Vw51Uz9cvp+9vdR7op\nQwBwGrNgYGGciQJIqCFKyWcO35Qec7LJRjCEUNK0OCfUt3t6c1ZGV4gSLNJmSKZZUk7mzoIj\nAuOQtdjZqzoAJs65rPh+XdWNu/RhiKK43PDZpiwpundT6w9fPXjHx6oe/WAmLw0MDAw+nBjC\n7pKFazwElpWrZ1Cel+YtMm3bJNTsFuct0q/KVdPGVqYcyyiysGurUlquWW0A2KGA+Y1/aC6X\nNG+xZjZzA/18fQ1RVc2XAYaZsAJXvw8AExoe068GWZZnzVUzskg0Ynnzn9BUqCpYlhkJg+ch\nydLM2ZorjUiysG1j2XDxjqHa6SU/NLcBCDOyEs/xRAp8XCzpPtTJqJSomjASX+r9tmUg7D5y\nAAwJzC2XzbwpFEtr6HBRm+4y5DOXuaz57NAgQMIVuaKNT/hYD53PNnZxYIdK3YM5fmtYTmvo\nWDqYVbrqj7o6BMAM9m/v/VVzWvu6qS/rznBbWu/uCm+7vurvFt6j19nX/bujgy9GxK7XDn8R\nGA0X+Uj+FXwJkeGYtbjw3i2td7968I7rpzylu6IYGBgYXIYYwu6ShR0eUtM8lOcByPOX8EcO\nco2H5aJSzT+R4SoFScTVKdPkiiq9QNizAxyfWHW1vg9PysiiVBPq9kHTTlmBZYW9OxE/wdhC\nrqpWCooBULOFWm0kEmbDQTXdq5RXCHt3qiXl8qTRO3Lt+WxbE69ye4J/nUbvIJQCGJhVopk4\nABrP+vY0A2ATEtKQXtsKIJaVHsnzAEj6nAD11LYBYMCJWpRolFAqua3DlXlBuf21nrs+X3+L\nxKSZNL5rZNvwpMoC/0K9SbDxjR3uTVFlUNYSBJSmacCole6Rged097WXDtwCgGVMFs7rNOcC\naBl+7bYZb1PQv9euPhtZt6HlrkC04aapr4z7bHChyHLOWVDwg23tP3654ZPXT3k6x7XgYo/I\nwMDA4CLAnLmKwUcRRYGq4liAKmXZ5JXXADBt3UhGwiQWHT0U5YRG+aOb8IgkM4P9SnYO4Tii\nKvqhFRQCIIn4KStkZwMgJ254V/KPp0AYjcONxwGAAhzPNR+1Pv032+N/tj3+Z7a9GYA9a1pY\n7oqpAQAgRFd1ABKZaaNjo2AlxRSOA1CsxyNwY9mjnhEOPmNE7qaKCEBMswOwsb4rnN/Oivmj\nfhMAh2gf24Tt6/IKZUu8X1+b8dO1mQ/kR/MBYMxOFAIyN+9bc/O+NS3r85nOGcOJRgBNgVdk\nNTYYrddrGHwYyHUvXlDwfUUTXz7wqb7I6Jr+T1+3nr6VgYGBwaWEMWN3icJxlOVI8vjkmZpb\noJRM4poOW1/6v1ShtGAZScbZrjYmMATA8vrLappHLS3XXC5QyrU2c63N4zomiThRFZKMnaoC\nZU/MPW8ZE3g7mjVLA2DatB6yRAWBSBJlOZrmgSIxoeBCz5cP97wbVfp9OMGmVTUf75ZNTOAh\nrFpGRZ5fqKR0R0QOmCBoLANAYKx5qARqebsf6KNkVLRFhBgAr5LtSDuevUASAICJRuA+nk60\neEx6g5k5X9nU+oO+kb0bmu8KxA4RgnHbkY1tdheRPPfSefnizs6fv9jwiZunv+h2L7/YIzIw\nMDD4QDGE3SULzchkertJIkEtFr1EXHkl19kKWZZmz0MiIdTXCvt2Qkxq/kzN4WAiI2puPtve\nynW1a7n5AJTcArl6Gr93FzfYn1h7LQCu8QjfdIQ7ckjNzUtVSN1RqNnD9vXIU2eMGwmJRq0v\nPB1fd9voeTJpe+xPAJS8AnHZKmZokOvpYnu6mVAQgG2ELPD8G6VHKOgZcyuxyTEK75i8yrFM\nd5OcsNrrRQEXP1ECyhKAESFKCINjVrosc1wyDkvNfUIXADo0APcJeTxTMIRbXHjv8/XXD8UP\njfdjPjbifx6+o8B9RYX/EywjnP4RAPSM7Dg08Eww3kih2QT/uIaiGj7Q91h3eEdSGeYYs8tS\nPNl3S5bzQ5cC+MNDYfpqDeruzl8+V3dTYs8QgF9v9n19yeAZGxoYGBhcAhhLsZcsckUVKBX2\nbE+VaC53cvXV0DSuvY260wBAlsTlqxJrPqY53QDEuQvjN96qFBQxXR0ghGiK5vVDMAHQvH7N\n65dnzaU2O1+7lxkaTlXQvH7N4+P6etm+HqWgWCkan36eHegbe8qEQvoHarWDEM3rl6bOlOYs\n0MMQuEN109y3CYyNgIzNpDl2lk6xCiCEsoylP0jU0ZVfPpYc7Z/wqzL/g7ACAKFv8NDQcy82\nf/6lgW+CQB5sAZDMSM81zwHgSjoB9LNd7fFtQ1LTocirW4d+N4lbAqAt9LaeIGtCOMac6RiV\nVmsm/fG2GW8vK/kJQ4jfPnVx4T3Lix/IdMyq73ukrvd/z/hj6hnZubnlhwDm5393SeG9Jzfc\n1vrj1uG3Kvw3LS3+yZzcb3FE2NT6g8FYwxl7vpwpTl87J/ebCXnoYg/EwMDA4IPGmLG7ZJGn\nzeRr9wq7tmnuNLl6dBZNnlzNHTrANR8VYlEAakGRkld4QjOWlRYuo3YH29PD9vaQaAQAGAZJ\n0bx1I9vfBVCAMW15h/Imtrebr9sHTeVaW5hYBIRwHa3MC39XS8vlylG3WPP619meLgDW557S\n12L5w6OihD9ygDqdqsfPNx3mWptACCjlurrMe3ezQhEwAkCjavaWI3xUjBaMmrP4dx4V0+2S\n08JHEqykZW05NFKcwYfjrqM9oxV2NToKMjjzFIohhpJZu3gxK0dkpSgf88TdCqPMaS6MJzqG\nq/Ls3cMABj3inuDDrMYUjOR8pm0NFcwDhXU1prdcr0f4iuLUi7G197nrmkwDYYAqdsvcSQte\nJDt41ppmKQEQSXb57NWLCu82cW4Amc7ZQ7HDrcNvz8w5gwvu6RuqmjQQqy1KW1vmvUGvn+2a\nf7D/SfJhTf774aHYc/XOjodS78mYtDMwMLhMMITdJQvl+MRNn7Q8+6T59X/wtXuVolJYbUTS\n7WoJiUZAiDh7gshBynHSzLlswaDpjVcsb76qCQIFsb72EknGKW9SJldxB+oIy8BqoWFRqN8H\nloOiwGKFqlCW1bKz+dq9OLa9T5y3SKjZy7U1Ja+4Sti1jYmEVX8WO9ArzZwv1OwSdu+gLENU\nlTpdmmBiAwMA5Q/Wcza7RigDotS/y8iZbFJ0H+zUOIZRtGBlnrOljygqANUsCOGYb2cjoRQg\nqsXEJsRYdrq9uZcQQgjpWzFN2/3mtc2reI3TiEYBarOLHqe9dcDWNcRIStLrLK784vTm6701\nrYkMd3hmFlG1O3fzlKqCyr05OQxgYeEPbR39Wa/vSmSlD6ycqfGsraPPW78bUyFwLv0xJ/nX\nTfKvG/sa7abcofhhWYvrtimn4vQNGYY3c+nd4W1doXnZrvkM4RjCTZjz3mAcoxaD9Hhoi6Ht\nDAwMLgcMYXcpo7nc8c99kavbyx89bKrdg2TihH3+lFr++aLmTpMnV53cVvX4kmuv4+v2sT2d\nRNMgJuWScrl6OrVYqaII9bXJlcvAMnzdPravmxBCQZWiUr0CGR7mWppUfxbX3UHtDmoxA9Dc\nbmnaDPOWDRB4AEpRkZqfx9ft47o6oKpETBKzRU+PYX7tJXZ4iFptD1c8trJ5rilsJRSSyzYw\nv1xyWgAQjaYd6BicXWLvGjINRQkoCAmXZgWr8zSWBaClOdx7m0DIS4Hvogg3NV7lkpyPV7zg\nTjqvjt1q7R4mmsIk1R5fVFoyB4S4j/SoZr5vUQVlGQDKoCWrOZ56FTva768mv0tmpvetnqOH\naMTyfDuZ3wFwCnmjr0sTjwSe7wxujsm9shIHoaOv+kxp70/fkIAsKfrxjo77t7TdwzFmj60q\nyzmnOH2NwDpO363BKMbMpoGBwWWGIewucSjLyjPnyjPnAhD27jS984Y8fbbqyzC/9SoA1ePl\nBnrZ9V3ikpXiitXj2mrpHnHFatO7b3Fd7fEbbqbm0SAM6nAAIGJCzc0/uRUAOFwkMCgtWi4K\nJ0TIqkWlsaJSYc8OPRem5nCJi1dMkPHK4UJgMPmxmyvj5Cn+gc82fSYv4O5ZVpWyPpGtAgDV\nxPcuqWQlpeClXbGctKHphakOkrle7G2KFvkBmBWTP+HpcPR8/sAn/jb5+d6Zlf3JA3s7f/OV\n2s8OMT1mRhEkysXFaIFPV3UxJfCk98GvdnzOJR0XT6GpJaGpJQASUiCYbGodfrPT0gLAax5N\nwrG17b6ekZ1l3uunu+80sS4QpqH3L7oH3lhiUv8/Dn7Kb5+aKjljw3Rr2VUVfx6KHe6L7O6N\n7Knr/uOh/ieXlzyoLwEbTMjJGUF0jEk7AwODSx5D2F12qAVFcvlk07tvEEVlhwLJFWvNr73E\nH2pIZZKdAEJSqg4AZVgccy0BQFSVP9TAtLcysQiRxkahnlU6htM0n+q+5UDkxbg6TIk7peoA\ngCEAdNMSPahirKEdANVqBhAcrIUdNsUCgNDRqZvXj3zJ7p0UN4sAbElzS3z7JG0eAMU8GoU6\nIB4CILEnhNMyiuqqb7E19xzl92zOf82u2NNVd9AcLnAsBZCQAj0jO3NcC2flfjXVRFZPMGrW\nGYzuH3t6lg0JiNc22WubPCXz9uH40bcav3Kw/4lFhXdP/EINjmXsJQz9e+3VPnvlrdMM9xkD\nA4PLBUPYXabQjCzS00ViUbajjQoCxBPExNgksHoCMaIolDvh2yLU7hW2bATVQBiiyEpZhThr\nLjWZQYj5jVeObeY7jvW5pxJrrk3lvTC/8Yq4Yo2wexvb3wtKNadbXLScDQe5xqNkJAyAazxC\nq6pX+e5W8CRAhcGQ53CPMBxhFE3jWQD6NjsdW8+QtTfUu3Syp67NPDiiaz6/adLaSeuEcBxH\na/OiWQA+e+hGypD75/2+0DwfABimMbp+snkOMJoa7N3A/b2J/QD2ZjasbV2W6j/jzd22jv5w\nVREy5pTHQh1MXYQPzeyf0uPZXmLP1W1TrLx/Y8v3IsmeFaU/29n5i4FoLYANzd+flfvldOsk\nAKom7ej4GYCBY/JObzgUP/Ls/mt1rxO/bZp+VaMagGC86fDAM9XZn7MLo94r6dZyE+sSlcs6\np/tZEhG7NU322Sdf7IEYGBgYfHAYdieXKXL5ZFBKBRN/cD+RZS3Nk7rEDg6Y//kiOzggzVuc\nWHWVZrWDUtO7b6T25zHBURcJadFyaf5SosgAKMuomdlaWrrmTiNnMVdHxKRp52albLKu6piR\nEH/oANPTnVx2heb3AxD27WSGhzPMVQ4uE0DOxgOMrAzNKu1dWhXPTAOQ1tBBNKpbn0CljCRn\nbD8ay/N2rZ0RnD8JgBCKEVXTKygWAUDvksqN8wMaVSuYJQBgdQxJTcPoB9DXv/H1I1+a4/58\noW0RgBnD0wHo+dNYlW1MvrNzctfrRRtejt/XyO5JsPHS2KQ1bUsP9j9Z1/u/Vj7DLmS3B9dL\nSlhUg+ubvjmS7CjxXA0gInZuarlbpTIAlvAFaSsBpFnLWWICEE60AZDkUKnnmmlZ/2LiXE1D\nr7gthQDagm/FpQGrydcb2b2h6XtNQ//oi+zpDm/b3v7TpBIs8Vz1Hr8AlwP66/XYKi72QAwM\nDAw+OIwZu8sPSSSJhFJeye/dxUTCoJQyRK6erl8kimJ6901QSFXV+uKs5nQxkTDb18t2d6q5\n+QCYRByAVFmt5BfofijUbOFamqQ5CwGwQwFIEgB62rgBIsvilOmayw1AzcpmoiNsf0/8xtuY\neIwZHDWQY/u6tfR0j1BC0CMyUsuCbJPFA4BLiI72QT4u2roC0XyfmG4XhqOE0uEZWdE8LwAu\nkgRAFFUYiYtpdr0CANFl3hd8w85lFA56gRjJKQZwUN1QZarOj+QwlLFzfhNxmhTBG3MBIGIS\ngMQkXyl+BwAJEIZwGfbplfzK+TsThNJ0S7luTbKk+N69Xb8bjDVQqjpMuQsL/4MBF4gdHBE7\nKY3oCgOE6J4mPGPW/UoiYne6tZwhfNPQqwzD+e3T3JaSqNjnMhfu7/0zpWqF/xOryn97oPfR\nA32PicoIz9ic5vxFhXfnuZde0O/EpUk42Q7A5zBm7AwMDC4jDGF32WF+7eVxJUSjwp7danYW\nE41y7S0QRc2drk6qJKoCgBybqGMH+nVhp2TncS3NEAQA1GanDidiMaKpbG8vExrijh6kFitJ\nxPnmRqWgiNrs1Gob7aG/l6alp04ZSdRMJupwcq3N4HiIovXZJ4DjFhUkEgHAUhYUra7OPfH9\niyxfGztyy0A4mu8LTC3IebcBAFRqGQibhqPOg52qy8qG42xCQhoCUwuyNzQAUNr3+xXrLHV1\nWlNn0uuk+eXegbK2+Ja9aeKcvmlr25cdiP4kzz55WfNNCTtnDylTo7PaM9uGoge+1fB1RpQD\nS6ZKDqulf9h1oDU8pchd3zJ1cPI2a5usxV3mopWlv9zSendXeNuykvtNrAvAVRV/ah1+Y2fH\ng0llaHnxAwD2df8BwIKC71l4HybyOtne/kAo0byq/KmUSYrTlLug8Pvv/ed+GRJOtsGYsTMw\nMLjMMITdZYe4cKmakw8AoGw0yu/bxfT3Mv1dzEAvtdsUXwbX1cGEhq1PPjKuIYnFRj9oGgB+\nzy5h++bRcAcKAKZNb6mZWcmVa81b3iHJJF+7F5TKVVPl4nL+yEESjQi1e/ja3fGbb+fra4kk\n8nt2Kpk5yeWrhF3b2f4+EFCMJpvQU3Ox4WEAUBUAqtXUHt9ebFuWZT6exIxLSABErzPhdVqG\nRjx1bURVFaspUpGHdLtz8wF9s53odcZyvPauQP5BsUi7TrEKQ2VpQ5P9Go0W2BbsDT66NWsv\no7EVwZI1bcuC5uCW3N3zhNvsoSAZCSMTFLRv7VzvlnrfhlqFkbtcA8+XbRG1+K3Wa6Yf8Ybz\nysZ6mhDC6KpOhyEsAI0e3w44lvM2STE4G8KJdo4xpVmLEvEJYq8NDAwMLkkMYXfZQX0ZauFo\nQgUVkKZMM//jWf7wQc3lTFy7jhkJcV0d45LAjiKMRp5y7a0A1JxcNb9Qj5bga/ZwXe2Jj99K\nBQEAGJaaTPGbPzXa0GySK6qEPTt00WJ95lECgOP0CtRiSV55je2xh4Hje/MopYQQ1eMDIM1d\naPnHc7mW2YS8tCf4yNWZP4/m+2I5nqLnd6S28mkCB5C2G+aOjlQwOboCY8euWgQA/zPtsREh\nMlo0Js9Zkk/uzKzdmVkLIM7Hk7y4KMwCIJHR7GdiurP7ukWbWn6gW5NUub9pYl3tU5lXe//S\nFd53wpzbWfDSgdsYwl1X9dSujl+M8zrZ2/XrwWj9c/U3rCz9pd8+DcC7Td8ZThxdV/3SOd7k\nrHhfO7+4mNwxJwAAIABJREFUUKiRZKfPWckQDjCEnYGBweWCIewMIM9ZyB8+yISCXEebmp17\nPEvsRJB4nO3uVPIKpLkLjxcq8rhahJ4QP8Hv2X56r1hqtZF4fOJLNhsIsSVMVc4bGsLPH4m8\nVum8jkuIAHS74LOHgs5yf9bF56RKdnf+RuSkKcPllYGy9XlbRXb0QeyDMUqgJMJ6/ASAht5H\ne0Z2plsnnWxNsqfrN+3Bd66u+Os5Debo4Asne51Ekz3jquWlLfVYJ51TzwDebvzqUPzIrTP/\nARx/RYqWfL7+BpYx3zjleUKYVOdRqeeVg7fnuBYuKbrvXG+UYkvr3d0jO26Z9uapKnzAIjIq\n9qhU9jsqP5jbGRgYGHxIMISdAbi2FgBgiLBra+K6m1V/pp4lltpHHXqZ4DB/5KBcVa05XNBU\nANRqTzVnhwJsfy/GREtoJhMjiURVKMsB2Bd8fOP8301w48b/8phKbs9/HsAu7563ste7RMcX\n6j4pqCfYGu+NPrNx3m++2PC5pY4vNUffqR95vsC6MKdLBBDPdJ/qoWrVtzfPf2YV/Rc/rgBA\noQKQea0m9NiteU+kqvEaz8hMlI8VRnKvaV2533soLiRLg4XWocSIV6BxjcijUq81+CYAfZl1\nQ8tdgWjDytJf6tYkncGNep2Y1E+p1hfZl+mYOW48T9Wsum3G26kcrz5bdXvwHQBW/riAHozV\nJ5TxeesZsAcHnvY7Zup96hbH11c9pe/SOxl9bJUZtwVih7rDu7Pti1OXBqK1GlU0NRqIH/TZ\npgAo9VwLoDHwIoBs57xTvcwLwvkp1FNxxvcwGjnhNISdgYHB5YUh7C472PZWJJP6ZyKJTE8X\nf/QQdbmlskmmPTv52j3yrLnMG69Y3nxVqp5O7Q4mHOYbasBy0qy5OBYtwbU2af4MzeZgA/3c\n0YPypEr+8IFUtISal891tvP7dstV04iicO2tsGH6QGVGzKdvxyMAWFaaPc/EnKDMwqbIxrwd\nq9uWJG7+lPXZJ8deIprmenfbdUVf3Bl7Uh54J601fyCTPky/8nH19xY2/YxPHeD63CA+0ddt\n7mQUVeP05BdYO+kPAF7v/4/nyZtXD358VW8WFHnIEqwrH4x4TXn7FSJJ+lRjmrU8JvUNxuo7\ngu/Kakyjyvb2n5b5rjs6+AI9Oyvmp2pWzcj5gv7ZIvj6ozUW3tseXO+zVdlMmYOxA4cH/p6q\nnFSC+ofDA8+O7eTo4AsAXj10BwW1Cf4C9xUV/k+wzKi78saW7w1G6wF0h7cD2NL8k4K0lbNy\nv9ozsvNg/5PhZBshHEeE3pHdurDTZ9F8tmoA2Y73V9jpIvJCMc7q+WR0YWfM2BkYGFxuGMLu\nsoOv3ZOaEKMms+Z0SguXSTPmQBD41mb+6CGlsETPEivU7CaSRC0WJb9Irp5OeQEACNHDHYTt\nm8EyakZmcuVaMAzb15OKllCKy0k0yrc08kcPUauNTrYAKAkVVAyXHIuNADgu5rpt3NjK7Vfu\nyXq7dO53M09SSuKiZTgwWFoXLlGvDwsjXSVMY4mI0Nk+9da0jWZHhTXBwkSJouGYsNMps1+x\nU/pfe9FgqX2VqEU2Df5iSG7mYctklkOWIACAz1rVFdpkEzJ2dT6kQiaULiu5nwHXMvy6clKu\niMFYw4H+x4dih1UtDmBz64/08iLPmsbBl2NyX9vwWzxrS7OUKFpyV+dDFJqqiU5TnsXiEZVw\nXB482P9EXc+fksqwpikAhuNHMh0zXz/6b6F4IwBFS7LgMh2z6vseCSVbZC0+FDusakmGsKom\nEZCp2Z/f2f4zSY21Dr/REdqQbi0zcS5KKYEm03h7cP3UrDtSox2I1qZZSy2CF0DPyI5DA88E\n4426YfI44Siq4QN9j3WHdySVYY4xuyzFk323ZDnn6FcZsKIa3tf1+96R3aqWdFmKZ+d+I91a\npl8duxSbcnKu6fmfgUgdparbUjIj51/Tx0zpHRl8rnHwpbg8YOUzJvlvtPDeLa0/Wlr8k2zn\nvA0td/WN7AHw0oHbWMLfPO21E9950sJ7GPAAfMeE3YsNt4QSrTdOfXZzy91doa0aVb22qqXF\n92U4Zpztd8jAwMDgo4Ah7C4jpFnzpFmnm5VJrr7G+vdHTTu3JK75+MRJYAEAmjs9eeU14woT\n144JISBEnjZLnjZLP1ODjyMAcekVeKFNL4ndfuf4sU2dicD6qDpAQZ/q/JSDzyxas3yBZ1Iq\nkdnr8m/jJcNXL/rpq73/PqL0AAxCGoAXer7MgL154V9ZMqpXRS1SE3isRdsMYCMeTx/cXum4\ndknO96UcxAKtJNkZF5J7h//Yk6hVtCTDCJSqBHDx2QdGXtobemxd9h9XZ9wL4K2Be14qf7M8\nUt6T1hqIHWgc+geAcLLtirL/OtD/eCDaYBeyjww8l1qAbhp+tdR73e7OX9V0/z6cbAeImXP7\nHHOiUv9Isk2f9nuhft20rDvreh82cc4819Lm4VetfMaK0p9vavmhqokjYufs3G8EYgfagm+F\nEq0mzlmQtqp3ZFdCDtT3/tVnr85xzE1Kg0kl5LIURZM9zUOvsYTrDG1Kt0yam/dNM5e2t+u3\noWQrAJ+tOjdt4dGBl0GIqolDsUNU/9FZSoKJ5rjUF5cDVt4LgFJV0ZL6dF3PyM7NLT/02qvn\n53+XY8w9kR31fY+Iamhmzlf0Z9zW+uPhxNGpWXc4zQWyEmsZ/uem1h+sLH1In/9jCL+5+W6f\nfcrc/O9EpZ4DvY9ubv2PayufYMj4/8+whJfUka1t91X4b5md+/Wo2Le9/cebW+/5WOWj+s+x\nMfBiTfd/Zzpmzsz9skqlht6/CaxDvwWAOTlf38/+tT34zvKSB8xcOoD+aM2GprvSreX6exiO\nN9b2/hEgbmu+IgEAx5hEJfjPQ5+flfvVFaUPhhPtrx/5wiuHPve5ObtZIpzqq25gYGDwkcPI\nPGFwHDWvQK6axoSCwoEzrHOdH/Hb79SPky+F5HYAspac7v4kBfUI5YeCz77QsE7P2QCAZQSZ\nxtcP/DTXOocAgGbjfAByLbM1qDXh49vmtgR+0xzZkG2vBlBqX8WC3zj480HxiH6VAbdp8BdW\nJr3MfqUGTdVElgiL0r+ZYa6OqQGVSl3JfceHRZgWW4tDyPbaqlJlSWVY1SRQerD/yZqe/2YZ\ngYABEIwfPdz/NICo2MMypmXFP851L+4Z2TmSbBv7pM3D/9Q/lHivolSTlPCezt96rJMBsIRX\nNbEt+BaAdOukufnfScoBWY0CKEi7ghBSnXWHx1YJgGPMCwq+t6TwXgoGgNtamOde5rNPtZuy\nAFDQztCGXPdCALp/Sqnn2kneGwCMJDoAUKB1+A19GLoVS7ZrPoBIsstnr15ceHeue3Gmc/bM\nnK94rBWtw6OJVlVNGojV5rmWlnlvyLDPyHUvXlx0X1XGp1MbB2Utnpe2dFr2nbmuhRW+m8p8\nNyTkoWCiccIvg6RGyn3r8t3LzFya1za52HN1Qh4cdXIGDg08Y+HTlxT/JNs5L8+15IqyX40k\nO1JtbaYs3erZZS5wW4oB1HY/zLHm5SX36++h3PdxAgagB3ueH/ODC03P/mKZ9zor78tyzq7K\n/HRU7BmKHZ5weAYGBgYfUQxhZ3AC4vLV1Grj62uYkQucjTSuBUeUnnFHQh1dTG2ObQKwNvM/\nl/v+vTRY0BXZOrdner810P769/QKhJK4Eqh0XusTyrMs0yysO64MA5iTdodHKGmNbdarqVQe\nkA4U2BZmWqoAeIXSpd5vVzk/jpT4oIk867zp7k+aGIffVDHJsUbSombOPtv9uXS+CAQd8e2p\nMWssjQjRYu6Eac6trfcNRvcrVNzf+xcAkhKh0ADMzvlGXAnqY8hyzslyzgsn2jjGjFGbP1AK\nShEVu/V+0izlPGuTtXhR+mrd2cRnn9oYeIln7QAiYhfV1EWF9+qVC9NXe61VqiaGEi0ARpId\nW9v+c2Pr9zUqAegMbUkNT7cA7IvUZjpn6aGvAKqyPhMRe9zmwsrMT+ol/ZFR/UqpauLc6ZYK\nAJP861aWPqRrJh27KVdWo7IWB8AwvJlL7w5v6wpt0agCgCHclMzbvdbjqlfPmXasbTaApHLK\n9fIs5+zUZ336MKkMAZDUSFwayHDMSs3CCqzjNMk2JCUSTBzNcMzQXx2AiNSjj7Bl8N2xNQvT\njw/PYcoGEJP6YGBgYHAJYSzFGpwAtViSK660vPqCsGNLcvXVKT303nln4CcnF05xfnx1xj1J\nNRxV+gAIjB3A2tZlf5z+ZLuzG0Cbs9u/J4nC0frl9tU2zjcz7dP14efeHhj15nBwWUNSs0wT\nPLEwhDMz7o7YLoYZHTlDuGrXTWNvqmeDrXBcVeG4qiW2AUBCGwHg5HOG5dbeZK3eFQAFKgEp\niZ0Qyzk1+/MdoY2RRLtKZZ99iqwlw/EWCk2lSZWO+qVZuHRRCQXiDVSjICB6Lg2CcVEWNt4f\nUlvtpuyu0FYAfvu0vsher60yEDvIs7YtbfdwjFmjFICsJQBsbbtPFyJz877hMBVEpZ4trfcA\nkNWYrMVTySoAJJQApUoqqoMB3x+tLfZc5XfMRN/fAAzHGymlGpUptGznXH3W7fSGyQRkSdGP\nd3Tcrw/MY6vKcs4pTl+jL5ICIIQxc2mpMbCEw6kzy53GyTkph/R3OLa+y1IwYT/6wwKwjgmP\nHUm2H/vQPfYW5jF9Euh3VE7VrYGBgcFHEUPYGYxHqaxWDjVwLY1cS5NSUnahup2TdkeWZeq4\nQgeXAyCqDqRKrI8+DDiXds5bX7AVQMQU9cc9+iUCYuE8ChVrQo8fjbxFQCjoiz2j279AKQgI\nyDLvd7YH/3Ak/BaA+pHno2qgxLZcYI7lMQNjZlwAFCodib7WHF0PYPPgLwlGs16oVOmK7y6y\nLQWgQc6K+p1JOTlmzD5b9UC0LoJOAGmW8iODz7NgqS5Hjrn3NQZebgy8rA869U+C8V5+VsEX\nSrb2R/bq65Vm3gtAn3aal//vLDH1RXYf7P87gF0dP+MKftQzstMmZMakvgN9T8WVQUmJHe/r\nRP1EQOLSUGo8z9VfB+Do4PNHB0eXJhUtHkq2SGoEQLZzvl64te2+cYbJDb1/6QpvS3Wbbi27\nquLPQ7HDfZHdvZE9dd1/PNT/5PKSB9MsJbhw6NOQZLzx4Sn/xqCjl46r5pSwO6kTAwMDg0sc\nYynWYAKSq66iPG/au5Mkx8d7njcZpqoS28pxh9+kT4Yd/+2r78Cb2zs9I+4FoGFMti4QBsyr\nvd/eEvhNlmVannUOADPjzracENiYLhTdkPfrKenXA1CpXBt64h+93wjK7ePGs3Xov+pCTzu4\nTAAz3J9em/lAjmUWABPr6EjsACBrcQpaFp3EDA2CTmxocnTg+ZMLCYjfVr2w4D8A+G3Vc/K+\nPTPnywByXAtXlf223HsjAEAFwBAOIL2R3XpWU3JiJ17b5CmZt9sEHwBFTR4NPAcgIQ8DCCVb\nJCWCMS9nIFo7dgzWE93ditLXMAy/suShpUXH503fOvplSY0QINM5G0BCCuiGySPJji2t97gt\nxW5zoXxSwG9qYKvLfrO6/PeSGj3Y/wQuKALnxElruBGx81T1bbyPgMSl438epISdy5J3Ycdm\nYGBg8CHHEHYGE0BdbmnBUohJYe+uC9WnaePb1kcfnvCSg8scNx/DULKkcy6AEVNUnbMkVR5V\nBlpim4vty1f6vucRygAktGBY7h7XIQFx8H4A0123XJnxY1mLN4y8MLZCXB3uTtTkWGbpM3N2\nzufm82QtCSDPMrc3uV/S4kk1ApBcbjpEkYlGTrrF8X/pC4hmIV2fH3Ka84cTTV57FSEMw5hK\nPFepmgh9n5xtsk3IACCpMb2QACPJTkplHJurS0gBHFteBKBqEgCetaqaaBMy9NmsLNecPPcy\nM5cmsKMzkVta7+0Mb04NL9M5x2byM8f22MXkfr99mt8xNWU+wjImCiqpMRBWUiJP166KK/0A\nrLw/L21pmedaAMPxI7oDs0Y1AMF40/a2n0al47kx0q3lJtYlKhd4O6aF95o4V09kl24rDUDW\n4h3HXKB19LVj/c3zrM1jreyL1IjKiH41LLbra7ulGWsu7NgMDAwMPuQYws5gYqQ5C1R/JtfS\nyPZ2vceuhD07Up+tjz58srwzMXYnlw0gqY7g2KTdsDmk/7M3WZeqqUIG4GCzcOxXu4mxR5V+\nHJvbC0pt24Z+G5GP74j3CMUC49DXHFNoUADYWG+qZFhqHhAPAci3zNeo2pOokWgUhFKnCwAT\nGBz/VCftPiRgdDOOXPcSjcpbWn/kNOX1Rfa9fviLDf1/89un5joXhRItHaGNACildb1/UjUJ\nhHCMhWUsAEyc08r7IlI3gNruh5uG/tEzsjOpDAOQ1GiJ56o5ed/Q79U/UhOX+peX/qzSPxoM\nQUEbev8WEbspKCFMrnMRx5j1mAwAQ7FDWY45AFKxpTxjpVRVNZEhrG72a+F8diG7PbheYOw5\n7kWHB5/VHZgBtAXfiksDVpOvN7J7Q9P3mob+0RfZ0x3etr39p0klWOK5avzLeW8QkDLv9Ul5\neHPLj3pGdnaGNr599OspSapj4TwAjgw+2xnaJGvx6Tlf0Ki8seWuztDG/si+cKJNg5brXjg5\n64YLOzYDAwODDznGHjuDU8Aw4pqPWZ/4i2n7lsR1N1Hu/f2qFNuW1YaffLP/h3PT77Swrr5r\n2e3De3OEGSNK7+HIP1PVnGyWm887Enk1xzJdn61RMbr5fX/4mebo+nLH2p5k3VBvq9dSDKAx\nuv5A5OWkFs7mph6NvhFTBvSNdDbGZ+cy2uJb9I1ZXYk9g+KRcsfqI5HXQ3KHnfXvH3lW31a/\nz7ppGucShw6LQnD8oOkJ84w72x+wCzlhsbVp6JVJvnX9kZqI2ANooWRzhn3mJP+6xsDLhwae\nZBjTjVNeOBp4vnX4zbg0QEGnZH/WJmRsbb1Xo3K578banj+m2yazYPb3/FlSY7qXSlXGZwrS\nVlFKTaxDVCMUWkTs7gxt6Qiuz3cv08XiiNhm47NtQpaoBAlhukLbJC2mB2yomjgYqQOlhwef\nMfFuUQ7xnFVPbpFuLd/R8TMALx/8JEs4j61qe8f9lNJc96JlJffv6vglQ/i63j81BV5OyEOE\nsIDW0PeYqIRBQaGZuTRZPSHJ7xij4ASA5qF/+mzVJs6pX323+bsxsc8qeEC1/6u7SvcWHo43\nHhl8DsDWtvtsfEZ+2vLJ/ls1TW4Nvrm17V6HKbcq41OyFu8Z2ZWyVinyrOkKbW0KvNIe3LB2\n0v96bVVXlD5U3/fors6HVC2pUTXNUrZu2rP6vJ2BgYHB5YMh7AxOiZqZLU2fLezbxdXXyDPm\nvK/3cvE5AEyM/e3+exWadHBZM12fnu/5Qnt858s9X0tVI4S5NvtX7w488NbAvQzhLKxL0qIM\nOA1KU+wdCo0h3JUZ/3kw8kJXdC+APrFeb9gS29wS24xUMAMhS73f3ht8pDn+LoARpXuF7y4G\nbF+yoS78dIapsidZZ+cyiqyLW2Ibj04etmoOE++msqJookZlAIqapDim7QgoEEq0LC66Z0vb\nPQ4+u3n4n7IaM3NpPlu1osYCicObW39o5tJyXUuqMj4lcI4pmZ+dkvnZLa13d4/sqPDdBODW\n6W8D8Nuna1RuGnolKQ9beO9k/60OU+6Wtns8tsn6sIs8Vx0eeIYhHAjCyRY9+0Uw2RJJdrpM\nRWsrHt7QcpcYDfaM7Nzc8h9+57TpWV/sDG8eTjZ1jWzri+4rSl9TlfEpM5++oeWuhDR009RX\nYmLv/r7jZr9uS7GeImJx4T0ABNbGMia3uXBK1mddpoKmoVca+h7NcMwioDNyviSwtn1df9jT\n9RuPrWpx0X2YyCi4vu+Rd5u/c2X571eUPghgc+vdCk3IanJq9p1W3gNgOH5kfeM3nOb8RYU/\nNHNpg7H6A/1PBuIHV5Q8ODX786kf/YH+JwCkIn9NrGtV+a/HfoU8tsrlJQ8A6A5v29x6d7nv\nBo5J+VvjY5WPjvvKVfhvrvDf/B6+swYGBgYfRgxhZ3A6pCUr+aYjwoH9akGJln7mlKwTMrd3\n2tzeaaevMzPt0zPTPn1yeYlt2TfL6saWeIWym3P/PLYkIDU+0X6LlfVek/mg7ny2NOObgklI\nxOOqqp7qjm4+7wr/D8cVXpP54LiSatdNpo3rSTIRv/WzYEa3LrzT+J3jNY55ilAg171Y12dn\niS6GxkJAKjM+WZnxybGFY/vUlyBn5X4tx7UAgKbKEanbzLpELrSg8PsAlhc/AODIwHN+x9Tl\npfcRzTop42YAbx39yojYNSv3q+PuONbs13JiyIWOrEYnZ9yWYZ8BoDLjkwf7nxyM1l1b+aSF\nTwcwOeO2zvDmgWitHhibMgrW9wv67FMJIfu6/9AZ2lCQtgoAAZLycKX/tnLfx/X+a7r/h2Ot\ny0se1Gf1fPapLGOu6f7vt49+ZXnJz/ljmwj7I3tZwjvNpzQ9SREWOwB4bJPOWNPAwMDg0sMQ\ndgangwpCcvU1lueeFHZuTq697gLa2l1AvEJZtfvmutDTByMvVzvXnbnBOaK53WxXhAmHtLRR\naXvbjHNQb+8Huzp+MfbUyvvm59/lMhelSib511Xn3kYIEcVRaz27KXcofnic3d1Z4reP+tQw\nhBM4p8DadFWHY25zSXkYx4yCc92LU0bBAHJcC/d1/6EvUqsLO52U27CsxQPxhnzXCo4x62Ei\nALKc82q6/3s4fmRj8/fL/TcyhO8MbRiI7q/wf0I3fD49ekhsusUQdgYGBpcjhrAzOANKcalc\nPpk/eog/ekieVHkePejBEHrMxIT5xN47iz1fa4q+fWjk5SLrEjvnv7CdU1c6ujqZwGBK2F10\npmR+xmurBkBAk2p4IFq/pfVHua5FCwp+oGebUDWxofeZ9uGNEbHnZKvhc4Il/Ng1TUIYgXWO\nPQWg59442SgYgIX3YkyQLwACkvIxTkhDlNL20DvtoXfG3ddrr6aatrvzIVUTHaac6dlfnOS7\nCWdBONHOEM5tLT6XpzQwMDC4RDCEncGZEVddxXW08rW7lbwCarWdXyfvk6TTERjbYu/X3ui7\ne3fwLyt8d13YzlWXmwOYoQGUfRCTQE/VrDrjjKDLXPRu07/j2NxhgXul21ywt+t3GfbpJd5r\nccxquCLjxqlZ/zKh1fD7wclGwTi2Uq0bwTxVsyrPvRCEpHKd6eS6Flb4bxvXm4mzO0zn7EJH\nKY2IHW5LMUuEc21rYGBgcAlg2J0YnBlqs4uLVxBJNu3YcubaF4nJjutyLbN7k3VdyX1nrPz6\nkS+dfc/U6QTDMIHBp2pWnbn2e+O8b+G1VQIIxI/gmNVwXtqieYXfzLDPOJXV8AXnZKNgAAl5\nAICV9034aFbBRwijaJLXNnnccR6qDkBc6lO0ZLql/PwewcDAwOCjjiHsDM4KefpsNSeP7e7k\nOtsu9lgmhoCs8H+PAbsn+FflWM7WCTknVQcALKs5nEw4xNEPyDvjjPJuS+t942r2juwGYBP8\nOObSZxUyUvXHWQ2PY6zZ73vhZKNgAJ3BzQAynaNR1eNmDTnG7LNV90dq9By4OqFEy+7OX0Wl\n8b7TZ0NYbAfgsVWcR1sDAwODSwBD2BmcHYQkV18DlhV2biWSdLFHMzFeoXSa+5a4EmgIvnjG\nyuc2aedyQ1XTE+73ddLuHDonussKCHBk8Lltbf9Z3/eITcgo814HwMpn2IXstsBbrUPrh+KH\nTrYaHtfZOLPf9/IIJxgFR2sO9T/d0P+o3z51S8u9qTr0RHE5PfuLDMOtb/pWU+CVvsi+o4Mv\nbmy5qy+6z8S6z2MAYT1ywmrM2BkYGFymGHvsDM4WzecX5yww7djC1+6V5i64gD3vCz6+MfDg\nxzJ/UeZYPWGFZ7u+MCAe+FLJ1jN2FZCaCUh96Plyz0oT0k6ucBo9dzjy2r7QeLcznljcfF6J\ne8rkDs6X8AxYh85mD9xp2NByVyDacNPUV8aVj1N1p78LwXFv5Jru/3Zbiqsybi/zXWdiXQAI\nIUuK763p/sO21p8xhPPbp+l2d/2Rmv29f6ZUrfB/Ymxv48x+z/vRAJxoFCxaef8k77qqzE8/\nU3fNqZqkW8tXlf76QP+j+/v+rJv/6W5/KaOTc0IPiTVm7AwMDC5bDGFncA7IC5fxRw/zRw8q\nRcWaL+PMDS4QZY5VWeYq/fOI3PvntrV3Fr1p5zJOPmUIy4BXqbSt748rvOOjKMaputePfGnt\npD/oes7GeUvtVwIota/y8KMBlb3J/R2JHQkttAPPh3JKMmI+eM5t5BR0b+dv2oPrVSotK/5J\nhmPWGRoQkGPGeBNKwH3d/31yo5PVmMtcdOXkX4+1OwFwVcWfUp91uzudcWa/upOwzslOe9dX\nPj321Mynj7PuSxkFpxirWfXY3HGyNc1aevKNzo9wso0hnNtSckF6MzAwMPjIYSzFGpwDlGWT\nV14DwLRjK7RzNs44b6a5PrHI+3X9c3di79hL404BMITNt83tiu7rjO8+Y88pqRdTAr2JGgBZ\npikl9hX64TWVA2AiSU5jD3uarcqo68fZr5kGovVNQ//w2CsXFd491mduHBN22Duy5yzv8gFE\ndXwkoKAjyU6XudAIiTUwMLhsMWbsDM4NNa9AqZrKNdTxh+rlqjPkk7hQpJZin+/5t/bYNgAP\nt17JESHHOnvs6VdLR5Xcmuy7/9J0w+7hvy5Mtx+MvDwoHlFoEppm4gWzYiZ0ApvlfOv8zvjO\nCe9OAF7lFUYFoSZVEFkJJ2RETVp4T7ZzwZTM21MZUQEEYocO9D+qhyzEpAGOMZn5CWzwQsm2\n9Y3f8NorV5T8nGPMY2fp9M/j6l90b+Tz4AMbc1zqV7SEkXPCwMDgcsYQdgbnTGL5antLE1+3\nT80v1ByuD/LWV/h+sI35/eHIP2/M+R8r6xEY69jTVDWvuWxuxme39/15/cC9Lj5vbvrnzYxr\nUDx8Jf8pAAAgAElEQVR8YORFpyNnpe8HBMe13eHIawCqnet6E7UyTepeu+NQGBWAXbQ7JLto\nHSbA+sZvMITPsE8v814XEXvq+x4ZjNWbOFdM7F9QeNfW1v+MywM8Y1M1EUAk2fFu83enZH5m\nSuZnJTWqUum5+htULWnm3KIatQuZy4t/esacCroEdJrzdQl4Qd7nJUbYyDlhYGBw2WMsxRqc\nOxZrcvlqoqrC9i2je6Y+KFx8roVNA+ARin2m8nGnY2uuyP42S3gN2pz0z+db5vtNk6ucH5/q\nuqU/eaAnUXNyzxwx51rmAmhP7EyqIf3QQ0RdnkqZlfNJdVSIDltDXutkm5DFMeYpmbcPxhr2\n9z1S5rt+atYdoUSzpEYUmtjT+VvCsCwjXFH+q+nZXwBQnXkHz9qH4of6ozXDsSOgmJv3zcWF\n96hUUakIgD2TUItLAxtb7rIJGWcjAS84H5Wl3hEjJNbAwOCyxxB2BueDUjVVLSxh+3u51uaL\nPZZTQKhu57Y/9H8qlfUj2zIDwKB4eML6aUIhgM74zud7/k0/6keeBdCbqCt3rFng/tKurDqe\n8gsLfhCTejOdsyszbtP1XGdoQ45rIQBJDifl4fy05XGxL9sxz20utgmZABym7HXVLy4rfqC2\n+2GGMCzDZzvnHxx4gmNM1ZmfCSfbOkMbTvMokhLZ0PJdjlhWlPxsbBpWg3GMJDtghMQaGBhc\n3hjCzuA8Sa75GOV5054dEJMXeywTMCL2U0oB9In1f++6XT9e6f0WgLg2fJqGDNh0oWiF764V\nvrt8wmQAlc7rZrs/p1rYIUswO56p0ASOZUTV9VxfpFbPiKpSCYDfXkVBT95RJymRYOKowDkp\nsLXtvkDsUKn3usK0K/UeTjUeVRM3tX5f1aSVpT83cedj7fYe0afrPhKTduFkG0NYIyTWwMDg\ncsbYY2dwnmhOl7RgiWnTO6a9u8SFSy/2cCYm3zq3K7FXIPZF3q+x4PVC4bQGaQXWha3xzUl1\npMi2JCh1DkqHDo68UmpfldBCAGwJE5K6kKUYk+H+mEUJCIiJcQPQTtqrl1ACAFjCJzUxmGxO\ns5Q39D2a4ZidunQyFFSXgNOy77TwvvN9DZcFFHQk2eE0FxgbEA0MDC5njBk7g/NHmrNQ9Wdy\nzUfZ3vPJ/vS+4jJlMYQlYGe4P5XUwn3Jeq+pTD+cXPZpGuZYZqYJhftCj4lalGVYADKN1QQf\n169SQu0RJpURNZXhXs+IyjImEGI1ZRCQhNQ/ttukEkzIwwAowBBuTdkflhTfyzLC9vb/xGgi\niQlQx0jAULLtPb+Vc2bsRN2HfNIuIQ3KWtxjNSInDAwMLmsMYWfwHmAYcc3HwDDCzm1Efa+Z\nRs+S0cSmUCc8TSEw1hzLzM74rkrndXbOfyjySljuCUodu4b/FFX6cGoIyNy0O2Utti/0GAMO\ngN80uSW+MaYECEiMj5uHoqmMqKkM93pGVDPnBsAQzmOr6I/UJOQhvU8N6htH/rWu52EComkS\nQzgzn27lffPzvxtJduHYwu7JjJWA29ruVbSLvOr9YdZ2x5KJGcLOwMDgssYQdgbvCTUzW54+\nm4mEufoJQk3Pibb41rrwU2OPhpEXTq5mY/0A9gYfbYy8JWmxcadjay73f4ch3Is9Xy2wLtKo\nsjnwyw2B+/vEeoEZdZvrTx54uvNTJwfJeoTiMvua1timQfEogCrnDSzha0KPe7jibnu/PNye\nyoja0PsoAEUT9YyoZm7UcmVq1p0U9J2m/38gWgvgUP9TSXm4OusOj7VSVEdS67bZzvk+2xQA\nhJl4U8RYCTiS7NzT9esJq71PfJhl3MmEk20whJ2BgcFljyHsDN4r4pKV1OEUGuqY4NB76adh\n5IV3Bh4Ye2wJ/NfJ1aqc12ebp9eH/2/94E9lLTHudGzNTEvVLbmP+oWK5tg7BGRE6UnjC670\n3yswVr0ChaZBS8mssUxzfcLKetrjWwFYWW+F42MjSo/LlK8R7W3b3xPS4Iycf5XVeFtoPSEk\nEDs0ybtuWfH95NiCqt8+dUXpL6y8t3XoDQCUastK7s92zpue8wVKqUblztDG/mjNof6nhxNH\nedbaOvRGONl6mpeT7Zw/ybeubfitluHXzuflnjunUnUfWrU3GhJrCDsDA4PLGyN4wuC9QgUh\neeU1lueeMu3cklhzHcjE28VOw8y0T89M+/RpKtyUezwXqoV135L3t7FXx57emH1CKlW/ueK6\n7P8CMCS1PNFxc1Du5MjxnfWZ5upP5j11OPJav9gAoMJxVYXjKv0Sx5hnp/9/mwZHs6ZOc31i\nmusTACZ1+eu4jbs7H1KoZOX9lb7bqjI/zTKmk8fss01ZUfpgZ2jT1rb7pmTenumYDcBrq1pd\n9uv6vkd3dT6kaqKV90/yrjtVD+OYln1nIHZgb9fvPNaK02Qnu1B85FJcjIhthDBp1tKLPRAD\nAwODiwmhH6zB7IUlHA7Lsjy2xO12cxwXCEwcY3iZ4HQ64/G4oigf5E0tLz3DHT0szl2oTKr8\nIO87IWaz2Ww2R6PRsS9hc+BXe4KPVDmvn+a69bx75pobucMHpKVXKIXFF2Kk7yOCIBBCRFG8\n2AP5gHiu/nor7/vcnOMJgjmOc7vdiUQiFoudpuGlDcuydrs9HA5f7IFcTDwej6ZpwWDwYg/k\nYpKWlhYKhT6iv/G9Xu/FHsJHCWMp1uDCkFx1NTWbhdrdJB6/2GOZmAWef3PyWYcir47I5x/D\nq7ncAJihwQs3LoMLQEIelNWYYU1sYGBgYAg7gwsDtdnFRcuJJAt7tl/ssUwMR8yLPd/UqLI7\n+Mh5d6K53SCECQxcuHEZXAD0kFhjg52BgYGBIewMLhjyjDlqdi7X3sp1tV/ssUzMJMeaQtui\nfrGhI7EjVfj6kS8F5fYnO29LHaIWOWUXHE+tNmYoAG28//BYNrTc9ez+j13Akb933m36znP1\n1+ufxw7vQzjU80AXdmkWI0usgYHB5Y4RPGFw4SAkeeXHbI89LOzcqmZkUV54P26yL/j4xsCD\n4woFxuYVyiqd101xfpyQ0T9X/t7++f5kw5dKto6tucJ712PxdXuDf8syT+OJBUDUFH29767Z\naZ8D0Bx7Jyh1nH4AmjuN7e5kwiEtbXzSsLEo6oX0nGsZ+ueuzoeWl/w80zHz/HrIS1t6CU9o\njegzdrZL9gENDAwMzhJD2BlcSDSfX5qzQNixha/dK81Z8P7daKrr5gzTaJSGBi0sdx0cefHt\ngfveGbyfAbHx3izrlHRTQabpeCTHyz3faI1t/HpZzUz3p3cF/9IQfmGG+/+xd5/xcVXXosDX\nPnWapqr3Ztly7wX3BsaUQJJLICQQcgMkpFxCyoUkLwXywiXJSy6BG7iEFlNMQujNFNOMjY1l\nybItS7JsSaNeRiNNn1P3+zBClmXZlmxJo7L+P3+YOdpnnzVHlry8z157f3VH9W0gAgDUNr+y\nZfpf26JHuuEciR212aG5kfF6dIfzw9o7PcEjX577+oA2rf6SYdcGj7JC1xXxDmEU+SJuQhiH\naVq8A0EIoTjDxA6NMPmitdyxSr76qJqbryeljNJVso3LpiVsjr3ulKr3eR8Ja908MSo0stjx\n7yoTOeZ7J6h6ihMu00FjgO1/7jLXrVWBHVWBN/PMq89+leZIaWXgda9SB1Q3c4k5xpXF1iuI\n3cEBMJ5OKCiStZBG5RcOfwGA2I35s1OuT0lY1Hf69rJNsUVDeqL1O2tutwhp3ZGaS2c8cqLr\nzWbf3ojapetKonnWrJSvpVmX9H6W0JGK9qe7QlWaHjXyrnTritmpN4hc74rKh1ofK2m8f33h\nfWUtD3cEyinV7MaCBRnfjq3Ku7Pmh97Isatn/4tjjH0xHGp9/Gj7s5um3X+49Ulv5NiX5rxy\n9o/sDddUtD/VGTqsahETn5ztWDcz5avjf/dVv9SQIGTyn69QiBBCUxbOsUMjjLJs9OLLAEDc\nu/vsE9FGhKQHX2n5QVTrvjL9z/Pt1wJAtnnZJem/+uH8T02sszLwxoHubbGWUc1Pgf71xMpH\najdxjJGC9k7zz/pK/ymh/6q/MbYLxfudv/PKdc2Rso89fwSABC6VJ2a7kHfI//zzTTc9H/rp\nrqz9endLRfsz3lAlpTpDuCzbKgLwYe2dLf59vUv4EiAE/lm+5dWKa9899j0Tn1jougwA9jf+\npcbzmqpHCpxbAMAbqvqo9q4dVbd4w9XtwbL3a36kqKGlWT8scF2u6JEaz8uvVFxT0f5MbBVl\nhnCy5t9df3e2ff3W4sfXFtwXVjp21f1aowoA5Dk3a7rU7D+leMXd80GCmJFonjWUm+kNV++s\n+UFYbl+Sefv6gt/nuy6p7nzh47pfDLqG8/gRUbpkLYAlsQghBBN9xI7neY475SMwDAMARqPx\nDGdMCSzLiqLI83zcIigqpnMWMIdKjTWV+tzznBN2JrHvOM/zoigCQKnn7wG1bW3qHTOdlx5x\nvwgAieZ8juM4wiUap7WEyvKsS0VRbA6XtURLAWBNyn8c9b3VGikHAJVVo3wUAICSgBDkdc5g\nSPQrLd1y/Y72n4us1cwlrkn+4Wddj4bUzqjm5YioUSXHsqKGvlcDjwidZgIMy/Cp1vm13h1b\nZz38btWPP679ef8VmnVd1UHTqBRS2vc3/TcAeEJHCBBFC9V4XgMAHTQCjKT1fFL/a441EkIC\nctOe+t9R0BIM6dnONcc7Xm/27aKgAwBDiKwFZqXdnp94MQDYzKlF0SvKGh+NaC0uc1FBysUH\nmh9s9n1clHJp7EZ1BitCUuuCrJtFUWQYhgDEblrsx+T01wdPPMJz5ktm3i9yNgDIdC0RBct+\n9wOeSGmm/aKR/T6OIG+0BQBSrLMG/ODHPhrHcVP5FwIhhGGYqXwHYgghU/wmxO7ABF3HDg0L\njtihUaFdchmYzEzpfuIflZVRo7o/pHhCiqeq502GsOmGOW+3/Ko2sKvIutnOZ8baMMCyjJhh\nWggAH7b/iQDLM8ajvjezTAuvyPwjoQQAInyEAlBCBY1ndS6gtABAlnkxAE0zzgqqnRX+lwFA\n1kOz7V8UWAsFvUs+ToAwAIoaMvB2ADI77WsAcMKzQ9UjJ8e2Yi8IRJXu+Rk3qVokKWE2AIic\nlRBWp2qiZTYAbJ7x/y6f8/iM1C+G5U5/pIFjDCvyfmwUHBxrishdvkg9ADhNpxR7ZtiX9b02\nC8kAEFE8ACCw5iz7yuaefYrWux7viY53CWEKErcM5ZYqWqgzeDjNuphjjJoux/5k2FcAQLu/\nfPjforHTE6kDAJcFR+wQQmiCj9gpijJg54nYyEQkEjnTKVMBz/OSJI3xzhOnIdzaTca3XoGP\nd0qbtp7HPmNnEvtcbzX/n/4Ht9ffZGCs8+3XrXH9UJIkQgjHcbquA6WSJCl6uClUYmYTg1pn\noWnjQsfXAeD2ooP/aLqxJXKQFxIUzbsl78/1oT0V/pckPZAszGoMlWQalnZEq6p8O2Jlth+0\n3RsbNss2rAhLXYJM/UKQEA4AOJoAAO6ujynVKEDvR+33ibOsGw7C45ISBIDl2Xfuqb9Hp6o3\nWAUALT1l05O+JBBHb0v7Wqc4Kyx7sh3rXKbisuaHACAod6QlrOgO1+qUEsKAZpC03v0kNFUH\nAEmOxnaYyLJvqPd+UNvxQa5zs65rtZ73UizzOWqTJEnXdQoQa6brOpz22h9tpVSv63qvrmvg\nZmKBaNt43sGiK3gCABK4vAE/+LGxOlVVp/IvBJZleZ6fyncAAEwmE6V0it8Eg8EQiUQm6Iid\n2WyOdwgTycRO7NB4ps6ep1UeZutr2foTWt4I7+C51HlzhmEBBfpyy3dNbGJY86xJ+tEs61WD\nNg5rHgqUZQTQYIZ1a9/x4oQrWiIHw1o3AcbA2GYkXNohHW2KlMSKLSoDr4ZVLwC4+HyvXL8l\n9XcfdP4uovVkGhcfC7zFawQAYpPPYplfVPVm2lfNSb3xraqbzUJySO4w8I6o0s0QVtJCAKDq\nEQCwG/OSExa0+PY6zNO6QpVH25457nm1MOnKWEiEsEGpHQBE1pZmXRZL7AgQkzCkHXXSEpaI\nnK2h58Nc5+b2QFlE6ZqXfsuwbmym7aIZydcNOChylmF1Msb8UTcBgiWxCCEE+CgWjaro5q2U\n4wwln4I0kou6AUCyMD3XvDLPvIonRo4x2vmsXZ4/R7SeMzRnAIBSYIAxsa6+o5/vLUYBdApU\npXKP0gAApb5nAaBLqo01E5kEQoidz9KpDgAiawXCsMASAF2X+19GYK0RxRvrkSHcxdP+CgAU\nyKfue6DfEB4BAAIuUzEArMn/rawFm3t6F9s77nn13ZrvAkCN55U3K78RO2jikyjVhnJbGMLl\nONa3BQ4oWqiu6z2eNWfZVg3lRAAwCUmEMKouJ5qLB/xJELOG2Elc+KWGBEOmwI7r7BMhhMYG\nJnZoFOl2p7JiDUQl8cBno3SJRLEooDQtd347ovV83PmnQdtYuBQCRKOnJJdhratHaQQAjogU\nwB3evcvzx6DaITCWGZZLYm3MXHJfe6984pQdKVg+KeySVT/0qxht6H6/vOVvBIiuywzhRN4O\nAA5jXiDaBACxVUgOND2g6icjsRvzRdam6hIA8IyRZcQVOT8HgNSEhZumPVCYeCUApFqXBKXW\nId6QXMdmnaot/n3u7l25rvUsIw7xRI4xJJnntAfKQnJb38GeSO3+xj8H5fPfXXe0RRWvpPqd\nk3ftZYQQGhZM7NBoEQ7sS/jD3cKu9wGAO3HM/PRjphefM7z/Ntva1L+Z4d03Tc9tO++rFFk2\nU6DtUkW+efXRwCsdux7s1xtVaGS3536W8KmGOWGtu+8slcpPN3ylXaoAAI3KAPBZ16Ot0cMA\nsNx5q43PBAADa48VJcg0RKm+u+vBBC4JAOpCH1PQKcsubp0HBDRdbuj5MNatpkfnpN3kMs2U\nND8FGlE6AMBhnJZkng0AQBgA6AiUe0JHgdKw3A4AB5ofjKrdKZb5AGDgnZRq1Z3Pi6zVE6ps\nDxyo8+5ItsxNS1ja5Dtl/4yzcJqmJ4hZh1v/Lqv+wsSt5z6hn/nptzIMt/P4Hcc9r7cFSo91\nvvxR7Z1twVKRtQ+rn7Hkk3CXWIQQOgkTOzS6lPmL5eWrgBAqimpWDtvVYdj5Nueu62ug5uSp\nRcXn3f8c+5dtfPbBnu0p4myOCG+lvCEVTQMAWQ93SjU61Sx8GgCsSrwdCNVBPxp4pTLwxkst\nt4VVT6FlMwAYWBsBUEECgAzDwkzj4ljPeaaVsSl0XfIJCvr6pDstbCoAlPueU6kMHJcaSnKS\nLCDkcMsTAMAQnmFEl6l4fsYtlFKdKodb/w4Ami57I8d41hSIugFgZf5vBNYCQGMLzoVlz8rc\nXyZb5gJAUGq9KPfnAmtV9aiqRyo6nhE5Z6p18QfHf2TgHEO/J3nOTUG5OUHMSE6YM6yb6TQV\nbSq832ksPNT22Ee1d1Z2bM+0rd5ceD/Pjt+Zy76IGwBwxA4hhGKweAKNLi0nTykqJpLEl+2n\nghDZfJnxtRf5yiNqTl6swYVkdQDAE+PVGQ+81HTbp96HTKzLS1v+mfKkq/mjav/bEc3LEWGe\n7RoAyDQuShcXtETLPui4jwBJFKddnfFQWPMCgJ3PbNf8Vi7NpzZ3SJXu8J6g2g4A9eHd84NL\nyxL2Wbn0gNJq4VJYRmCA+UrmtpdbvgsMBwCizLKCcFnx31+uuCbLvqqxZ9dHtXcWJ39lUeZ3\nj3W+5O5+nxDiCR2dnvilWalfq/G8crDlkWbfJ0beFVG989K+dbDlkURzMQCoVAYAwrClzQ/N\nTP7q9OR/q+l8qTXwWURpO975+rTEKxPEzE/qfz0z5fp069L+Hz/HsSnHsWnAPZmZcv3MlOsF\n4ZS9etcXntxgd13+fw36GgAcpsJVeXdfyHdkjMVG7AasCIMQQlMWJnZoLEirN3DHq4Uj5ZGc\nfCoIIJ1cd8Dw7ptMlyd87Q0AYHhvBwkGpJXrxD0fMqFg6Ks3Gd7fQfwBad1mYf8extMBDLsi\nK2fR0gNscwP/0UvE7wOjkZs55+tFLxzsee54cKemhFpCJUGttcC2enFldlqnLZoaEA7sZdva\nLPk+Yic/sL+quXorTCsDbwDAqpoZ01s3ykb2jfTXG23t+zsfJqyQZVyywH59ckN9E3fcS9ou\n8m/IeeUzc54XHFTsDl6V/j9AKfBvEUUhHC146+jPOn4IQBuSsj7K3r+/8U+qLpn45JnJ120q\nn2bsVlq3LE16/UBmN/EUpJ+A1zgu4Trzn52fuD3JaSfg1cbOnRfPeuza+e91h49XtG871PaY\nooUMnKPQdcWslOsNvDMW7bXzB65CggAgECuJNWJJLEIIAWBih8YGFcXohkuMrzwv7vmQKIqW\nljF4M45lVEX4bLdaWKSbzABACctIEXHfLnneIt1m545VCYdKSTgMQKVV66ggCCV7xc/26Emp\nS5w3LXHcFEsT9W/cYjAYpMP/IlKr+PH76qw58rJVlwcvMex6H/R3w1ddAywLAHNbchd/9n0t\nLSO6bjZR1Q0HAv52b5Y/vXXNzEiiHQAStO6bK76u2EyRRKtnScKG4He/dKCRclUNWxdRhug2\n+xeq1jtlRyQNOjYs1HnW0dB24z6bb05e58q5sU/EHtrHSMHkjw92LZ0h2xdeeXSuc29VODOZ\ngK9z/ap1wvrE3UcstS2NmZyUOPFGy8YDX8RtMWT0baeLEEJTHM6xQ6NMlkgkQiIRLS1Ty8xm\nurooyypzFwzemAKJhLWCacqseX1L3xFZlmfP11LTqdGkzJkPDMO2t0gr1+l2BzWZ1VlzAYBt\nG7xsk8iSWjxLzcmnBqOemKwWTCfhEOvrraLgKw5TozG6/mItI0vNyQtfsjUx4gSAWI1qDKNq\nwUyXd25OKN3pK0r3FaaxEVnoDgGAbrezlIs6zG2blwTz08JZyZ0r50aTHZZjp1SHsJLiXVgU\nSU/STIbuhUWUYYwtnrYNi2SHVTUbexZMAwBjS+eF3eUpSlJ7JM3nNOJzWIQQ6oWJHRpdhrde\ntTz4B8uDf7A8/Ge2qSG2BQUVDH0NSDhEFNn8zOMk2vt8Vs3OO/lVWQIAw/tvs+2tAAAMQ0WD\nbrHSz7d91I1mACDRkwuIEFVln3i4762adnINNt1sBgBhz8em57YRWSKhIAAx/fOp2Fet5vyu\nNAEA6sIf9/8IwZykk71ZRABgZQUAqM0R4sLt0x2a8eSSIorNwkoKI5+y7Uc0vXfxPMoQ3SAo\nVpNm6j1FNRoAgA2P330dxrPYlmtYEosQQn3wUSwaXdJFa7SMbAAAoEwkwpeVsM0Nhh2vRK6+\ndsA+Y9yJYwAAhNB+e3UzoeDAHgmAQTzlLQBQffDLE9K/MWUYANCTkml6Vm8eybLQb1M6V8oy\naNjfFCkxK0sdfE6sB03kB/RAdAoAmt0hUGKvaRVrPuKDIUZSgQChAACk3749lGV0juvXA+gG\nof/bAe23l226bgFOpxuS3soJMyZ2CCHUC0fs0OiiSSlabr6Wm6/lFijFs8PX3UjtDiYUEj87\nZWE2LTmVO17d++bzhI+oKoRDMGLbzH5+rfRMeeES0AbZy4ElPABQoCXexykM3FRxR/Vth1qf\nPPneYHCoDptHlVIcbZuXNn15XeOX14dyUi4ktu1lA0tc0Vn4om4AwEexCCHUB0fs0OCILPNH\nDnJVFUxPN4mEKc/rDpdaVCwvXh6rPAAA40v/4GprAj/6xXD6JdKaDYZXX+DqTsgLl1G+dzBM\ny8wWSvZS0di/LeuuJZQCYQBOHZBTFMPOHYyng6hq7/DeqVkaJYT6faSzHYCann1ST0qSFy/X\nHb3PQ/nyUmH3x5ErvggAoJ/SM9PRCgDX1FzBVIFs3MOyprN8FDYiGxQuysqeBXnUktDbg6Ke\n5ZSz68vqcNBuiPxRXOsEIYROgSN2aBBEUYzPPC5+8A61O6Q1G6OXf1FetQE4Tvx4p/HVf11g\n50x3NwCAovDlB/oOahmZVBDIqQ9e+ePVIAgDR+w0nen2EkWSl6+ObLpUKZwBAFx97SkpGiHa\nGy+DIAKAsmAx4+0yvvsWkU6Zx0ZNZhAN/SfncW1tXH0dAEjTZzw3860DSQf5YAQozX9+DxeR\nd1TfNuCDEF0HAI1obFdv6YOhs8fQ0gUAlA4c7RsuHLobCl/UbRHTRc4W70AQQmi8wBE7NAiu\n8gjr6ZDWbZaXrOg7KC9YbHj9Rb6qgm2o0/rVN5wd666Dz5MnIktMSxN/rJJabRSAr6pQc/Nj\nX6Isp+UWcDVVfScyfj/T0a45nKyv55QeFZkSJrrhUioIAECtNqH8AJGirLu2fyEtUzxbc9cR\nv08pnk15Xvx0F1d3XBf7T84jyoyZfHnpZynl79b82UqSvl36FWBY0HUurTBV3PJh10Ma1XqE\n7i8c3zzgQ5W2/O/0jLtUk0E18sYoTahpChscxnavraLONzvPfrjWeqwxmJ+uWowwZNvLNp06\n5xA+rv1Ztn19jmMjIWf7D9iHtXd6gke+PPf1oV9rcpA0n6T2pCacocIaIYSmJEzs0CCYUAAA\ntNS0U44SIm3eKm24hJotfccoISQSEXfu4OqOgyLrSSnSxZdpKSdP5A+W9JUeUNGgW63q3IXg\n97HNDUCp8Z3XKdf7dXXadO5YJQAQVaEczx2vpiwLRjP0dAMA6fFCShrb1kJ0nRIwPf80NVuU\n3HytoPcxHNfWIhyrYj4fPNP2fsKoGlDK9Hi19EwAYDydekYmAJBgAFTF/Mzj1GSivMDpDAD4\naedHaZ9sat5IdJn0dK8/5lwX/G636EuMOAAg+/WSW5nr/nf29lNvCLQtL0r+6KDjRLe96dNI\nemLLZSsow5iaPK59lUSn3fMLh3jDO5nG3qyub2YfgRb/Zy3+z050vbE67x6BSxhiV1OHP4LP\nYRFCaCB8FIsGoaVmAIC46wPi9/U/Tg3G/lkdAADLGl/cThOs0UuvlNdsZLq7jC8+F5vxJpS7\nVxsAACAASURBVC9aphZOp2aLlpImrd0Yueyq4A9+Km25kj1ykIQC0pYrtZwC0HQiywAAlGrO\nRGqyAADb2ACUcnU1WnZedOMlVBQBgNqdbJfHsHMHAOjpWZFNl6oFRfzRw8LeXaGv3gQAJBym\nLEcJoRwLhOFuvFne+gVqMhnefSs2KZBIETCagBBCgLBcZNOlyozZRFWKvdNYyk43rNufUi7p\nQQAQS/Yyfj+rM02WtiOuYwDwWt7OfxXsAIBj9rq/zn26ztoYezIrJ9o8LrnN2l1346VtFy9V\nrGbVYmz4yvoTN18ey+patyw7cfMV/W9Y/dcuabpq9clbbTJUfGvtP8Q/AvTL6mKvKcxOvaEz\ndGS3+7cj8m2dZD7fTAxLYhFC6CQcsUODUPMKlHmL+PIDlkf+omVkqVk5ema2lpFFeWFASyLL\n6vSZ8uLlve8jYXHvJ0x7m57eu7cECQXV5avkhb07nIofvAOCGL7m62A0Kdm5lsf+Glt2hG1r\nUQunqzNm8qWfcccqqSCQcFgtLGI6O/umwQkle4HjQZb0hAQ9JU1OSaMsKxzYxzY3AvSue0JU\nlTqc4PcRo4kmp8iLloq7PuRqa2Kx8Ac+oxxP7S7S3aWnpDF+H2VZk2Io7ipcPP0bTYHP2s2d\nOf4MZcYsNa8AAA553p/R5AIAr8EX4sMAoDJahI+orEqBvtp6e45pxXx7ltgcYnw9L7V8J0HI\n2FD4/2LR+qLut6r+3SykXDHzmd67qkdfPHxVnvPiJVl39L+HlR3/BIAlWXcUuLae/r3oiZxo\n8u1uC5SkJiwGgA9O/GdIaluRe+de9+/Dctu/zXtrQHtvuKai/anO0GFVi5qF5Cz72pkpX+WY\nkwsHtvj3Vnb8sztcQ0E3C8k59o0zkq9hmd7v7Ee1dwWiLesL7ytrebgjUE6pZjcWLMj4dl/+\nJGm+iranmn17o6qXYww2Y35x0lfSrEvO+Jdp1PgibsBF7BBC6FSY2KHBRS++TJk9jz9ykHXX\ni5/uAgBgWbWgSLporZ6U3L+lMnNO32vqcAIAEwn1rzVViopjL4gksc2NyoxZRBBAU0EQpDXr\nDW+/AQBMlwcKpyuF0/my/WxnO5w4FhvqEw7s6z1XVZnOdjU7l2uoJ4Eg0VQA0LKy4cA+rqUJ\nAKjJEqu9oKKBaN7YxD4tOR0AGE8nAIAgsi1NanYOkZXeyWwGI1E1AMjzZxr88sXuNVFWAgBl\n1jxqMgFAOlkFTZUAcFHez1iz0yufeLfjN1YufZbtKgNj65SqKvwvdzkzL21eQro6Uy2LG3re\n16gSWzOlPVAmcraQ3B6S28xCKgB0Bg/rVE1LWDrgVjf2fMwzpjznxYN+I4oSr27y7W7s2RVL\n7DhGVGmkpPGBfNcWE+8a0Ngbrt5Zc7vVkL0k83aLMakjePhQ8zZP+Oj6gj8QIADQ4t+3q/b/\nJFrmLM/+T44xtAT2Hm57UtJ6FmZ8L9YDS3hZ8++uv3tG8lcWZ/5HUGr71P3bXXW/vnzmttjn\n2lP3W2/k2Ny0m6yGHEUN1Xrf/Lju5xsK/5Rknn2Gv0qjJVYSi7vEIoRQf5jYoTPS0jNjs9NI\nJMI2ufnj1dzRw2xtTeT6b2rJqb2NGIaazH2nxNbvHVCg2teAhAJAKV95hK88MuBajMcDAFQU\nteQUtr2NaWpQZ88DAK7+hO5wMt1ekKNAKeeuAwCuuYF79smTV/B0AoCWlsEdrwaG6V2FuK4G\nsvJij3GZnm4A0KxWNpb/yb1biqlZObzDwXR7EyQLX/LpLKW42dAEBHq4LhuYAMAg9f6AVAZe\nn22+4UDP0zxj3JD8C5GxAECyWMwSsbTnqSZrRlqXJ61oSZ13R1eoMtkyFwDag6UZtpUtvk87\nggfznFsAoD1YxhAuxbrwlJusSyG5Lck8myGD/zC6zDPh8y0WAIAARBXvzOTripKuPr1xWfPD\nHGtaV/AHkbMKgpBqXQA6V9b8UKt/X7p1OQAEok1Jljkrc38pcnYASLUu7gpV1Xnf60vsAEDW\nAkVJ38u2rwUAA+fId2093PqEL1LvNE3TdLkjdDDPsWVa4lWxxum25UfbnyVkpBcbHAJftN4s\npBp4x9hfGiGExi2cY4fOjRqN6rQZkUu/EPnitURVhZK9wziZEGBO+WumFk4PXf/Nvj/K7PkA\nwAZ7INo7WgYARNOUgiK2vZVEInryyVIMNTNHWrYKGEaz2eX5i6WlFynTixmfT09OVbNzPw8X\nqCDqB0vZI2VcWwsAML4eajLp6b0bYJzy0QxGAKCEhq+6JnrxZcmRRAr0fffP2NZmvuoof6wy\n1qw+/Em7dNQjV6eKszkialSJ/Uk3LgCAdrOH8XSmJSwihOkIlgMApXpHsDzRPDPRMqstcDDW\nSUewLNEym2dOWRtP0cMAwLNmOAOWEVjCx5r1ybKvOb2looc94SOplkUcY9B0OfYnzboMADqD\nh2Ntpid/aUPhn2JZXYxFzFS04ID+06yL+16b+EQAiKpdAMAwvIFzNvv2NPV8olMVABjCzU69\nIdE060zxjxJZC0TVbhfuOYEQQqfCETt0Gk3ja6qorqkz5w74ippbAIQQv//8OqYJNmAYoql6\nembfQb21GQBAVsSyz6QVq7X0TOB5UFUQBK7uBBV4LTGJqwYQjUAI0VW1aIbucAiHSvmKQ0RT\nqdmizJylzl3Qu1+FrhNFJqLAXnaVtvMd9mApAFCjKbrpUmoyASEkGDolJl0DAL8QNAForiQ+\nMQs6WuvhUE35f88OLdAKXVxnOwBQSg90P0kprQ/vqQ/vGfC5giaFqe8SiMllLO4MHgKArnC1\nooVSLAsUNVTleR4AZC3QHTk+L+3mAefyjBkAJC1wppum6ZJGFYE9WbNCgBi4QYapInIXpdTd\n87675/0BXwornxcL61K158XG7l0hpVVRw0AoxJbc67cnGyGMyJ5cGY4hLADoVItdenXeb/c2\n3PtJ/a85xuAyz0qzLsl3XiKwY12064vWA+45gRBCp8HEDp2GZfm9nzDdXRFnopaa3v8rfMUh\noFRLST3TqWdHeV7LyGbddYyvR7f1Dhpp2XnK3AVMUwN34piaV6Clpkc3X2Z482W2uYFtrNdy\n8tX8QjW/EAC45FS2tYUEA3pSSnTjpQDAdHv56qNaYRFlT/5NVgqLuDnzeYNB2XwpOXxQKNkr\nLVyi2+ymbX8DQrgmN6U0fENvgqVlZLHtbbU2d2pvhAIBkmyY8U7RZ2k5P9cDb9CONgDIN60u\nV94CgEzj4pkJVw74XObjDaAFSHdXmnXJ0Y7tOlU7gqVmMc0spCQnzC9reSggNfuidZTS2PjZ\nKTebEayGrJ7ICU2XWEaE03SFqwDAYeq3cgohZ1nZLtN20Yzk6wCA5zlCiCwrACByvXnh7vq7\nW/z7piV+Yb79ZpG1AWGOtD7e5BuYqp6F0zTt0hmPdYWq2gL7WwMl5c3/W9n+7LqCPziMBUPv\n5ML5IlgSixBCg8DEDg1CuuRy4wvPGp99Qi0q1lPSqNFIIhG20c3V1ugOp7x81fn3vG6TcfuT\npuf+Li1fpdscrLdL2LuLclz0kitMzz8t7N0dveJqzZWoW238wTIiSUr+ySEZZdFS5u3Xje+8\nIc+ZTy0JjM/HHykDlpMXnSxHoALPVx0luk7TM9nWZrashJpMenauadvfAIBSSggBQsxPPRrd\nvJXp6uTLy3xOUu2svahfkJuSfvlc49c+8vwxTZwTFEMAsLFjkwbu47bjih5OFAfO1mcTeICD\nTFdnWuaSw21PdoWr2oJlKZYFAGA35gtcQnugzBetMwnJNkPO6fck277+SNu2Gs8rM5KvOf2r\nNZ0vA0C2fd05761JSCKEUXU50VwMAIIgEEKkfvttRGRPi39fhu2iRZnfP3lXtcg5ex6AAEk0\nFyeai2en3uANH3u35ntH259ZmfvL4fZzIfySGwDwUSxCCA2AiR0ahJaWEb7hFn7/Hq6hnqup\nJppKBZG6XNKq9cqipacvejKMnlPTI9d/U9j9kbjrfSJJ1GRWi4qlFaup2aLMW8QfLOGOlCvz\nFmn50/iDJXqCTU9OOXmuKym65Uq+vFQo209kmRqNanaeMmf+KfFQEl23yVi6Xz1Ywmq6npwq\nL13efzyvN7cDMOzcQc0WpXhWRVaV3n3KxLsUw6x59uvKep6BBL0+sXJ1aKvpeMNl/Mb/me3u\nkCqDWoeF7a0L7pYbaoLvzE5Y6wRgPB5H0WqRs7X693uCRwpytgIAAZJsntMROuiP1J8+XBdT\nlHR1bddbh9uetAjpmfaTSTMFWtnxXKNvV7Z9baL53JPYOMaQZJ7THijrq8MFgJ5IbY3nleKU\nayxChg4qAJj4k0XN3nB1R/AQAOhUH7TPAbrDx6s6/jkn/RsWoXco12kqElmbpPrOfuKIw11i\nEUJoUJjYocHpVpu08VLprG0iV39lwBF15txAv5l5pzcAAC05ddDj0pqN3Ilq4Ui5llMgz5kv\nz5k/SFROl7R+4AZfp6A6TbDql1xmMBiCwaCqqoM0oRQAwtd/szee7uqTMazfHPvIK13fOx7c\nWRV4k/K0feNip5CnUYWpf4GqoXfafznX+m8WLtmvtFQEXmaJMD/pOmCr2K5OAiQ1YdGJrtd1\nqsZG7AAgyTzvaPuzsu6fk9Z7xfZg2Ycn/rM4+dq5ad8EAIFNWFvwu49O/OyT+l8nW+alWBbw\nrCWqdDX79/qidenWZUuzf3y2j9zP/PRbdx7/4c7jd8xM/qrDnO2LNhxu2cYw4vz0WwDAxKdY\nhHR3984k8yyzmNoZqjjheW1a0pXHOl+q7343y7baJCSfvX+TmNQa2N91vHpGypctQpqmyw09\nH0bV7gWuW4cY4UjxRetNQrKBc47xdRFCaJzDxA6NF1QUpQ1bDK88L376cWTLFRCPFTT68Ixp\nffLPXm35Qd8RlvCbU371QvOtOlUO+p5T9bDI2rKMS2dbrxZYM7VaSU83UZR061J39/s2Q05f\nfUNsmh1DuJSE3oVOKKWU6hRODpLZDHlbix8/5nmluWd3Vee/ND0qcFaXcfqctBszbCtjS9AN\nhdNUtKnw/or2bYfaHlO1kMg5M22rZ6VcH6u6JYSszv/NgaYHP2v8E8NwyZZ5awvuZYBrD5Qd\nan2MUm3QZ8H9iaxtU9EDFa3bKtqeklQ/z5ithuyVub8ctEp39ChaMKJ4s+yrz90UIYSmGBIb\nvZigfD6foij9j9jtdo7jPB5PvEIaD6xWazgcHnSwavwzvvQP7ni1tHyVOm3GcM817NzBdLSB\noui3fP/0EbvYNLuYvuKJ4Xq97cc1gXeXOr5VaNnY/zhXcYirr41uubz/4izxdfocu0nDEzry\nXs3t89K/ta7g3rM04zjObrdHIpFQKHSWZpMby7IWi8XnG+tn5eOKy+XSdb27uzvegcSTw+Ho\n6emZoP/iJyYmxjuEiQRH7ND4Et24xdxQJxz4TMvIjm3/MHRsbAsKAOaRB2QA+OZ3+n/1vJO5\n/tYl/qQ+tPug77lM4xIDa+07Tu0OAGA9neMnsZvEfNEGwAl2CCE0GFygGI0v1GqTV60niiyU\nfDrscynt//9R4fGHRjQ0AAALl7LMebOsB8v9z/U/rtvtAECm9lDxmOktiTUNe0wXIYQmPUzs\n0LgjL1yqZ2Rx7jqu0d3/+enZDb3lBVpo/7pTyK8NfeiRa/oOUpMFeJ71dIxNDFNcbBE7XOsE\nIYROh4kdGn8IiW6+DADED98FANO2v41Z0jYULOE3Jv8CKJR0P6H3FUAQotvsJBggUjSu0U0J\nPqneyCdiSSxCCJ0OEzs0HpmefLj31ZBrY0+fQiefOsduBGUaFxUlXOyV604Ed/Yd1G0OAGC6\nRvhp7PayTdvLNo1snxOaooejcpcL95xACKHBYGKHxjUCsdyOQPTc1Z39czvhBz8ZxbAA1iX+\nVGDM5b5/SFrvzrnUZgcAfBo72vyRegrUZcYJdgghNAhM7NB4RwAIAfMLz4gfvsc11oN+tj0S\nwjfcHL7hZv2W75+lzYgwcYnLnLfIeqjMtz12RLM7YKTrJ/rG6nDQro9PcgOAwzhwYzeEEEKA\niR0anwI/GbjxqJaYxDXWix++Z3r+GXHvJ0xH+3D75CuPmJ961PzM4yQ6yO6oTGe7+alHzU89\nyra3DrHDhY6vJwrT6kIfdUiVAABGIxhEtqtzuIH1Sduxr+CRV/venp7MZby2O+/xN8+7/8nB\n31s5gSN2CCE0CEzs0DjVP7cL/OSX4RtvDd30bXnZSuA4rqbK+PZrxlf+xZeXklBwuD1zJ46d\nfpA/fgyY4f04MMBuSPkZAJR0P0FBg9g0u0j4PEIaiu1lm4IFGf6ZuaPR+QTi613rBOfYIYTQ\nIHCBYjR+DRi30xOTpTUbpVXr2YZ6/lApf7xaOFQqHC7Tk5KV/GlaXgHl+HP2qSWncserlVnz\n+h8kqsq6a3VXEtM5vIHADMPC6QlbqgJv1QTfK7JcotscTHsb09WpmS3D6ud0gz57fVj61nXL\n37vAnic6X6TeyDuNPK5EjxBCg8DEDk00DKPl5mu5+VI0wlVX8hXlbHOj2NFOS/dpmTlq/jQt\nNf0sZ2uZ2ULJXqa9VU85uUUE664liqKmZw5I7FivhztUxna0EUWhZouSm6/Onk+53p8aw/s7\niD+wcfWtSyvNaUEDw+yLJJoClCQca0zY5xZ6gqpZ7JlX6JuVF2uf+conXDDSumVp4u4KsbMb\nKJWSnZ5VsyXnyR0sgBAmKid9cvjH9bfwOtdh9LyZ90Gruffx7tcqr8oue7Pum1sBgInKrgNV\nJnc7F5Ioz0oua/e8aeHs5BG4w+OYqkciiifdtjzegSCE0DiFiR2aqKjBqMxbqMxbyHR5uKoK\n/shBrvY4V3ucmi20sIjOXQCDDeBpGZn0kMDXVEv9Ejv+eLWelKRbbf1bsl0ew9uv6TabvGyV\nbjBwHe384TK2sz26aWtsERZKWEaK2EqONM7KfkF6eJP38jl1KTxrh5ae9ivW6QKfuPtI0q5D\n0RSXlGgFAJ1lmaiU/EFp19JZUrKd9wXT3tmf/tqehms3aKLQ+6EYJv2tfZE0V3DTGt4fSi2p\nuqnuG/XXb6YMAwCpTbsh2rvpZ9q7+4VOn3fpDNlhZSXFWuVO27G3+cpV0dTJvLqbL1pPgeKe\nEwghdCaY2KEJT3clyivXyhetYVua+Ipy7uhhprxULS/lXUkkr0DJLwTR0NeYspyWW8CeqCGy\nRAURABi/n+lol5avGtCtULIXOD6yaWvsdDkljbKscGAf29yoZWbH2hBZlmbPz8tINzW88Qb3\n4mz3dwSdb+f9sj0BCOlZMM1S22Js6YwldkCAUbSez8fVNIPTs2JWynsHEqobe+YWxDpkFDVY\nkH7ybVR2lh4TOn1SiqN/bETTjC0e//Qc3+z82JFQbqqj9BgMddW/icoXdQPuEosQQmeGxRNo\nsiBEy8iKXnx56Dt36Fddw0ybzng9Qsle8wvbDR+8w7lr4fNtZNVp04mmcrUnYm+549WUZbXc\n/FM6kxWms11NzyAcRzQ19kfLygYA9tSCXD0llQF2fdJdGtEinKRyRFciTMAPAKrRAABs+JQV\n+MJZKSdfpyUCgOD1928QmJbZ91qxmQCAO20NP8owmslgqW+11LYSXQcAyhDv4unRlMk8XAcA\n/mgDADixcgIhhM4AR+zQZENFkc5byK9Y5W9sgENl/KEytqmBbWoAngcApqdby8jSHS7ueJUy\nYyZQytXVaNl5lBf6d0KiIaCUqzvB1Z0Y0D8JhU6+YdlYxUaGccGMhK0aKLKBggbE0wFWG2UA\nAMjnCSUAUIZohpMPiHWDAKdmfpQhmlE82X+sUFc/2cPnQZCWLctS3j+Q+s5nOs9GU1zh7OTA\n9Ky+R7qTlS9aD1gSixBCZ4aJHZq0qNWmLFspL1vJtrdyR8qFwwcBwPD+27rdTi02tsnNejpB\nipJwWC0c/NGempmjzJk38KggDtYW1iT9iJCnQ+CL8FHB06nln3sFXRLL2Ia8bVp/UpK94ZoN\nYkePubHd1NSZ+GmFs/RY8+Urex/7TlL+qNvA2U3CJK8RQQih84aJHZr8tJQ0LSWNWm3ih++q\nOflcYz309ACA+PFO3WiiZovWr5AihposQAjRVT1xqDmEiXXxjJmCvyTt0Oqu3EHbEJ2yUVkz\n9I6rsREJADTT4JniuREipTikFId38QxDZ0/GSx87SqvbLl5ynr2Ne6oeCcsdadal8Q4EIYTG\nL5xjh6YMhgEAacsVwW/fLm24hIoGEgqyng4SjYj7djMd7QD9nplynJacyra2kGDgZAfdXnHv\nJ0zAd6Yr8MTEM8YT9vo2qeJMW5+Z607ubGGubwWASLpruB9F9PhS3jvA+08+FI4m2XVRYKPy\ncLuaQPzRBtwlFiGEzg5H7NCUQ80WedEyzZVkev5pIIQKIldTxdVUUaMZACASjjVTFi1l3n7d\n+M4b8pz51JLA+Hz8kTJgOXnR2UaMbHwmAPk0rWRLtweMA5+K6gJnO1zLRuVoskP0+p2fVaoW\nQzD/bAvvDUqxGE2NHYaOnp75BUqCiWia5XgLG5H8F80ablcTSGyCHZbEIoTQWWBih6YoLSdP\nt9qowxn+8vVsQ71w9BBXVQEAhk8+0KuPxrayiG65ki8vFcr2E1mmRqOanafMmT+gzGIAgTHP\npKsrxI9r2v41I++bA75KgbRtWZq4+4ij7BjoNJrq8qyaQ1l2uMHrBqHpi6ud+6udJVVMVKYC\nLzsS2i5ech454gTSWxJrxMoJhBA6I0LpaQV3E4fP51MUpf8Ru93OcZzH44lXSOOB1WoNh8Oq\nqsY7kLgxmUwmk+n0vx5nR6JRrvooX1HOtjQBpZRltcwcddp0LTV9WPUNUU/NE53XayzdOvsp\nI3/yMWv6G58a2rpq//3yYXySCyAIAiFEkgYulTJxfVz78xb/vm8tO2wWUofSnuM4u90eiURC\n/QuZpxiWZS0Wi893xvkDU4HL5dJ1vbu7O96BxJPD4ejp6Zmg/+InJuIWgsOAI3YI9aIGQ+9W\nFl4PV1nBV5Rz7lrOXUtNZjWvUC2crluHVHBqcBasKV/xTvaH5S2PLM+5a7TDnjp8UbfI2YaY\n1SGE0NSEiR1CA+nOAVtZHOEryvmKcs2ZqOUXKnnTwHDWOlaGWSCtPRSuqIed+c5LkxPmj1Xg\nk5mmS2GlPTVh0tb8IoTQiMCqWITOoG8ri+/9OHrll7XcfLa7SyjZa37x2d6tLM5Q9woAJDF1\nS91aAuRA8wM6nbrPxEeQX3JTSnFpYoQQOjscsUPoHCjHKdNnKtNnkoCfP3qYP9y7lYUgilp2\nnlo0Q3MOnP+huRIzq9NmcxsPR9871vnijORrAKDlshXxCH+S8EViu8RiYocQQmeDiR1CQ0UT\nrHL/rSwqj8TWSdHtdjV/mlpQRA3GWEvdlQwA671bj7tKK9qezrFvMAo4+feC+KOxxA7XOkEI\nobPBxA6hYYttZSGv28zWn+AqDvHHq4XS/UJZiZaaruYVarl5hlefB0KsR+vWpszZkfdRWev/\nXpTz83hHPbH5om7AXWIRQuhcMLFD6DxRllULitSCIikU5I8e5o6Us63NbGszLdnbtzjKovY5\nh5OqGuCDAtfWFMuCeIY7wfmiboFNMIsDN39DCCHUHxZPIHShqNkiL1kRvunb4RtvkRctI7LU\nt+4dAXJJrIqi8X6NDmNRPdSfpsshpc1lnkFgGAsKIoTQFISJHUIjRktOlTZcMuBgeihlXkex\nX2o61vliXKKaBPzRBkp1pxEn2CGE0DlgYofQqFt65UsGznmkbVtIbo93LBOSX6oHAKcZJ9gh\nhNA5YGKH0AgL/OSXA44YeeeK3Ds1XTrY8khcQprofNEGwMoJhBAaAiyeQGjknZ7bzUm9sbL9\nucaej1r9W9KsuH3C8Pij9QCAj2IRQuiccMQOobFACLOu4D5CmNLmB7GKYrh8UTfPmi2GjHgH\nghBC4x0mdgiNkZSE+bNSvhaQmqs7X4h3LBOJpstBudVlwpJYhBA6N0zsEBo7K3N/YeCcFW1P\nheS2eMcyYfilRkp13HMCIYSGAhM7hMaOgXeszPu5pksHm/833rFMGJ9vJoaVEwghdG6Y2CE0\npmalfC3NurjRt6vFvy/esUwMuJkYQggNHSZ2CI0pQph1Bf/FELa06a+aLsc7nAnAL7kBwIWL\n2CGE0BBgYofQWEu2zJuV+vWg3Fzd+Xy8Y5kAfBE3z5otYma8A0EIoQkAEzuE4uCi3J8beVdF\n+7NYRXF2GlWCUovTVIQlsQghNBSY2CEUBwbOvjL3F5oulbU8HO9YxrVAtJGChhPsEEJoiDCx\nQyg+ZqZ8Nc26pKnnE6yiOItYSawD1zpBCKGhwcQOofjoV0XxP1hFcSY+CUtiEUJoGDCxQyhu\nki1zZ6fdGJRbqjr+Ge9Yxil/JFYSOyPegSCE0MSAiR1C8bQi5y4jn3i0YztWUQzKJ7k5xpgg\nYEksQggNCSZ2CMWTgbOvyvulpkulzX+Ndyzjjk7V3pJYgr+pEEJoSPDXJUJxVpxybYZtRbNv\nT4t/b7xjGV/8UpNOVVyaGCGEhg4TO4TijABZX3gfQ7gDzVhFcYreklgjlsQihNBQYWKHUPy5\nTMVz0r4RklorO56LdyzjiB93iUUIoWHCxA6hceGi3J+ZhZTK9u1+qSnesYwXvYkdlsQihNCQ\nYWKH0LggsAkX5f5Co0pp84PxjmW88EXdLCMmiFnxDgQhhCYMTOwQGi+KU76SYbuozV/S7NsT\n71jiT6dqQGpymooYwsY7FoQQmjAwsUNovCBA1hX8F8vwB5ofVPVovMOJs4DUrFMVJ9ghhNCw\nYGKH0DiSaC6ek3pTWO6o6vhHvGOJs9gEOycmdgghNByY2CE0vqzIvdMspFa2PzfFqyh8vYkd\nrnWCEELDgIkdQuOLwCasyvulRpXSpildReGXYmudYEksQggNAyZ2CI07M5L/LdO+H/+nnwAA\nIABJREFUqi1Q0uTbHe9Y4sYfqWcZ0WrIiXcgCCE0kWBih9B4tC7/XpbhS5v/Z2pWUVDQAlKz\nw1iIJbEIITQs3FhezOv1Pv744+Xl5bIs5+fn33TTTUVFAyfQ/OAHP6ivr+97azAY/vnPf45l\nkAiNBy7zjHlp3yptfuho+/a5aTfFO5yxFoi2aFTBkliEEBquMU3sfvvb3wqC8Jvf/MZoND77\n7LN33333o48+ajAY+rcJBoO33HLL8uXLY28ZBscU0RS1LOenxzwvV3X8I8+5aaot0hubYOc0\nY2KHEELDM3ZpUyAQSEpK+u53v5ufn5+WlnbDDTf4/f7GxsbTm6WmpiZ+zul0jlmECI0rAmtZ\nlfsrnaolU6+KwhepBwCnERM7hBAanrEbsUtISLjrrrv63nZ1dTEMk5iY2L+NoiiSJH366adP\nP/10IBAoLCy84YYbMjIyxixIhMaV6clfqmh/prFnV6NvV5ZtdbzDGTv+aAMAuHDEDiGEhmlM\nH8X2CQQCDzzwwFVXXeVwOPofD4fDdrtdVdXbbrsNALZv337XXXc99NBDZrM51uCNN974+te/\n3tf+pZdeWrNmTf8eCCEA4HK5Rv0zjGOEEJ7n4x1FPMX+Glit1ngHMgK+sOihhz5YUN7y8PSM\nDTxrGu7pA6Y6TBRBtZFlhPyMRQy50N9RRqNxgt6EkUIIwV+JLMviTcAnYFPEKCZ2n3zyyR//\n+MfY63vvvbe4uDj2uqmp6Z577pk/f/6NN9444BSbzbZt27a+tz/96U9vvPHGPXv2bN68OXbE\nZDLl5+f3NRBFUdO0/j2wLEsIGXBwqmFZVtd1Smm8A4kbhmEYhpkcN8Flmr4s7/t7Tvyp1P34\nktzbhn5iLLudiHdAp3pP2O0yF1GdaHD+P8uxf851Xdd1fQTDm1gIIQzDTPFfiRzHUUqn+E1g\nWXbi3gGOi88g1AQ1ijdr4cKF999/f+x1ampq7EV5efnvf//766677vLLLz9nD0ajMSkpyePx\n9B1Zv359SUlJ31ufz9fT09P/FLvdznHcgINTjdVqDYfDqqrGO5C4MZlMJpMpGAwqihLvWEbA\nvJTvH2rafqjpmXTzOqthqFUUgiAQQiRJGtXYRoNfatJ02W6YdoE/yBzH2e12SZJCodBIxTbh\nsCxrsVh8Pl+8A4knl8ul6/oU/3fB4XD4fL6J+D89ABgwawud3SgWT5hMppzPiaIIAEePHr3v\nvvvuuOOOM2V1brf7wQcf7MtIotFoZ2dnX1KI0NQksJbVeb/RqVra/EC8YxkLftxMDCGEztfY\nDW/Ksvzf//3fV155ZU5OTt8gnMViMRgM7777bjQaveKKK5xO56effqqq6rXXXqtp2rZt2ywW\ny0UXXTRmQSI0PhUlXX20/Tl39/sNPR9l29fGO5zR5Y/WA4DLjJuJIYTQsI1dYldZWdnW1vbs\ns88+++yzfQdvvfXWyy677ODBg36//4orrkhISLjnnnueeOKJ22+/nef56dOn33vvvbHRPoSm\nuHUF9z59YHVZ88Pp1qUcY4x3OKMoVhLrNOKIHUIIDdvYJXbz5s179dVXB/3ST37yk77X+fn5\n99xzz1gFhdCEYTfmL8j8dknjXyranp6XfnO8wxlFvmg9y/A2Q168A0EIoYkH93VAaMJYlv1j\nqyGrquNfvmhdvGMZLZRSv9RkNxawzJResgchhM4PJnYITRgcY1yV92sK2oGmByhMyOq2cwop\nLZou4Z4TCCF0fjCxQ2gimZZ4ZY5jY0fwUGP3h/GOZVT4IlgSixBC5w8TO4QmmHUFv2OJUNry\nkKJNwuXZfJIbsCQWIYTOFyZ2CE0wdmP+wszbooq3ov2ZeMcy8rAkFiGELgQmdghNPEuz70gQ\ns6o7XuiJ1MY7lhHmj9azDG835p+7KUIIodNgYofQxMMxxjUFd0++KgpKqT/aaDPksYwQ71gQ\nQmhCwsQOoQmp0HV5rnNTZ+hwQ/cH8Y5lxITlNlWPOk1YEosQQucJEzuEJqp1Bb9jGbGs5eFJ\nU0XRWzmBiR1CCJ0vTOwQmqhshryFGbdFFW9F21PxjmVk+CL1AIAjdgghdN4wsUNoAluafYdV\nzK7ufKknciLesYyAz9c6wcQOIYTOEyZ2CE1gHGNYU3DPpKmi8EfdDOHsxoJ4B4IQQhMVJnYI\nTWwFrq15zs2doSNu73vxjuWCUKD+aKPdmMcSLIlFCKHzhIkdQhPe2oLfsYx4sOURRQvGO5bz\nF5bbVT2CE+wQQuhCYGKH0IRnM+Quzvx+VO0+3LYt3rGcP1/UDbjnBEIIXRhM7BCaDBZn/YfV\nkFPjebl7wlZR+GOJHVZOIITQBcDEDqHJgGMMa/N/S6l+oOkvE7SKIjZih4vYIYTQhcDEDqFJ\nIt+1Jc95sSdUUe99N96xnA9/1M0QFktiEULoQmBih9Dksa7gvzjGOBGrKChQf7TBZ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in: https://uc-r.github.io/hc_clustering"],"metadata":{"id":"FQwVrcjDRHu-"}},{"cell_type":"markdown","source":["#Other methods **UNSUPERVISED and CLASSIFICATION**: dimension reduction using PCA and discriminant analysis using LDA\n","\n","\n","\n","![](https://miro.medium.com/v2/resize:fit:640/format:webp/1*z8ysJEwuCz-_WxisMg13LA.png)\n","\n","\n","\n","See in https://medium.com/machine-learning-researcher/dimensionality-reduction-pca-and-lda-6be91734f567\n","\n","In Machine Learning and Statistic, Dimensionality Reduction the process of reducing the number of random variables under consideration via obtaining a set of principal variables. It can be divided into feature selection and feature extraction.\n","\n","We will deal with two main algorithms in Dimensionality Reduction\n","\n","Principal Component Analysis (PCA)\n","Linear Discriminant Analysis (LDA)\n","\n","**PCA** is a technique (UNSUPERVISED) for feature extraction. So it combines our input variables in a specific way, then we can drop the “least important” variables while still retaining the most valuable parts of all the variables.\n","\n","**LDA** (SUPERVISED, CLASSIFICATION)is a type of Linear combination, a mathematical process using various data items and applying a function to that site to separately analyze multiple classes of objects or items. Following Fisher’s Linear discriminant, linear discriminant analysis can be useful in areas like image recognition and predictive analysis in marketing."],"metadata":{"id":"tISp9b6AxS7w"}},{"cell_type":"markdown","source":["In PCA and LDA we will use the wine dataframe. Issue and examples inspired in: https://little-book-of-r-for-multivariate-analysis.readthedocs.io/en/latest/src/multivariateanalysis.html"],"metadata":{"id":"5rJ7xu3GdGoH"}},{"cell_type":"code","source":[" wine <- read.table(\"http://archive.ics.uci.edu/ml/machine-learning-databases/wine/wine.data\",\n"," sep=\",\")\n","\n"," head(wine) #without variables definition\n"," #tail(wine)\n","\n","#defining the variable name, when it is convenient to do so, for example to interpret the variables\n","#names(wine)<- c(\"groups\",\"Alcohol\",\"Malic acid\",\"Ash\",\"Alcalinity of ash\",\"Magnesium\",\"Total phenols\", \"Flavanoids\", \"Nonflavanoid phenols\", \"Proanthocyanins\",\"Color intensity\", \"Hue\", \"OD280/OD315 of diluted wines\",\"Proline\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":286},"id":"hwA86udHeSD3","executionInfo":{"status":"ok","timestamp":1717497446548,"user_tz":-120,"elapsed":730,"user":{"displayName":"toni Monleon","userId":"05269440626861479105"}},"outputId":"8bf92551-7b99-4258-9bc9-57670be6405f"},"execution_count":39,"outputs":[{"output_type":"display_data","data":{"text/html":["\n","\n","\n","\t\n","\t\n","\n","\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\n","
A data.frame: 6 × 14
V1V2V3V4V5V6V7V8V9V10V11V12V13V14
<int><dbl><dbl><dbl><dbl><int><dbl><dbl><dbl><dbl><dbl><dbl><dbl><int>
1114.231.712.4315.61272.803.060.282.295.641.043.921065
2113.201.782.1411.21002.652.760.261.284.381.053.401050
3113.162.362.6718.61012.803.240.302.815.681.033.171185
4114.371.952.5016.81133.853.490.242.187.800.863.451480
5113.242.592.8721.01182.802.690.391.824.321.042.93 735
6114.201.762.4515.21123.273.390.341.976.751.052.851450
\n"],"text/markdown":"\nA data.frame: 6 × 14\n\n| | V1 <int> | V2 <dbl> | V3 <dbl> | V4 <dbl> | V5 <dbl> | V6 <int> | V7 <dbl> | V8 <dbl> | V9 <dbl> | V10 <dbl> | V11 <dbl> | V12 <dbl> | V13 <dbl> | V14 <int> |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | 1 | 14.23 | 1.71 | 2.43 | 15.6 | 127 | 2.80 | 3.06 | 0.28 | 2.29 | 5.64 | 1.04 | 3.92 | 1065 |\n| 2 | 1 | 13.20 | 1.78 | 2.14 | 11.2 | 100 | 2.65 | 2.76 | 0.26 | 1.28 | 4.38 | 1.05 | 3.40 | 1050 |\n| 3 | 1 | 13.16 | 2.36 | 2.67 | 18.6 | 101 | 2.80 | 3.24 | 0.30 | 2.81 | 5.68 | 1.03 | 3.17 | 1185 |\n| 4 | 1 | 14.37 | 1.95 | 2.50 | 16.8 | 113 | 3.85 | 3.49 | 0.24 | 2.18 | 7.80 | 0.86 | 3.45 | 1480 |\n| 5 | 1 | 13.24 | 2.59 | 2.87 | 21.0 | 118 | 2.80 | 2.69 | 0.39 | 1.82 | 4.32 | 1.04 | 2.93 | 735 |\n| 6 | 1 | 14.20 | 1.76 | 2.45 | 15.2 | 112 | 3.27 | 3.39 | 0.34 | 1.97 | 6.75 | 1.05 | 2.85 | 1450 |\n\n","text/latex":"A data.frame: 6 × 14\n\\begin{tabular}{r|llllllllllllll}\n & V1 & V2 & V3 & V4 & V5 & V6 & V7 & V8 & V9 & V10 & V11 & V12 & V13 & V14\\\\\n & & & & & & & & & & & & & & \\\\\n\\hline\n\t1 & 1 & 14.23 & 1.71 & 2.43 & 15.6 & 127 & 2.80 & 3.06 & 0.28 & 2.29 & 5.64 & 1.04 & 3.92 & 1065\\\\\n\t2 & 1 & 13.20 & 1.78 & 2.14 & 11.2 & 100 & 2.65 & 2.76 & 0.26 & 1.28 & 4.38 & 1.05 & 3.40 & 1050\\\\\n\t3 & 1 & 13.16 & 2.36 & 2.67 & 18.6 & 101 & 2.80 & 3.24 & 0.30 & 2.81 & 5.68 & 1.03 & 3.17 & 1185\\\\\n\t4 & 1 & 14.37 & 1.95 & 2.50 & 16.8 & 113 & 3.85 & 3.49 & 0.24 & 2.18 & 7.80 & 0.86 & 3.45 & 1480\\\\\n\t5 & 1 & 13.24 & 2.59 & 2.87 & 21.0 & 118 & 2.80 & 2.69 & 0.39 & 1.82 & 4.32 & 1.04 & 2.93 & 735\\\\\n\t6 & 1 & 14.20 & 1.76 & 2.45 & 15.2 & 112 & 3.27 & 3.39 & 0.34 & 1.97 & 6.75 & 1.05 & 2.85 & 1450\\\\\n\\end{tabular}\n","text/plain":[" V1 V2 V3 V4 V5 V6 V7 V8 V9 V10 V11 V12 V13 V14 \n","1 1 14.23 1.71 2.43 15.6 127 2.80 3.06 0.28 2.29 5.64 1.04 3.92 1065\n","2 1 13.20 1.78 2.14 11.2 100 2.65 2.76 0.26 1.28 4.38 1.05 3.40 1050\n","3 1 13.16 2.36 2.67 18.6 101 2.80 3.24 0.30 2.81 5.68 1.03 3.17 1185\n","4 1 14.37 1.95 2.50 16.8 113 3.85 3.49 0.24 2.18 7.80 0.86 3.45 1480\n","5 1 13.24 2.59 2.87 21.0 118 2.80 2.69 0.39 1.82 4.32 1.04 2.93 735\n","6 1 14.20 1.76 2.45 15.2 112 3.27 3.39 0.34 1.97 6.75 1.05 2.85 1450"]},"metadata":{}}]},{"cell_type":"markdown","source":["Each variable has its scale and its unit, this can be a problem...?"],"metadata":{"id":"QhLJ2Mfme5ud"}},{"cell_type":"markdown","source":["#Standardising Variables\n","\n","If you want to compare different variables that have different units, are very different variances, it is a good idea to first standardise the variables.\n","\n","For example, we found above that the concentrations of the 13 chemicals in the wine samples show a wide range of standard deviations, from 0.1244533 for V9 (variance 0.01548862) to 314.9074743 for V14 (variance 99166.72). This is a range of approximately 6,402,554-fold in the variances.\n","\n","As a result, it is not a good idea to use the unstandardised chemical concentrations as the input for a principal component analysis (PCA, see below) of the wine samples, as if you did that, the first principal component would be dominated by the variables which show the largest variances, such as V14.\n","\n","Thus, it would be a better idea to first standardise the variables so that they all have variance 1 and mean 0, and to then carry out the principal component analysis on the standardised data. This would allow us to find the principal components that provide the best low-dimensional representation of the variation in the original data, without being overly biased by those variables that show the most variance in the original data.\n","\n","You can standardise variables in R using the “scale()” function.\n","\n","For example, to standardise the concentrations of the 13 chemicals in the wine samples, we type:"],"metadata":{"id":"LlZVr6fXvNJb"}},{"cell_type":"code","source":[" standardisedconcentrations <- as.data.frame(scale(wine[2:14])) #use scale"],"metadata":{"id":"enU1H5SrvM_f","executionInfo":{"status":"ok","timestamp":1717497456101,"user_tz":-120,"elapsed":373,"user":{"displayName":"toni Monleon","userId":"05269440626861479105"}}},"execution_count":40,"outputs":[]},{"cell_type":"markdown","source":["Note that we use the “as.data.frame()” function to convert the output of “scale()” into a “data frame”, which is the same type of R variable that the “wine” variable.\n","\n","We can check that each of the standardised variables stored in “standardisedconcentrations” has a mean of 0 and a standard deviation of 1 by typing:"],"metadata":{"id":"zJK2grYwvM14"}},{"cell_type":"code","source":["print(\"means:\")\n","sapply(standardisedconcentrations,mean)\n","\n","print(\"----------------------------------------------------------------------------\")\n","print(\"----------------------------------------------------------------------------\")\n","print(\"variances:\")\n","sapply(standardisedconcentrations,sd)\n","\n"],"metadata":{"id":"uu3jJtSvvMti","colab":{"base_uri":"https://localhost:8080/","height":156},"executionInfo":{"status":"ok","timestamp":1717436619559,"user_tz":-120,"elapsed":578,"user":{"displayName":"toni Monleon","userId":"05269440626861479105"}},"outputId":"6c4463fa-ceef-4dbd-c758-d7176303cc78"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["[1] \"means:\"\n"]},{"output_type":"display_data","data":{"text/html":["
V2
-8.59176620688482e-16
V3
-6.77644630873763e-17
V4
8.0451760363661e-16
V5
-7.72049403184734e-17
V6
-4.07393534579009e-17
V7
-1.39556006322723e-17
V8
6.95826335844938e-17
V9
-1.04218629278997e-16
V10
-1.22136897486352e-16
V11
3.64937578439868e-17
V12
2.09374057963209e-16
V13
3.00345929462872e-16
V14
-1.03442937443795e-16
\n"],"text/markdown":"V2\n: -8.59176620688482e-16V3\n: -6.77644630873763e-17V4\n: 8.0451760363661e-16V5\n: -7.72049403184734e-17V6\n: -4.07393534579009e-17V7\n: -1.39556006322723e-17V8\n: 6.95826335844938e-17V9\n: -1.04218629278997e-16V10\n: -1.22136897486352e-16V11\n: 3.64937578439868e-17V12\n: 2.09374057963209e-16V13\n: 3.00345929462872e-16V14\n: -1.03442937443795e-16\n\n","text/latex":"\\begin{description*}\n\\item[V2] -8.59176620688482e-16\n\\item[V3] -6.77644630873763e-17\n\\item[V4] 8.0451760363661e-16\n\\item[V5] -7.72049403184734e-17\n\\item[V6] -4.07393534579009e-17\n\\item[V7] -1.39556006322723e-17\n\\item[V8] 6.95826335844938e-17\n\\item[V9] -1.04218629278997e-16\n\\item[V10] -1.22136897486352e-16\n\\item[V11] 3.64937578439868e-17\n\\item[V12] 2.09374057963209e-16\n\\item[V13] 3.00345929462872e-16\n\\item[V14] -1.03442937443795e-16\n\\end{description*}\n","text/plain":[" V2 V3 V4 V5 V6 \n","-8.591766e-16 -6.776446e-17 8.045176e-16 -7.720494e-17 -4.073935e-17 \n"," V7 V8 V9 V10 V11 \n","-1.395560e-17 6.958263e-17 -1.042186e-16 -1.221369e-16 3.649376e-17 \n"," V12 V13 V14 \n"," 2.093741e-16 3.003459e-16 -1.034429e-16 "]},"metadata":{}},{"output_type":"stream","name":"stdout","text":["[1] \"----------------------------------------------------------------------------\"\n","[1] \"----------------------------------------------------------------------------\"\n","[1] \"variances:\"\n"]},{"output_type":"display_data","data":{"text/html":["
V2
1
V3
1
V4
1
V5
1
V6
1
V7
1
V8
1
V9
1
V10
1
V11
1
V12
1
V13
1
V14
1
\n"],"text/markdown":"V2\n: 1V3\n: 1V4\n: 1V5\n: 1V6\n: 1V7\n: 1V8\n: 1V9\n: 1V10\n: 1V11\n: 1V12\n: 1V13\n: 1V14\n: 1\n\n","text/latex":"\\begin{description*}\n\\item[V2] 1\n\\item[V3] 1\n\\item[V4] 1\n\\item[V5] 1\n\\item[V6] 1\n\\item[V7] 1\n\\item[V8] 1\n\\item[V9] 1\n\\item[V10] 1\n\\item[V11] 1\n\\item[V12] 1\n\\item[V13] 1\n\\item[V14] 1\n\\end{description*}\n","text/plain":[" V2 V3 V4 V5 V6 V7 V8 V9 V10 V11 V12 V13 V14 \n"," 1 1 1 1 1 1 1 1 1 1 1 1 1 "]},"metadata":{}}]},{"cell_type":"markdown","source":["We see that the means of the standardised variables are all very tiny numbers and so are essentially equal to 0, and the standard deviations of the standardised variables are all equal to 1."],"metadata":{"id":"b3TkXvm5vMki"}},{"cell_type":"markdown","source":["#Principal Component Analysis\n","\n","(based on https://little-book-of-r-for-multivariate-analysis.readthedocs.io/en/latest/src/multivariateanalysis.html)\n","\n","See more about PCA in https://medium.com/machine-learning-researcher/dimensionality-reduction-pca-and-lda-6be91734f567\n","\n","**The purpose of principal component analysis is to find the best low-dimensional representation of the variation in a multivariate data set**.\n","\n","\n","\n","\n","![](https://miro.medium.com/v2/resize:fit:640/format:webp/1*z8ysJEwuCz-_WxisMg13LA.png)\n","\n","\n","For example, in the case of the wine data set, we have 13 chemical concentrations describing wine samples from three different cultivars. We can carry out a principal component analysis to investigate whether we can capture most of the variation between samples using a smaller number of new variables (principal components), where each of these new variables is a linear combination of all or some of the 13 chemical concentrations.\n","\n","\n","![](https://miro.medium.com/v2/resize:fit:640/format:webp/1*gLUHHLNL_kPY9666LlsPEQ.png)\n","\n","To carry out a principal component analysis (PCA) on a multivariate data set:\n","* the first step is often to standardise the variables under study using the “scale()” function (see above). This is necessary if the input variables have very different variances, which is true in this case as the concentrations of the 13 chemicals have very different variances (see above).\n","\n","* Once you have standardised your variables, you can carry out a principal component analysis using the “prcomp()” function in R.\n","\n","\n","For example, to standardise the concentrations of the 13 chemicals in the wine samples, and carry out a principal components analysis on the standardised concentrations, we type:"],"metadata":{"id":"FfEZHEODvo6h"}},{"cell_type":"code","source":[" standardisedconcentrations <- as.data.frame(scale(wine[2:14])) # standardise the variables\n","\n","\n","\n"," wine.pca <- prcomp(standardisedconcentrations) # do a PCA\n"," #other possibility is use\n"," #wine.pca <- prcomp(wine[2:14], scale=T) # do a PCA"],"metadata":{"id":"sDMv2jhavMcY","executionInfo":{"status":"ok","timestamp":1717497761792,"user_tz":-120,"elapsed":348,"user":{"displayName":"toni Monleon","userId":"05269440626861479105"}}},"execution_count":41,"outputs":[]},{"cell_type":"markdown","source":["You can get a summary of the principal component analysis results using the “summary()” function on the output of “prcomp()”:"],"metadata":{"id":"dSiO5yb9vMTy"}},{"cell_type":"code","source":["summary(wine.pca)"],"metadata":{"id":"D7P0ue1bvML5","colab":{"base_uri":"https://localhost:8080/","height":183},"executionInfo":{"status":"ok","timestamp":1717497764261,"user_tz":-120,"elapsed":354,"user":{"displayName":"toni Monleon","userId":"05269440626861479105"}},"outputId":"0c82af7f-d2de-496a-e6fe-554978f19df1"},"execution_count":42,"outputs":[{"output_type":"display_data","data":{"text/plain":["Importance of components:\n"," PC1 PC2 PC3 PC4 PC5 PC6 PC7\n","Standard deviation 2.169 1.5802 1.2025 0.95863 0.92370 0.80103 0.74231\n","Proportion of Variance 0.362 0.1921 0.1112 0.07069 0.06563 0.04936 0.04239\n","Cumulative Proportion 0.362 0.5541 0.6653 0.73599 0.80162 0.85098 0.89337\n"," PC8 PC9 PC10 PC11 PC12 PC13\n","Standard deviation 0.59034 0.53748 0.5009 0.47517 0.41082 0.32152\n","Proportion of Variance 0.02681 0.02222 0.0193 0.01737 0.01298 0.00795\n","Cumulative Proportion 0.92018 0.94240 0.9617 0.97907 0.99205 1.00000"]},"metadata":{}}]},{"cell_type":"markdown","source":["This gives us the standard deviation of each component, and the proportion of variance explained by each component. The standard deviation of the components is stored in a named element called “sdev” of the output variable made by “prcomp”:"],"metadata":{"id":"T-TSwMgpvMC6"}},{"cell_type":"code","source":["wine.pca$sdev #SDV FOR EACH OF THE 13 PRINCIPAL COMPONENTS COMPUTED (LINEAR ASSOCIATION OF ALL 13 ORIGINAL VARIABLES)"],"metadata":{"id":"3was0jRPvL4g","colab":{"base_uri":"https://localhost:8080/","height":34},"executionInfo":{"status":"ok","timestamp":1717497768084,"user_tz":-120,"elapsed":359,"user":{"displayName":"toni Monleon","userId":"05269440626861479105"}},"outputId":"346e7243-44e4-4f79-b23d-113fb4b38070"},"execution_count":43,"outputs":[{"output_type":"display_data","data":{"text/html":["\n","
  1. 2.16929717950087
  2. 1.58018155077547
  3. 1.2025273259733
  4. 0.958631276222941
  5. 0.923703512147873
  6. 0.801034975203289
  7. 0.742312812728591
  8. 0.590336652503682
  9. 0.53747552746396
  10. 0.500901669205374
  11. 0.475172221093246
  12. 0.410816546439585
  13. 0.321524393611011
\n"],"text/markdown":"1. 2.16929717950087\n2. 1.58018155077547\n3. 1.2025273259733\n4. 0.958631276222941\n5. 0.923703512147873\n6. 0.801034975203289\n7. 0.742312812728591\n8. 0.590336652503682\n9. 0.53747552746396\n10. 0.500901669205374\n11. 0.475172221093246\n12. 0.410816546439585\n13. 0.321524393611011\n\n\n","text/latex":"\\begin{enumerate*}\n\\item 2.16929717950087\n\\item 1.58018155077547\n\\item 1.2025273259733\n\\item 0.958631276222941\n\\item 0.923703512147873\n\\item 0.801034975203289\n\\item 0.742312812728591\n\\item 0.590336652503682\n\\item 0.53747552746396\n\\item 0.500901669205374\n\\item 0.475172221093246\n\\item 0.410816546439585\n\\item 0.321524393611011\n\\end{enumerate*}\n","text/plain":[" [1] 2.1692972 1.5801816 1.2025273 0.9586313 0.9237035 0.8010350 0.7423128\n"," [8] 0.5903367 0.5374755 0.5009017 0.4751722 0.4108165 0.3215244"]},"metadata":{}}]},{"cell_type":"markdown","source":["The total variance explained by the components is the sum of the variances of the components:"],"metadata":{"id":"SpgFEcL1vLv5"}},{"cell_type":"code","source":["sum((wine.pca$sdev)^2)"],"metadata":{"id":"NgYBK-WMvLl5","colab":{"base_uri":"https://localhost:8080/","height":34},"executionInfo":{"status":"ok","timestamp":1717497770262,"user_tz":-120,"elapsed":401,"user":{"displayName":"toni Monleon","userId":"05269440626861479105"}},"outputId":"a1f75e05-8569-4373-d7ef-3fb635c0df0d"},"execution_count":44,"outputs":[{"output_type":"display_data","data":{"text/html":["13"],"text/markdown":"13","text/latex":"13","text/plain":["[1] 13"]},"metadata":{}}]},{"cell_type":"markdown","source":["In this case, we see that the total variance is 13, which is equal to the number of standardised variables (13 variables). This is because for standardised data, the variance of each standardised variable is 1. The total variance is equal to the sum of the variances of the individual variables, and since the variance of each standardised variable is 1, the total variance should be equal to the number of variables (13 here)."],"metadata":{"id":"45ZY0382vLaS"}},{"cell_type":"markdown","source":["Deciding How Many Principal Components to Retain\n","In order to decide how many principal components should be retained, it is common to summarise the results of a principal components analysis by making a scree plot, which we can do in R using the “screeplot()” function:"],"metadata":{"id":"v9ByvfW7wDVA"}},{"cell_type":"code","source":["screeplot(wine.pca, type=\"lines\")"],"metadata":{"id":"K6ddH-FZvLMv","colab":{"base_uri":"https://localhost:8080/","height":437},"executionInfo":{"status":"ok","timestamp":1717497774246,"user_tz":-120,"elapsed":465,"user":{"displayName":"toni Monleon","userId":"05269440626861479105"}},"outputId":"f98d6a06-7c3f-44db-d12e-71470e143ebe"},"execution_count":45,"outputs":[{"output_type":"display_data","data":{"text/plain":["Plot with title “wine.pca”"],"image/png":"iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAMAAADKOT/pAAADAFBMVEUAAAABAQECAgIDAwME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"},"metadata":{"image/png":{"width":420,"height":420}}}]},{"cell_type":"markdown","source":["The most obvious change in slope in the scree plot occurs at component 4, which is the “elbow” of the scree plot. Therefore, it cound be argued based on the basis of the scree plot that the first three components should be retained.\n","\n","Another way of deciding how many components to retain is to use **Kaiser’s criterion**: that we should only retain principal components for which the variance is above 1 (when principal component analysis was applied to standardised data). We can check this by finding the variance of each of the principal components:"],"metadata":{"id":"uy8-sMx8vK73"}},{"cell_type":"code","source":["(wine.pca$sdev)^2 #NOW IS THE VARIANCE FOR EACH PRINCIPAL COMPONENT"],"metadata":{"id":"XGwkdQnNwJBO","colab":{"base_uri":"https://localhost:8080/","height":34},"executionInfo":{"status":"ok","timestamp":1717497803825,"user_tz":-120,"elapsed":383,"user":{"displayName":"toni Monleon","userId":"05269440626861479105"}},"outputId":"6f32f7b3-f7ff-495b-c709-f2fe003d541c"},"execution_count":46,"outputs":[{"output_type":"display_data","data":{"text/html":["\n","
  1. 4.70585025299042
  2. 2.49697373341116
  3. 1.4460719697125
  4. 0.918973923752824
  5. 0.853228178354317
  6. 0.641657031498934
  7. 0.551028311941032
  8. 0.348497363289253
  9. 0.288879942622662
  10. 0.25090248221273
  11. 0.225788639698689
  12. 0.168770234828548
  13. 0.103377935686929
\n"],"text/markdown":"1. 4.70585025299042\n2. 2.49697373341116\n3. 1.4460719697125\n4. 0.918973923752824\n5. 0.853228178354317\n6. 0.641657031498934\n7. 0.551028311941032\n8. 0.348497363289253\n9. 0.288879942622662\n10. 0.25090248221273\n11. 0.225788639698689\n12. 0.168770234828548\n13. 0.103377935686929\n\n\n","text/latex":"\\begin{enumerate*}\n\\item 4.70585025299042\n\\item 2.49697373341116\n\\item 1.4460719697125\n\\item 0.918973923752824\n\\item 0.853228178354317\n\\item 0.641657031498934\n\\item 0.551028311941032\n\\item 0.348497363289253\n\\item 0.288879942622662\n\\item 0.25090248221273\n\\item 0.225788639698689\n\\item 0.168770234828548\n\\item 0.103377935686929\n\\end{enumerate*}\n","text/plain":[" [1] 4.7058503 2.4969737 1.4460720 0.9189739 0.8532282 0.6416570 0.5510283\n"," [8] 0.3484974 0.2888799 0.2509025 0.2257886 0.1687702 0.1033779"]},"metadata":{}}]},{"cell_type":"markdown","source":["We see that the variance is above 1 for principal components 1, 2, and 3 (which have variances 4.71, 2.50, and 1.45, respectively). Therefore, using Kaiser’s criterion, we would retain the first three principal components.\n","\n","A third way to decide how many principal components to retain is to decide to keep the number of components required to explain at least some minimum amount of the total variance. For example, if it is important to explain at least 80% of the variance, we would retain the first five principal components, as we can see from the output of “summary(wine.pca)” that the first five principal components explain 80.2% of the variance (while the first four components explain just 73.6%, so are not sufficient)."],"metadata":{"id":"MJ7pLgQrwKuy"}},{"cell_type":"markdown","source":["Loadings for the Principal Components\n","\n","The loadings for the principal components are stored in a named element “rotation” of the variable returned by “prcomp()”. This contains a matrix with the loadings of each principal component, where the first column in the matrix contains the loadings for the first principal component, the second column contains the loadings for the second principal component, and so on.\n","\n","Therefore, to obtain the loadings for the **first principal component** in our analysis of the 13 chemical concentrations in wine samples, we type:"],"metadata":{"id":"TwPvy2v1wU5f"}},{"cell_type":"code","source":["wine.pca$rotation[,1]"],"metadata":{"id":"d4KW9AJKwKjX","colab":{"base_uri":"https://localhost:8080/","height":34},"executionInfo":{"status":"ok","timestamp":1717497850440,"user_tz":-120,"elapsed":405,"user":{"displayName":"toni Monleon","userId":"05269440626861479105"}},"outputId":"acb74eff-efb1-4251-93ae-e44f2b216151"},"execution_count":47,"outputs":[{"output_type":"display_data","data":{"text/html":["
V2
-0.144329395406011
V3
0.245187580257221
V4
0.00205106144437123
V5
0.239320405487535
V6
-0.141992041952987
V7
-0.39466084506663
V8
-0.422934296710059
V9
0.298533102954715
V10
-0.313429488307688
V11
0.0886167047247227
V12
-0.296714563586381
V13
-0.376167410738713
V14
-0.286752226896805
\n"],"text/markdown":"V2\n: -0.144329395406011V3\n: 0.245187580257221V4\n: 0.00205106144437123V5\n: 0.239320405487535V6\n: -0.141992041952987V7\n: -0.39466084506663V8\n: -0.422934296710059V9\n: 0.298533102954715V10\n: -0.313429488307688V11\n: 0.0886167047247227V12\n: -0.296714563586381V13\n: -0.376167410738713V14\n: -0.286752226896805\n\n","text/latex":"\\begin{description*}\n\\item[V2] -0.144329395406011\n\\item[V3] 0.245187580257221\n\\item[V4] 0.00205106144437123\n\\item[V5] 0.239320405487535\n\\item[V6] -0.141992041952987\n\\item[V7] -0.39466084506663\n\\item[V8] -0.422934296710059\n\\item[V9] 0.298533102954715\n\\item[V10] -0.313429488307688\n\\item[V11] 0.0886167047247227\n\\item[V12] -0.296714563586381\n\\item[V13] -0.376167410738713\n\\item[V14] -0.286752226896805\n\\end{description*}\n","text/plain":[" V2 V3 V4 V5 V6 V7 \n","-0.144329395 0.245187580 0.002051061 0.239320405 -0.141992042 -0.394660845 \n"," V8 V9 V10 V11 V12 V13 \n","-0.422934297 0.298533103 -0.313429488 0.088616705 -0.296714564 -0.376167411 \n"," V14 \n","-0.286752227 "]},"metadata":{}}]},{"cell_type":"markdown","source":["This means that the first principal component is a linear combination of the variables: -0.144*Z2 + 0.245*Z3 + 0.002*Z4 + 0.239*Z5 - 0.142*Z6 - 0.395*Z7 - 0.423*Z8 + 0.299*Z9 -0.313*Z10 + 0.089*Z11 - 0.297*Z12 - 0.376*Z13 - 0.287*Z14, where Z2, Z3, Z4...Z14 are the standardised versions of the variables V2, V3, V4...V14 (that each have mean of 0 and variance of 1).\n","\n","Note that the square of the loadings sum to 1, as this is a constraint used in calculating the loadings:"],"metadata":{"id":"qwoOJYVPwKY9"}},{"cell_type":"code","source":["sum((wine.pca$rotation[,1])^2)"],"metadata":{"id":"PDe7ZJQpwKP3","colab":{"base_uri":"https://localhost:8080/","height":34},"executionInfo":{"status":"ok","timestamp":1717497863778,"user_tz":-120,"elapsed":442,"user":{"displayName":"toni Monleon","userId":"05269440626861479105"}},"outputId":"b01b27a6-a57b-4039-c419-a4eb1bdbd9b2"},"execution_count":48,"outputs":[{"output_type":"display_data","data":{"text/html":["1"],"text/markdown":"1","text/latex":"1","text/plain":["[1] 1"]},"metadata":{}}]},{"cell_type":"markdown","source":["One of the first questions that we are going to carry out is to represent in a reduced dimension graph (the PCA), from the calculations obtained, the data of all the wines and of all the variables analyzed. It is what is called a biplot:"],"metadata":{"id":"ust3T7pfD5Sj"}},{"cell_type":"code","source":["biplot(wine.pca,cex=0.8)\n","abline(h = 0, v = 0, lty = 2, col = 8)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":437},"id":"mGmYkGmREJ-v","executionInfo":{"status":"ok","timestamp":1717497875433,"user_tz":-120,"elapsed":821,"user":{"displayName":"toni Monleon","userId":"05269440626861479105"}},"outputId":"5302e574-2578-43ba-db0b-bd75db52f042"},"execution_count":49,"outputs":[{"output_type":"display_data","data":{"text/plain":["plot without title"],"image/png":"iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAIAAAByhViMAAAACXBIWXMAABJ0AAASdAHeZh94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\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KWWIX7zzTf0yIgRIyZMmFBRUaGrq5uW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AeMQvwAEBIAhAEARBBgzGwC7dSO9NjmOx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\nhwDKS1tb+86dO7GxsUePHk1PT5fehUOn052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is a type of scatterplot used in PCA. In this special plot, the original data is represented by principal components that explain the majority of the data variance using the loading vectors and PC scores.\n","\n","In this plot is possible see the different variables and the wines, distributed in function of the loadings. But, what is the PC1 and PC2 interpretation?\n","\n","To interpret each principal components, examine the magnitude and direction of the coefficients for the original variables. The larger the absolute value of the coefficient, the more important the corresponding variable is in calculating the component. Seehttps://bioturing.medium.com/how-to-read-pca-biplots-and-scree-plots-186246aae063"],"metadata":{"id":"JSJYSJDGFKE9"}},{"cell_type":"markdown","source":["____\n","**THIS SECTION IS ONLY TO EXPAND THEORETICAL CONCEPTS AND TO LEARN HOW THIS STATISTICAL METHOD WORKS**\n","To calculate the values of the first principal component, we can define our own function to calculate a principal component given the loadings and the input variables’ values:"],"metadata":{"id":"qJHJ-y1hwKGW"}},{"cell_type":"code","source":["calcpc <- function(variables,loadings)\n"," {\n"," # find the number of samples in the data set\n"," as.data.frame(variables)\n"," numsamples <- nrow(variables)\n"," # make a vector to store the component\n"," pc <- numeric(numsamples)\n"," # find the number of variables\n"," numvariables <- length(variables)\n"," # calculate the value of the component for each sample\n"," for (i in 1:numsamples)\n"," {\n"," valuei <- 0\n"," for (j in 1:numvariables)\n"," {\n"," valueij <- variables[i,j]\n"," loadingj <- loadings[j]\n"," valuei <- valuei + (valueij * loadingj)\n"," }\n"," pc[i] <- valuei\n"," }\n"," return(pc)\n"," }"],"metadata":{"id":"CPbNfrrgwJ6Y","executionInfo":{"status":"ok","timestamp":1717497967547,"user_tz":-120,"elapsed":390,"user":{"displayName":"toni Monleon","userId":"05269440626861479105"}}},"execution_count":50,"outputs":[]},{"cell_type":"markdown","source":["We can then use the function to calculate the values of the first principal component for each sample in our wine data:"],"metadata":{"id":"wu3L_tJPwlhZ"}},{"cell_type":"code","source":["calcpc(standardisedconcentrations, wine.pca$rotation[,1])"],"metadata":{"id":"HOuwoNSVwkbu","colab":{"base_uri":"https://localhost:8080/","height":190},"executionInfo":{"status":"ok","timestamp":1717497971616,"user_tz":-120,"elapsed":407,"user":{"displayName":"toni Monleon","userId":"05269440626861479105"}},"outputId":"3d196be8-2194-4ad9-d08a-918e2e782859"},"execution_count":51,"outputs":[{"output_type":"display_data","data":{"text/html":["\n","
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2.53183508\n"," [85] -0.83297044 -0.78568828 0.80456258 0.55647288 1.11197430 0.55415961\n"," [91] 1.34548982 1.56008180 1.92711944 -0.74456561 -0.95476209 -2.53670943\n"," [97] 0.54242248 -1.02814946 -2.24557492 -1.40624916 -0.79547585 0.54798592\n","[103] 0.16072037 0.65793897 -0.39125074 1.76751314 0.36523707 1.61611371\n","[109] -0.08230361 -1.57383547 -1.41657326 0.27791878 1.29947929 0.45578615\n","[115] 0.49279573 -0.48071836 0.25217752 0.10692601 2.42616867 0.54953935\n","[121] -0.73754141 -1.33256273 1.17377592 0.46103449 -0.97572169 0.09653741\n","[127] -0.03837888 1.59266578 0.47821593 1.78779033 1.32336859 2.37779336\n","[133] 2.92867865 2.14077227 2.36320318 3.05522315 3.90473898 3.92539034\n","[139] 3.08557209 2.36779237 2.77099630 2.28012931 2.97723506 2.36851341\n","[145] 2.20364930 2.61823528 4.26859758 3.57256360 2.79916760 2.89150275\n","[151] 2.31420887 2.54265841 1.80744271 2.75238051 2.72945105 3.59472857\n","[157] 2.88169708 3.38261413 1.04523342 1.60538369 3.13428951 2.23385546\n","[163] 2.83966343 2.59019044 2.94100316 3.52010248 2.39934228 2.92084537\n","[169] 2.17527658 2.37423037 3.20258311 3.66757294 2.45862032 3.36104305\n","[175] 2.59463669 2.67030685 2.38030254 3.19973210"]},"metadata":{}}]},{"cell_type":"markdown","source":["**end of theoretical section**\n","\n","----\n","\n","In fact, the values of the first principal component are stored in the variable wine.pca$x[,1] that was returned by the “prcomp()” function, so we can compare those values to the ones that we calculated, and they should agree:"],"metadata":{"id":"iGzJ30M0wkTb"}},{"cell_type":"code","source":["wine.pca$x[,1]"],"metadata":{"id":"km5qtBWYwkEm","colab":{"base_uri":"https://localhost:8080/","height":190},"executionInfo":{"status":"ok","timestamp":1717497980404,"user_tz":-120,"elapsed":474,"user":{"displayName":"toni Monleon","userId":"05269440626861479105"}},"outputId":"fe109883-bf3a-43f2-e940-60e302c7f227"},"execution_count":52,"outputs":[{"output_type":"display_data","data":{"text/html":["\n","
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2.20364929520703\n\\item 2.61823527696862\n\\item 4.2685975768988\n\\item 3.57256359852983\n\\item 2.79916760396144\n\\item 2.89150275052853\n\\item 2.31420887085633\n\\item 2.54265841277236\n\\item 1.80744270572725\n\\item 2.75238050659445\n\\item 2.72945104659533\n\\item 3.59472856998581\n\\item 2.88169707501281\n\\item 3.38261412804098\n\\item 1.0452334187292\n\\item 1.60538368798997\n\\item 3.13428951191097\n\\item 2.23385545791818\n\\item 2.8396634261414\n\\item 2.59019044004634\n\\item 2.94100315593216\n\\item 3.52010247940368\n\\item 2.39934228363786\n\\item 2.92084537245454\n\\item 2.17527658443826\n\\item 2.37423036799742\n\\item 3.20258311215208\n\\item 3.66757293755117\n\\item 2.45862032495413\n\\item 3.3610430460834\n\\item 2.59463669227798\n\\item 2.67030684549029\n\\item 2.38030254326328\n\\item 3.1997321036619\n\\end{enumerate*}\n","text/plain":[" [1] -3.30742097 -2.20324981 -2.50966069 -3.74649719 -1.00607049 -3.04167373\n"," [7] -2.44220051 -2.05364379 -2.50381135 -2.74588238 -3.46994837 -1.74981688\n"," [13] -2.10751729 -3.44842921 -4.30065228 -2.29870383 -2.16584568 -1.89362947\n"," [19] -3.53202167 -2.07865856 -3.11561376 -1.08351361 -2.52809263 -1.64036108\n"," [25] -1.75662066 -0.98729406 -1.77028387 -1.23194878 -2.18225047 -2.24976267\n"," [31] -2.49318704 -2.66987964 -1.62399801 -1.89733870 -1.40642118 -1.89847087\n"," [37] -1.38096669 -1.11905070 -1.49796891 -2.52268490 -2.58081526 -0.66660159\n"," [43] -3.06216898 -0.46090897 -2.09544094 -1.13297020 -2.71893118 -2.81340300\n"," [49] -2.00419725 -2.69987528 -3.20587409 -2.85091773 -3.49574328 -2.21853316\n"," [55] -2.14094846 -2.46238340 -2.73380617 -2.16762631 -3.13054925 0.92596992\n"," [61] 1.53814123 1.83108449 -0.03052074 -2.04449433 0.60796583 -0.89769555\n"," [67] -2.24218226 -0.18286818 0.81051865 -1.97006319 1.56779366 -1.65301884\n"," [73] 0.72333196 -2.55501977 -1.82741266 0.86555129 -0.36897357 1.45327752\n"," [79] -1.25937829 -0.37509228 -0.75992026 -1.03166776 0.49348469 2.53183508\n"," [85] -0.83297044 -0.78568828 0.80456258 0.55647288 1.11197430 0.55415961\n"," [91] 1.34548982 1.56008180 1.92711944 -0.74456561 -0.95476209 -2.53670943\n"," [97] 0.54242248 -1.02814946 -2.24557492 -1.40624916 -0.79547585 0.54798592\n","[103] 0.16072037 0.65793897 -0.39125074 1.76751314 0.36523707 1.61611371\n","[109] -0.08230361 -1.57383547 -1.41657326 0.27791878 1.29947929 0.45578615\n","[115] 0.49279573 -0.48071836 0.25217752 0.10692601 2.42616867 0.54953935\n","[121] -0.73754141 -1.33256273 1.17377592 0.46103449 -0.97572169 0.09653741\n","[127] -0.03837888 1.59266578 0.47821593 1.78779033 1.32336859 2.37779336\n","[133] 2.92867865 2.14077227 2.36320318 3.05522315 3.90473898 3.92539034\n","[139] 3.08557209 2.36779237 2.77099630 2.28012931 2.97723506 2.36851341\n","[145] 2.20364930 2.61823528 4.26859758 3.57256360 2.79916760 2.89150275\n","[151] 2.31420887 2.54265841 1.80744271 2.75238051 2.72945105 3.59472857\n","[157] 2.88169708 3.38261413 1.04523342 1.60538369 3.13428951 2.23385546\n","[163] 2.83966343 2.59019044 2.94100316 3.52010248 2.39934228 2.92084537\n","[169] 2.17527658 2.37423037 3.20258311 3.66757294 2.45862032 3.36104305\n","[175] 2.59463669 2.67030685 2.38030254 3.19973210"]},"metadata":{}}]},{"cell_type":"markdown","source":["We see that they do agree.\n","\n","The first principal component has highest (in absolute value) loadings for V8 (-0.423), V7 (-0.395), V13 (-0.376), V10 (-0.313), V12 (-0.297), V14 (-0.287), V9 (0.299), V3 (0.245), and V5 (0.239). The loadings for V8, V7, V13, V10, V12 and V14 are negative, while those for V9, V3, and V5 are positive. Therefore, an interpretation of the first principal component is that it represents a contrast between the concentrations of V8, V7, V13, V10, V12, and V14, and the concentrations of V9, V3 and V5.\n","\n","Similarly, we can obtain the loadings for the second principal component by typing:"],"metadata":{"id":"R3pbOM9kwjzs"}},{"cell_type":"code","source":["wine.pca$rotation[,2]"],"metadata":{"id":"vwqtuwSCw3R_","colab":{"base_uri":"https://localhost:8080/","height":34},"executionInfo":{"status":"ok","timestamp":1717498001059,"user_tz":-120,"elapsed":373,"user":{"displayName":"toni Monleon","userId":"05269440626861479105"}},"outputId":"e84384bc-1e44-4b74-d7fb-3b9e96371ab1"},"execution_count":53,"outputs":[{"output_type":"display_data","data":{"text/html":["
V2
-0.483651547817214
V3
-0.224930934627844
V4
-0.316068814025316
V5
0.0105905022881906
V6
-0.299634003237862
V7
-0.0650395118192797
V8
0.00335981210030731
V9
-0.0287794881129867
V10
-0.0393017222897331
V11
-0.529995672070043
V12
0.279235147924282
V13
0.164496192835784
V14
-0.364902831798082
\n"],"text/markdown":"V2\n: -0.483651547817214V3\n: -0.224930934627844V4\n: -0.316068814025316V5\n: 0.0105905022881906V6\n: -0.299634003237862V7\n: -0.0650395118192797V8\n: 0.00335981210030731V9\n: -0.0287794881129867V10\n: -0.0393017222897331V11\n: -0.529995672070043V12\n: 0.279235147924282V13\n: 0.164496192835784V14\n: -0.364902831798082\n\n","text/latex":"\\begin{description*}\n\\item[V2] -0.483651547817214\n\\item[V3] -0.224930934627844\n\\item[V4] -0.316068814025316\n\\item[V5] 0.0105905022881906\n\\item[V6] -0.299634003237862\n\\item[V7] -0.0650395118192797\n\\item[V8] 0.00335981210030731\n\\item[V9] -0.0287794881129867\n\\item[V10] -0.0393017222897331\n\\item[V11] -0.529995672070043\n\\item[V12] 0.279235147924282\n\\item[V13] 0.164496192835784\n\\item[V14] -0.364902831798082\n\\end{description*}\n","text/plain":[" V2 V3 V4 V5 V6 V7 \n","-0.483651548 -0.224930935 -0.316068814 0.010590502 -0.299634003 -0.065039512 \n"," V8 V9 V10 V11 V12 V13 \n"," 0.003359812 -0.028779488 -0.039301722 -0.529995672 0.279235148 0.164496193 \n"," V14 \n","-0.364902832 "]},"metadata":{}}]},{"cell_type":"markdown","source":["This means that the second principal component is a linear combination of the variables: 0.484*Z2 + 0.225*Z3 + 0.316*Z4 - 0.011*Z5 + 0.300*Z6 + 0.065*Z7 - 0.003*Z8 + 0.029*Z9 + 0.039*Z10 + 0.530*Z11 - 0.279*Z12 - 0.164*Z13 + 0.365*Z14, where Z1, Z2, Z3...Z14 are the standardised versions of variables V2, V3, ... V14 that each have mean 0 and variance 1.\n","\n","Note that the square of the loadings sum to 1, as above:"],"metadata":{"id":"nm__lunWw18p"}},{"cell_type":"code","source":["sum((wine.pca$rotation[,2])^2)"],"metadata":{"id":"Dqru7qBLw1xH","colab":{"base_uri":"https://localhost:8080/","height":34},"executionInfo":{"status":"ok","timestamp":1717498004148,"user_tz":-120,"elapsed":374,"user":{"displayName":"toni Monleon","userId":"05269440626861479105"}},"outputId":"f42039ca-474b-4e56-913f-f8fa52deda5c"},"execution_count":54,"outputs":[{"output_type":"display_data","data":{"text/html":["0.999999999999999"],"text/markdown":"0.999999999999999","text/latex":"0.999999999999999","text/plain":["[1] 1"]},"metadata":{}}]},{"cell_type":"markdown","source":["The second principal component has highest loadings for V11 (0.530), V2 (0.484), V14 (0.365), V4 (0.316), V6 (0.300), V12 (-0.279), and V3 (0.225). The loadings for V11, V2, V14, V4, V6 and V3 are positive, while the loading for V12 is negative. Therefore, an interpretation of the second principal component is that it represents a contrast between the concentrations of V11, V2, V14, V4, V6 and V3, and the concentration of V12. Note that the loadings for V11 (0.530) and V2 (0.484) are the largest, so the contrast is mainly between the concentrations of V11 and V2, and the concentration of V12."],"metadata":{"id":"SNGdI3dPw1g9"}},{"cell_type":"markdown","source":["Scatterplots of the Principal Components\n","The values of the principal components are stored in a named element “x” of the variable returned by “prcomp()”. This contains a matrix with the principal components, where the first column in the matrix contains the first principal component, the second column the second component, and so on.\n","\n","Thus, in our example, “wine.pca$x[,1]” contains the first principal component, and “wine.pca$x[,2]” contains the second principal component.\n","\n","We can make a scatterplot of the first two principal components, and label the data points with the cultivar that the wine samples come from, by typing:"],"metadata":{"id":"WIVGUjo0xCKv"}},{"cell_type":"code","source":["plot(wine.pca$x[,1],wine.pca$x[,2]) # make a scatterplot\n","text(wine.pca$x[,1],wine.pca$x[,2], wine$V1, cex=0.7, pos=4, col=\"red\") # add labels\n","abline(h = 0, v = 0, lty = 2, col = 8)\n","\n","\n","#and the biplot\n","biplot(wine.pca,cex=0.8)\n","abline(h = 0, v = 0, lty = 2, col = 8)"],"metadata":{"id":"HhsawLJJw1K7","colab":{"base_uri":"https://localhost:8080/","height":857},"executionInfo":{"status":"ok","timestamp":1717498013165,"user_tz":-120,"elapsed":970,"user":{"displayName":"toni Monleon","userId":"05269440626861479105"}},"outputId":"9b2def74-2c3e-49df-c628-1f00c39f45e2"},"execution_count":55,"outputs":[{"output_type":"display_data","data":{"text/plain":["plot without 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PgUCHYAAEKQjsoTioqKv//++8qVKxMTE7Ozs83NzW1tbaXg4GUA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\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abmR7OzDc3XNnJoDkALQbCTbAh2UNeL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\nTg6wtATR0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l7MmTJ1JSUjIyMj/88MOq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\nnCyy5xdkkyBJSOwYjl+pWuihgoIC7NoAACAVZ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ADtVWFhYVxcXHJysre3NyGkT58+QqEwKCho5syZallaBwDAPFhj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\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KWWIX7zzTf0yIgRIyZMmFBRUaGrq5uW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AeMQvwAEBIAhAEARBBgzGwC7dSO9NjmOx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\nAElFTkSuQmCC"},"metadata":{"image/png":{"width":420,"height":420}}}]},{"cell_type":"markdown","source":["The scatterplot shows the first principal component on the x-axis, and the second principal component on the y-axis. We can see from the scatterplot that wine samples of cultivar 1 have much lower values of the first principal component than wine samples of cultivar 3. Therefore, the first principal component separates wine samples of cultivars 1 from those of cultivar 3.\n","\n","We can also see that wine samples of cultivar 2 have much higher values of the second principal component than wine samples of cultivars 1 and 3. Therefore, the second principal component separates samples of cultivar 2 from samples of cultivars 1 and 3.\n","\n","Therefore, the first two principal components are reasonably useful for distinguishing wine samples of the three different cultivars.\n","\n","Above, we interpreted the first principal component as a contrast between the concentrations of V8, V7, V13, V10, V12, and V14, and the concentrations of V9, V3 and V5. We can check whether this makes sense in terms of the concentrations of these chemicals in the different cultivars, by printing out the means of the standardised concentration variables in each cultivar, using the “printMeanAndSdByGroup()” function (see above):"],"metadata":{"id":"1JotAkNBwJsS"}},{"cell_type":"code","source":["#However, for convenience, you might want to use the function “printMeanAndSdByGroup()” below, which prints out the mean and standard deviation of the variables for each group in your data set:\n","\n","printMeanAndSdByGroup <- function(variables,groupvariable)\n"," {\n"," # find the names of the variables\n"," variablenames <- c(names(groupvariable),names(as.data.frame(variables)))\n"," # within each group, find the mean of each variable\n"," groupvariable <- groupvariable[,1] # ensures groupvariable is not a list\n"," means <- aggregate(as.matrix(variables) ~ groupvariable, FUN = mean)\n"," names(means) <- variablenames\n"," print(paste(\"Means:\"))\n"," print(means)\n"," # within each group, find the standard deviation of each variable:\n"," sds <- aggregate(as.matrix(variables) ~ groupvariable, FUN = sd)\n"," names(sds) <- variablenames\n"," print(paste(\"Standard deviations:\"))\n"," print(sds)\n"," # within each group, find the number of samples:\n"," samplesizes <- aggregate(as.matrix(variables) ~ groupvariable, FUN = length)\n"," names(samplesizes) <- variablenames\n"," print(paste(\"Sample sizes:\"))\n"," print(samplesizes)\n"," }"],"metadata":{"id":"Vr2QxYbH73B_"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["printMeanAndSdByGroup(standardisedconcentrations,wine[1])"],"metadata":{"id":"GGgyng0IxaUT","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1717436741339,"user_tz":-120,"elapsed":365,"user":{"displayName":"toni Monleon","userId":"05269440626861479105"}},"outputId":"e981d936-9d01-4175-9781-98c75bc9ebe8"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["[1] \"Means:\"\n"," V1 V2 V3 V4 V5 V6 V7\n","1 1 0.9166093 -0.2915199 0.3246886 -0.7359212 0.46192317 0.87090552\n","2 2 -0.8892116 -0.3613424 -0.4437061 0.2225094 -0.36354162 -0.05790375\n","3 3 0.1886265 0.8928122 0.2572190 0.5754413 -0.03004191 -0.98483874\n"," V8 V9 V10 V11 V12 V13\n","1 0.95419225 -0.57735640 0.5388633 0.2028288 0.4575567 0.7691811\n","2 0.05163434 0.01452785 0.0688079 -0.8503999 0.4323908 0.2446043\n","3 -1.24923710 0.68817813 -0.7641311 1.0085728 -1.2019916 -1.3072623\n"," V14\n","1 1.1711967\n","2 -0.7220731\n","3 -0.3715295\n","[1] \"Standard deviations:\"\n"," V1 V2 V3 V4 V5 V6 V7 V8\n","1 1 0.5692415 0.6163463 0.8280333 0.7624716 0.7350927 0.5416007 0.3979478\n","2 2 0.6626591 0.9090742 1.1498967 1.0030563 1.1730101 0.8713912 0.7065071\n","3 3 0.6531461 0.9738258 0.6732065 0.6761844 0.7625055 0.5703767 0.2938394\n"," V9 V10 V11 V12 V13 V14\n","1 0.5628555 0.7200189 0.5342623 0.5096112 0.5029315 0.7034472\n","2 0.9960462 1.0519061 0.3989712 0.8878480 0.6994087 0.4992299\n","3 0.9974790 0.7142999 0.9968323 0.5006795 0.3832607 0.3654948\n","[1] \"Sample sizes:\"\n"," V1 V2 V3 V4 V5 V6 V7 V8 V9 V10 V11 V12 V13 V14\n","1 1 59 59 59 59 59 59 59 59 59 59 59 59 59\n","2 2 71 71 71 71 71 71 71 71 71 71 71 71 71\n","3 3 48 48 48 48 48 48 48 48 48 48 48 48 48\n"]}]},{"cell_type":"markdown","source":["Does it make sense that the first principal component can separate cultivar 1 from cultivar 3? In cultivar 1, the mean values of V8 (0.954), V7 (0.871), V13 (0.769), V10 (0.539), V12 (0.458) and V14 (1.171) are very high compared to the mean values of V9 (-0.577), V3 (-0.292) and V5 (-0.736). In cultivar 3, the mean values of V8 (-1.249), V7 (-0.985), V13 (-1.307), V10 (-0.764), V12 (-1.202) and V14 (-0.372) are very low compared to the mean values of V9 (0.688), V3 (0.893) and V5 (0.575). Therefore, it does make sense that principal component 1 is a contrast between the concentrations of V8, V7, V13, V10, V12, and V14, and the concentrations of V9, V3 and V5; and that principal component 1 can separate cultivar 1 from cultivar 3.\n","\n","Above, we intepreted the second principal component as a contrast between the concentrations of V11, V2, V14, V4, V6 and V3, and the concentration of V12. In the light of the mean values of these variables in the different cultivars, does it make sense that the second principal component can separate cultivar 2 from cultivars 1 and 3? In cultivar 1, the mean values of V11 (0.203), V2 (0.917), V14 (1.171), V4 (0.325), V6 (0.462) and V3 (-0.292) are not very different from the mean value of V12 (0.458). In cultivar 3, the mean values of V11 (1.009), V2 (0.189), V14 (-0.372), V4 (0.257), V6 (-0.030) and V3 (0.893) are also not very different from the mean value of V12 (-1.202). In contrast, in cultivar 2, the mean values of V11 (-0.850), V2 (-0.889), V14 (-0.722), V4 (-0.444), V6 (-0.364) and V3 (-0.361) are much less than the mean value of V12 (0.432). Therefore, it makes sense that principal component is a contrast between the concentrations of V11, V2, V14, V4, V6 and V3, and the concentration of V12; and that principal component 2 can separate cultivar 2 from cultivars 1 and 3."],"metadata":{"id":"jostQXztxXik"}},{"cell_type":"markdown","source":["#Linear Discriminant Analysis\n","\n","(based on https://little-book-of-r-for-multivariate-analysis.readthedocs.io/en/latest/src/multivariateanalysis.html)\n","\n","See more in http://www.sthda.com/english/articles/36-classification-methods-essentials/146-discriminant-analysis-essentials-in-r/ and in https://rpubs.com/Nolan/298913\n","\n","\n","\n","\n","Discriminant analysis is a classification method. It assumes that different classes generate data based on different Gaussian distributions. To train (create) a classifier, the fitting function estimates the parameters of a Gaussian distribution for each class (see Creating Discriminant Analysis Model).\n","\n"],"metadata":{"id":"D4rihHYJFNpJ"}},{"cell_type":"markdown","source":["\n","\n","\n","![](https://uc-r.github.io/public/images/analytics/discriminant_analysis/LDA.jpg)\n","\n","\n","The purpose of principal component analysis is to find the best low-dimensional representation of the variation in a multivariate data set. For example, in the wine data set, we have 13 chemical concentrations describing wine samples from three cultivars. By carrying out a principal component analysis, we found that most of the variation in the chemical concentrations between the samples can be captured using the first two principal components, where each of the principal components is a particular linear combination of the 13 chemical concentrations.\n","\n","**The purpose of linear discriminant analysis (LDA) is have a supervised method to classify the samples using the numerical variables, without knowing the group. For it in LDA we find the linear combinations of the original variables (the 13 chemical concentrations here) that gives the best possible separation between the groups (wine cultivars here) in our data set.**\n","\n","Linear discriminant analysis is also known as “canonical discriminant analysis”, or simply “discriminant analysis”.\n","\n","If we want to separate the wines by cultivar, the wines come from three different cultivars, so the number of groups (G) is 3, and the number of variables is 13 (13 chemicals’ concentrations; p = 13). **The maximum number of useful discriminant functions that can separate the wines by cultivar is the minimum of G-1 and p variables, and so in this case it is the minimum of 2 and 13, which is 2.** Thus, we can find at most 2 useful discriminant functions to separate the wines by cultivar, using the 13 chemical concentration variables.\n","\n","You can carry out a linear discriminant analysis using the “lda()” function from the R “MASS” package. To use this function, we first need to install the “MASS” R package (for instructions on how to install an R package, see How to install an R package).\n","\n","See more theory in https://medium.com/machine-learning-researcher/dimensionality-reduction-pca-and-lda-6be91734f567\n","\n"],"metadata":{"id":"H6Iz542jxkxq"}},{"cell_type":"markdown","source":["Let' s go to recover the wine dataframe (ONLY IF IS NECESSARY)"],"metadata":{"id":"_10QYxXXE4UU"}},{"cell_type":"code","source":["#wine example\n"," wine <- read.table(\"http://archive.ics.uci.edu/ml/machine-learning-databases/wine/wine.data\",\n"," sep=\",\")\n","\n"," head(wine) #without variables definition\n"," #tail(wine)\n","\n","#defining the variable name, when it is convenient to do so, for example to interpret the variables\n","#names(wine)<- c(\"groups\",\"Alcohol\",\"Malic acid\",\"Ash\",\"Alcalinity of ash\",\"Magnesium\",\"Total phenols\", \"Flavanoids\", \"Nonflavanoid phenols\".\"Proanthocyanins\",\"Color intensity\",\n","# \"Hue\", \"OD280/OD315 of diluted wines\",\"Proline\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":286},"id":"whrpQzXqE1WY","executionInfo":{"status":"ok","timestamp":1717498092292,"user_tz":-120,"elapsed":785,"user":{"displayName":"toni Monleon","userId":"05269440626861479105"}},"outputId":"c10cd216-ec2d-4891-fbea-7caef6402207"},"execution_count":56,"outputs":[{"output_type":"display_data","data":{"text/html":["\n","\n","\n","\t\n","\t\n","\n","\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\n","
A data.frame: 6 × 14
V1V2V3V4V5V6V7V8V9V10V11V12V13V14
<int><dbl><dbl><dbl><dbl><int><dbl><dbl><dbl><dbl><dbl><dbl><dbl><int>
1114.231.712.4315.61272.803.060.282.295.641.043.921065
2113.201.782.1411.21002.652.760.261.284.381.053.401050
3113.162.362.6718.61012.803.240.302.815.681.033.171185
4114.371.952.5016.81133.853.490.242.187.800.863.451480
5113.242.592.8721.01182.802.690.391.824.321.042.93 735
6114.201.762.4515.21123.273.390.341.976.751.052.851450
\n"],"text/markdown":"\nA data.frame: 6 × 14\n\n| | V1 <int> | V2 <dbl> | V3 <dbl> | V4 <dbl> | V5 <dbl> | V6 <int> | V7 <dbl> | V8 <dbl> | V9 <dbl> | V10 <dbl> | V11 <dbl> | V12 <dbl> | V13 <dbl> | V14 <int> |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | 1 | 14.23 | 1.71 | 2.43 | 15.6 | 127 | 2.80 | 3.06 | 0.28 | 2.29 | 5.64 | 1.04 | 3.92 | 1065 |\n| 2 | 1 | 13.20 | 1.78 | 2.14 | 11.2 | 100 | 2.65 | 2.76 | 0.26 | 1.28 | 4.38 | 1.05 | 3.40 | 1050 |\n| 3 | 1 | 13.16 | 2.36 | 2.67 | 18.6 | 101 | 2.80 | 3.24 | 0.30 | 2.81 | 5.68 | 1.03 | 3.17 | 1185 |\n| 4 | 1 | 14.37 | 1.95 | 2.50 | 16.8 | 113 | 3.85 | 3.49 | 0.24 | 2.18 | 7.80 | 0.86 | 3.45 | 1480 |\n| 5 | 1 | 13.24 | 2.59 | 2.87 | 21.0 | 118 | 2.80 | 2.69 | 0.39 | 1.82 | 4.32 | 1.04 | 2.93 | 735 |\n| 6 | 1 | 14.20 | 1.76 | 2.45 | 15.2 | 112 | 3.27 | 3.39 | 0.34 | 1.97 | 6.75 | 1.05 | 2.85 | 1450 |\n\n","text/latex":"A data.frame: 6 × 14\n\\begin{tabular}{r|llllllllllllll}\n & V1 & V2 & V3 & V4 & V5 & V6 & V7 & V8 & V9 & V10 & V11 & V12 & V13 & V14\\\\\n & & & & & & & & & & & & & & \\\\\n\\hline\n\t1 & 1 & 14.23 & 1.71 & 2.43 & 15.6 & 127 & 2.80 & 3.06 & 0.28 & 2.29 & 5.64 & 1.04 & 3.92 & 1065\\\\\n\t2 & 1 & 13.20 & 1.78 & 2.14 & 11.2 & 100 & 2.65 & 2.76 & 0.26 & 1.28 & 4.38 & 1.05 & 3.40 & 1050\\\\\n\t3 & 1 & 13.16 & 2.36 & 2.67 & 18.6 & 101 & 2.80 & 3.24 & 0.30 & 2.81 & 5.68 & 1.03 & 3.17 & 1185\\\\\n\t4 & 1 & 14.37 & 1.95 & 2.50 & 16.8 & 113 & 3.85 & 3.49 & 0.24 & 2.18 & 7.80 & 0.86 & 3.45 & 1480\\\\\n\t5 & 1 & 13.24 & 2.59 & 2.87 & 21.0 & 118 & 2.80 & 2.69 & 0.39 & 1.82 & 4.32 & 1.04 & 2.93 & 735\\\\\n\t6 & 1 & 14.20 & 1.76 & 2.45 & 15.2 & 112 & 3.27 & 3.39 & 0.34 & 1.97 & 6.75 & 1.05 & 2.85 & 1450\\\\\n\\end{tabular}\n","text/plain":[" V1 V2 V3 V4 V5 V6 V7 V8 V9 V10 V11 V12 V13 V14 \n","1 1 14.23 1.71 2.43 15.6 127 2.80 3.06 0.28 2.29 5.64 1.04 3.92 1065\n","2 1 13.20 1.78 2.14 11.2 100 2.65 2.76 0.26 1.28 4.38 1.05 3.40 1050\n","3 1 13.16 2.36 2.67 18.6 101 2.80 3.24 0.30 2.81 5.68 1.03 3.17 1185\n","4 1 14.37 1.95 2.50 16.8 113 3.85 3.49 0.24 2.18 7.80 0.86 3.45 1480\n","5 1 13.24 2.59 2.87 21.0 118 2.80 2.69 0.39 1.82 4.32 1.04 2.93 735\n","6 1 14.20 1.76 2.45 15.2 112 3.27 3.39 0.34 1.97 6.75 1.05 2.85 1450"]},"metadata":{}}]},{"cell_type":"code","source":["#install.packages(\"mass\") #is not necessary IN COLAB"],"metadata":{"id":"_K8qTNvaxQ3M"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["For example, to carry out a linear discriminant analysis (lda) using the 13 chemical concentrations in the wine samples to classify one of the three groups of cultivars, we type:"],"metadata":{"id":"GelSiXhF3pKv"}},{"cell_type":"code","source":[" library(\"MASS\") # load the MASS package\n"," #using the linear discriminant methos (Fisher discriminant: LDA)\n"," wine.lda <- lda(wine$V1 ~ wine$V2 + wine$V3 + wine$V4 + wine$V5 + wine$V6 + wine$V7 +\n"," wine$V8 + wine$V9 + wine$V10 + wine$V11 + wine$V12 + wine$V13 +\n"," wine$V14) #remenber that wine$V1 is a qualitative variable that constins 3 groups (cultivars)\n"," #wine.lda is our discriminant model (classification algorithm)"],"metadata":{"id":"tTJYrcUMxOsM","executionInfo":{"status":"ok","timestamp":1717498101794,"user_tz":-120,"elapsed":388,"user":{"displayName":"toni Monleon","userId":"05269440626861479105"}},"colab":{"base_uri":"https://localhost:8080/"},"outputId":"e6870024-ac87-424f-bcd9-97c9ab16c0ae"},"execution_count":57,"outputs":[{"output_type":"stream","name":"stderr","text":["\n","Attaching package: ‘MASS’\n","\n","\n","The following object is masked from ‘package:dplyr’:\n","\n"," select\n","\n","\n"]}]},{"cell_type":"markdown","source":["A good way to observe the results is by representing the 3 cultivars in a reduced space with 2 dimensions (LD1, LD2). these dimensions are linear combinations of all the variables used and allow for maximum separation between groups. We type:"],"metadata":{"id":"5sWb8I1JHAAA"}},{"cell_type":"code","source":["#represent LDA functions\n","plot(wine.lda, col = as.integer(wine$V1))\n","abline(h = 0, v = 0, lty = 2, col = 8)\n","\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":437},"id":"LEKcNLMMHHqY","executionInfo":{"status":"ok","timestamp":1717498108031,"user_tz":-120,"elapsed":524,"user":{"displayName":"toni Monleon","userId":"05269440626861479105"}},"outputId":"e37c86f1-fd52-4783-9812-8ea6a94f25b2"},"execution_count":58,"outputs":[{"output_type":"display_data","data":{"text/plain":["plot without title"],"image/png":"iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAIAAAByhViMAAAACXBIWXMAABJ0AAASdAHeZh94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\ndD8pD2iiv/1c8wQ+mBCTJ3RC8zPVjra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can observa that all data are represented in a reduced space of two dimensions, and all are perfected classified using all of the covariates. But, what is the method (algorithm) perfomance?"],"metadata":{"id":"v3d8U9l5GHTw"}},{"cell_type":"code","source":["#Perform classification and obtaining the prediction for each observation (wine)\n","fit.LDA.C = predict(wine.lda, newdata=wine[,c(2:14)])$class\n","fit.LDA.C #predicted values\n","\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":69},"id":"rgVUUOaZHx8v","executionInfo":{"status":"ok","timestamp":1717498116018,"user_tz":-120,"elapsed":371,"user":{"displayName":"toni Monleon","userId":"05269440626861479105"}},"outputId":"cde71624-4f08-45ff-af7d-7b14b6e475a0"},"execution_count":59,"outputs":[{"output_type":"display_data","data":{"text/html":["\n","
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\n","\t\n","\t\tLevels:\n","\t\n","\t\n","\t
  1. '1'
  2. '2'
  3. '3'
\n","
"],"text/markdown":"1. 1\n2. 1\n3. 1\n4. 1\n5. 1\n6. 1\n7. 1\n8. 1\n9. 1\n10. 1\n11. 1\n12. 1\n13. 1\n14. 1\n15. 1\n16. 1\n17. 1\n18. 1\n19. 1\n20. 1\n21. 1\n22. 1\n23. 1\n24. 1\n25. 1\n26. 1\n27. 1\n28. 1\n29. 1\n30. 1\n31. 1\n32. 1\n33. 1\n34. 1\n35. 1\n36. 1\n37. 1\n38. 1\n39. 1\n40. 1\n41. 1\n42. 1\n43. 1\n44. 1\n45. 1\n46. 1\n47. 1\n48. 1\n49. 1\n50. 1\n51. 1\n52. 1\n53. 1\n54. 1\n55. 1\n56. 1\n57. 1\n58. 1\n59. 1\n60. 2\n61. 2\n62. 2\n63. 2\n64. 2\n65. 2\n66. 2\n67. 2\n68. 2\n69. 2\n70. 2\n71. 2\n72. 2\n73. 2\n74. 2\n75. 2\n76. 2\n77. 2\n78. 2\n79. 2\n80. 2\n81. 2\n82. 2\n83. 2\n84. 2\n85. 2\n86. 2\n87. 2\n88. 2\n89. 2\n90. 2\n91. 2\n92. 2\n93. 2\n94. 2\n95. 2\n96. 2\n97. 2\n98. 2\n99. 2\n100. 2\n101. 2\n102. 2\n103. 2\n104. 2\n105. 2\n106. 2\n107. 2\n108. 2\n109. 2\n110. 2\n111. 2\n112. 2\n113. 2\n114. 2\n115. 2\n116. 2\n117. 2\n118. 2\n119. 2\n120. 2\n121. 2\n122. 2\n123. 2\n124. 2\n125. 2\n126. 2\n127. 2\n128. 2\n129. 2\n130. 2\n131. 3\n132. 3\n133. 3\n134. 3\n135. 3\n136. 3\n137. 3\n138. 3\n139. 3\n140. 3\n141. 3\n142. 3\n143. 3\n144. 3\n145. 3\n146. 3\n147. 3\n148. 3\n149. 3\n150. 3\n151. 3\n152. 3\n153. 3\n154. 3\n155. 3\n156. 3\n157. 3\n158. 3\n159. 3\n160. 3\n161. 3\n162. 3\n163. 3\n164. 3\n165. 3\n166. 3\n167. 3\n168. 3\n169. 3\n170. 3\n171. 3\n172. 3\n173. 3\n174. 3\n175. 3\n176. 3\n177. 3\n178. 3\n\n\n\n**Levels**: 1. '1'\n2. '2'\n3. '3'\n\n\n","text/latex":"\\begin{enumerate*}\n\\item 1\n\\item 1\n\\item 1\n\\item 1\n\\item 1\n\\item 1\n\\item 1\n\\item 1\n\\item 1\n\\item 1\n\\item 1\n\\item 1\n\\item 1\n\\item 1\n\\item 1\n\\item 1\n\\item 1\n\\item 1\n\\item 1\n\\item 1\n\\item 1\n\\item 1\n\\item 1\n\\item 1\n\\item 1\n\\item 1\n\\item 1\n\\item 1\n\\item 1\n\\item 1\n\\item 1\n\\item 1\n\\item 1\n\\item 1\n\\item 1\n\\item 1\n\\item 1\n\\item 1\n\\item 1\n\\item 1\n\\item 1\n\\item 1\n\\item 1\n\\item 1\n\\item 1\n\\item 1\n\\item 1\n\\item 1\n\\item 1\n\\item 1\n\\item 1\n\\item 1\n\\item 1\n\\item 1\n\\item 1\n\\item 1\n\\item 1\n\\item 1\n\\item 1\n\\item 2\n\\item 2\n\\item 2\n\\item 2\n\\item 2\n\\item 2\n\\item 2\n\\item 2\n\\item 2\n\\item 2\n\\item 2\n\\item 2\n\\item 2\n\\item 2\n\\item 2\n\\item 2\n\\item 2\n\\item 2\n\\item 2\n\\item 2\n\\item 2\n\\item 2\n\\item 2\n\\item 2\n\\item 2\n\\item 2\n\\item 2\n\\item 2\n\\item 2\n\\item 2\n\\item 2\n\\item 2\n\\item 2\n\\item 2\n\\item 2\n\\item 2\n\\item 2\n\\item 2\n\\item 2\n\\item 2\n\\item 2\n\\item 2\n\\item 2\n\\item 2\n\\item 2\n\\item 2\n\\item 2\n\\item 2\n\\item 2\n\\item 2\n\\item 2\n\\item 2\n\\item 2\n\\item 2\n\\item 2\n\\item 2\n\\item 2\n\\item 2\n\\item 2\n\\item 2\n\\item 2\n\\item 2\n\\item 2\n\\item 2\n\\item 2\n\\item 2\n\\item 2\n\\item 2\n\\item 2\n\\item 2\n\\item 2\n\\item 3\n\\item 3\n\\item 3\n\\item 3\n\\item 3\n\\item 3\n\\item 3\n\\item 3\n\\item 3\n\\item 3\n\\item 3\n\\item 3\n\\item 3\n\\item 3\n\\item 3\n\\item 3\n\\item 3\n\\item 3\n\\item 3\n\\item 3\n\\item 3\n\\item 3\n\\item 3\n\\item 3\n\\item 3\n\\item 3\n\\item 3\n\\item 3\n\\item 3\n\\item 3\n\\item 3\n\\item 3\n\\item 3\n\\item 3\n\\item 3\n\\item 3\n\\item 3\n\\item 3\n\\item 3\n\\item 3\n\\item 3\n\\item 3\n\\item 3\n\\item 3\n\\item 3\n\\item 3\n\\item 3\n\\item 3\n\\end{enumerate*}\n\n\\emph{Levels}: \\begin{enumerate*}\n\\item '1'\n\\item '2'\n\\item '3'\n\\end{enumerate*}\n","text/plain":[" [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n"," [38] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2\n"," [75] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2\n","[112] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3\n","[149] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3\n","Levels: 1 2 3"]},"metadata":{}}]},{"cell_type":"markdown","source":["And now computing the **confussion matrix** (real group classes (cultivar) vs predicted classes by the method)"],"metadata":{"id":"53mRaJxOGoKI"}},{"cell_type":"code","source":["#Determine misclassification with confussion matrix and accuracy\n","table(as.matrix(wine[,1]),as.matrix(fit.LDA.C)) #cols = real classes, rows = predicted classes\n","\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":109},"id":"feIuGMLZGnRN","executionInfo":{"status":"ok","timestamp":1717498123038,"user_tz":-120,"elapsed":701,"user":{"displayName":"toni Monleon","userId":"05269440626861479105"}},"outputId":"dd74f9dd-0acb-41f2-ace4-48561e96adf7"},"execution_count":60,"outputs":[{"output_type":"display_data","data":{"text/plain":[" \n"," 1 2 3\n"," 1 59 0 0\n"," 2 0 71 0\n"," 3 0 0 48"]},"metadata":{}}]},{"cell_type":"markdown","source":["Well done!. We see that total correct classified is 100% = (59 + 71 + 48)/ (59 + 71 + 48). This is the **accuracy**!\n","\n","\n","\n","Binary confussion matrix interpretation:\n","![](https://miro.medium.com/v2/resize:fit:969/1*d0UCCIF10Soi7VQGxdVrWQ.jpeg)\n","\n","\n","\n","\n","An alternative to this form to obtain the confussion matrix is use library(caret):"],"metadata":{"id":"NXb_66zKG3_u"}},{"cell_type":"code","source":["#attention, it takes a lot of time !!!!!!!\n","\n","#other possibility to obtain a confusion matrix is:\n","\n","#install.packages(\"caret\")\n","#library(caret)\n","#confusionMatrix(unlist(c(wine[,1])),fit.LDA.C)\n"],"metadata":{"id":"tdWV-z2ZHkH_"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["There exists more metrics in order to obtain the perfomance of our method, like precission, recall and others. See in: https://www.analyticsvidhya.com/blog/2021/07/metrics-to-evaluate-your-classification-model-to-take-the-right-decisions/"],"metadata":{"id":"S5QAuk_Nj9d3"}},{"cell_type":"markdown","source":["The performance (accuracy) of the method is the same if the number of classifying variables decreases. Let's see what happens with 4 variables."],"metadata":{"id":"ul594aziHqUd"}},{"cell_type":"code","source":["#using the linear discriminant method (Fisher discriminant: LDA)\n","wine.lda1 <- lda(wine$V1 ~ wine$V3 + wine$V4 + wine$V5 + wine$V7) #now only using V3, V4, V5 and V7\n","\n","#represent LDA functions\n","plot(wine.lda1, col = as.integer(wine$V1))\n","abline(h = 0, v = 0, lty = 2, col = 8)\n","fit.LDA.C1 = predict(wine.lda1, newdata=wine[,c(2:14)])$class\n","\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":437},"id":"dEGd3zV-H46H","executionInfo":{"status":"ok","timestamp":1717498318773,"user_tz":-120,"elapsed":404,"user":{"displayName":"toni Monleon","userId":"05269440626861479105"}},"outputId":"21a70499-4ad4-4db4-9d44-67087b7bc828"},"execution_count":61,"outputs":[{"output_type":"display_data","data":{"text/plain":["plot without title"],"image/png":"iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAIAAAByhViMAAAACXBIWXMAABJ0AAASdAHeZh94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\nFC0GZ2nP56suLFIc0RmF8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mF/wFtZDO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\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OKHLBu+cm72krOeyo2Va/UoWCuOlKS0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\n/wvxolbP56KDsAAAAABJRU5ErkJggg=="},"metadata":{"image/png":{"width":420,"height":420}}}]},{"cell_type":"markdown","source":["It can be seen that the method no longer classifies 100% of the wines. Let's look at the confusion matrix and the accuracy:"],"metadata":{"id":"mhH3qNM6iiaG"}},{"cell_type":"code","source":["#Determine misclassification with confussion matrix and accuracy\n","table(as.matrix(wine[,1]),as.matrix(fit.LDA.C1))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":109},"id":"Qe6DJvxCihZN","executionInfo":{"status":"ok","timestamp":1717498326476,"user_tz":-120,"elapsed":402,"user":{"displayName":"toni Monleon","userId":"05269440626861479105"}},"outputId":"18643cb4-dc55-4d9a-c879-02b46d225ddb"},"execution_count":62,"outputs":[{"output_type":"display_data","data":{"text/plain":[" \n"," 1 2 3\n"," 1 55 4 0\n"," 2 9 53 9\n"," 3 0 7 41"]},"metadata":{}}]},{"cell_type":"markdown","source":["We see that total correct classified is < 100% (55 + 53 + 41)/ (59 + 71 + 48) = ACCURACY = 0.8370787 (83,7%), not bad!\n","\n"],"metadata":{"id":"K1OIrji1If3P"}},{"cell_type":"markdown","source":["There are alternatives to LDA, like:\n","\n","\n","\n","* Quadratic Discriminant Analysis\n","* Classification methods (supervised basen on ML):\n","\n"," * SVM\n"," * Decision Trees\n"," * Logistic Regression\n"," * ANN\n","\n","![](https://techvidvan.com/tutorials/wp-content/uploads/sites/2/2020/05/Classification-Algorithms-in-R.jpg)\n","\n","\n","\n","https://techvidvan.com/tutorials/wp-content/uploads/sites/2/2020/05/Classification-Algorithms-in-R.jpg\n","\n","\n","\n","See in https://rpubs.com/Nolan/298913, https://uw.pressbooks.pub/appliedmultivariatestatistics/chapter/discriminant-analysis/\n","\n","And for Classification in Ml in https://techvidvan.com/tutorials/classification-in-r/"],"metadata":{"id":"jEHB-gFCunHg"}},{"cell_type":"markdown","source":["**Deep in discriminant linear functions (LDA): Loadings for the Discriminant Functions**\n","\n","\n","This section complements what was previously seen and allows to know details about the LDA, and how the different calculations are obtained.\n","\n","Let us now return to the discriminant model with all the variables that we have used at the beginning. Come back to the LDA model with all variables \"wine.lda\" and we type:\n"],"metadata":{"id":"JpzReYk_xPum"}},{"cell_type":"code","source":["plot(wine.lda, col = as.integer(wine$V1))\n","abline(h = 0, v = 0, lty = 2, col = 8)"],"metadata":{"id":"WGmPn96hjs1O","colab":{"base_uri":"https://localhost:8080/","height":437},"executionInfo":{"status":"ok","timestamp":1717498335198,"user_tz":-120,"elapsed":439,"user":{"displayName":"toni Monleon","userId":"05269440626861479105"}},"outputId":"45c1cce7-7f1a-443c-e284-a6730dc4a0cb"},"execution_count":63,"outputs":[{"output_type":"display_data","data":{"text/plain":["plot without title"],"image/png":"iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAIAAAByhViMAAAACXBIWXMAABJ0AAASdAHeZh94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\ndD8pD2iiv/1c8wQ+mBCTJ3RC8zPVjra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CL6q8SRX2npA4sAYkKwA2AEC2cLcd19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"},"metadata":{"image/png":{"width":420,"height":420}}}]},{"cell_type":"markdown","source":["\n","To get the values of the loadings of the discriminant functions (coefficients of linear discriminants, LD1, LD2) for the wine data, we can type:"],"metadata":{"id":"-lEl_2J7jwCC"}},{"cell_type":"code","source":["wine.lda"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":677},"id":"j49mqFSsIQC2","executionInfo":{"status":"ok","timestamp":1717436826828,"user_tz":-120,"elapsed":359,"user":{"displayName":"toni Monleon","userId":"05269440626861479105"}},"outputId":"bf8cd382-b552-4060-ac42-79068695c03d"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["Call:\n","lda(wine$V1 ~ wine$V2 + wine$V3 + wine$V4 + wine$V5 + wine$V6 + \n"," wine$V7 + wine$V8 + wine$V9 + wine$V10 + wine$V11 + wine$V12 + \n"," wine$V13 + wine$V14)\n","\n","Prior probabilities of groups:\n"," 1 2 3 \n","0.3314607 0.3988764 0.2696629 \n","\n","Group means:\n"," wine$V2 wine$V3 wine$V4 wine$V5 wine$V6 wine$V7 wine$V8 wine$V9\n","1 13.74475 2.010678 2.455593 17.03729 106.3390 2.840169 2.9823729 0.290000\n","2 12.27873 1.932676 2.244789 20.23803 94.5493 2.258873 2.0808451 0.363662\n","3 13.15375 3.333750 2.437083 21.41667 99.3125 1.678750 0.7814583 0.447500\n"," wine$V10 wine$V11 wine$V12 wine$V13 wine$V14\n","1 1.899322 5.528305 1.0620339 3.157797 1115.7119\n","2 1.630282 3.086620 1.0562817 2.785352 519.5070\n","3 1.153542 7.396250 0.6827083 1.683542 629.8958\n","\n","Coefficients of linear discriminants:\n"," LD1 LD2\n","wine$V2 -0.403399781 0.8717930699\n","wine$V3 0.165254596 0.3053797325\n","wine$V4 -0.369075256 2.3458497486\n","wine$V5 0.154797889 -0.1463807654\n","wine$V6 -0.002163496 -0.0004627565\n","wine$V7 0.618052068 -0.0322128171\n","wine$V8 -1.661191235 -0.4919980543\n","wine$V9 -1.495818440 -1.6309537953\n","wine$V10 0.134092628 -0.3070875776\n","wine$V11 0.355055710 0.2532306865\n","wine$V12 -0.818036073 -1.5156344987\n","wine$V13 -1.157559376 0.0511839665\n","wine$V14 -0.002691206 0.0028529846\n","\n","Proportion of trace:\n"," LD1 LD2 \n","0.6875 0.3125 "]},"metadata":{}}]},{"cell_type":"markdown","source":["This means that the first discriminant function is a linear combination of the variables: -0.403*V2 + 0.165*V3 - 0.369*V4 + 0.155*V5 - 0.002*V6 + 0.618*V7 - 1.661*V8 - 1.496*V9 + 0.134*V10 + 0.355*V11 - 0.818*V12 - 1.158*V13 - 0.003*V14, where V2, V3, ... V14 are the concentrations of the 13 chemicals found in the wine samples. For convenience, the value for each discriminant function (eg. the first discriminant function) are scaled so that their mean value is zero (see below).\n","\n","Note that these loadings are calculated so that the within-group variance of each discriminant function for each group (cultivar) is equal to 1, as will be demonstrated below."],"metadata":{"id":"BlqK2SLAxPT6"}},{"cell_type":"markdown","source":["These scalings are also stored in the named element “scaling” of the variable returned by the lda() function. This element contains a matrix, in which the first column contains the loadings for the first discriminant function, the second column contains the loadings for the second discriminant function and so on. For example, to extract the loadings for the first discriminant function, we can type:"],"metadata":{"id":"K8q2CT1a320Q"}},{"cell_type":"code","source":["wine.lda$scaling[,1]"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":69},"id":"w_rBW5-f3xsa","executionInfo":{"status":"ok","timestamp":1717436831578,"user_tz":-120,"elapsed":370,"user":{"displayName":"toni Monleon","userId":"05269440626861479105"}},"outputId":"e9f75a97-6abb-4ca7-bcd4-6c77351b03ca"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/html":["
wine$V2
-0.403399780500476
wine$V3
0.165254596068548
wine$V4
-0.369075256357559
wine$V5
0.154797888801332
wine$V6
-0.00216349625827316
wine$V7
0.6180520678581
wine$V8
-1.66119123482067
wine$V9
-1.49581843970032
wine$V10
0.13409262842985
wine$V11
0.35505570971822
wine$V12
-0.818036073451751
wine$V13
-1.15755937590347
wine$V14
-0.00269120640308077
\n"],"text/markdown":"wine$V2\n: -0.403399780500476wine$V3\n: 0.165254596068548wine$V4\n: -0.369075256357559wine$V5\n: 0.154797888801332wine$V6\n: -0.00216349625827316wine$V7\n: 0.6180520678581wine$V8\n: -1.66119123482067wine$V9\n: -1.49581843970032wine$V10\n: 0.13409262842985wine$V11\n: 0.35505570971822wine$V12\n: -0.818036073451751wine$V13\n: -1.15755937590347wine$V14\n: -0.00269120640308077\n\n","text/latex":"\\begin{description*}\n\\item[wine\\textbackslash{}\\$V2] -0.403399780500476\n\\item[wine\\textbackslash{}\\$V3] 0.165254596068548\n\\item[wine\\textbackslash{}\\$V4] -0.369075256357559\n\\item[wine\\textbackslash{}\\$V5] 0.154797888801332\n\\item[wine\\textbackslash{}\\$V6] -0.00216349625827316\n\\item[wine\\textbackslash{}\\$V7] 0.6180520678581\n\\item[wine\\textbackslash{}\\$V8] -1.66119123482067\n\\item[wine\\textbackslash{}\\$V9] -1.49581843970032\n\\item[wine\\textbackslash{}\\$V10] 0.13409262842985\n\\item[wine\\textbackslash{}\\$V11] 0.35505570971822\n\\item[wine\\textbackslash{}\\$V12] -0.818036073451751\n\\item[wine\\textbackslash{}\\$V13] -1.15755937590347\n\\item[wine\\textbackslash{}\\$V14] -0.00269120640308077\n\\end{description*}\n","text/plain":[" wine$V2 wine$V3 wine$V4 wine$V5 wine$V6 wine$V7 \n","-0.403399781 0.165254596 -0.369075256 0.154797889 -0.002163496 0.618052068 \n"," wine$V8 wine$V9 wine$V10 wine$V11 wine$V12 wine$V13 \n","-1.661191235 -1.495818440 0.134092628 0.355055710 -0.818036073 -1.157559376 \n"," wine$V14 \n","-0.002691206 "]},"metadata":{}}]},{"cell_type":"markdown","source":["----\n","**THIS SECTION IS ONLY TO EXPAND THEORETICAL CONCEPTS AND TO LEARN HOW THIS STATISTICAL METHOD WORKS**\n","\n","To calculate the values of the first discriminant function for each wine in order to represent in the discriminant plot, we can define our own function “calclda()”:"],"metadata":{"id":"5LuVoLAY3xiV"}},{"cell_type":"code","source":[" calclda <- function(variables,loadings)\n"," {\n"," # find the number of samples in the data set\n"," as.data.frame(variables)\n"," numsamples <- nrow(variables)\n"," # make a vector to store the discriminant function\n"," ld <- numeric(numsamples)\n"," # find the number of variables\n"," numvariables <- length(variables)\n"," # calculate the value of the discriminant function for each sample\n"," for (i in 1:numsamples)\n"," {\n"," valuei <- 0\n"," for (j in 1:numvariables)\n"," {\n"," valueij <- variables[i,j]\n"," loadingj <- loadings[j]\n"," valuei <- valuei + (valueij * loadingj)\n"," }\n"," ld[i] <- valuei\n"," }\n"," # standardise the discriminant function so that its mean value is 0:\n"," ld <- as.data.frame(scale(ld, center=TRUE, scale=FALSE))\n"," ld <- ld[[1]]\n"," return(ld)\n"," }"],"metadata":{"id":"JenTF3mh3xWQ","executionInfo":{"status":"ok","timestamp":1717498345064,"user_tz":-120,"elapsed":956,"user":{"displayName":"toni Monleon","userId":"05269440626861479105"}}},"execution_count":64,"outputs":[]},{"cell_type":"markdown","source":["The function calclda() simply calculates the value of a discriminant function for each sample in the data set, for example, for the first disriminant function, for each sample we calculate the value using the equation -0.403*V2 - 0.165*V3 - 0.369*V4 + 0.155*V5 - 0.002*V6 + 0.618*V7 - 1.661*V8 - 1.496*V9 + 0.134*V10 + 0.355*V11 - 0.818*V12 - 1.158*V13 - 0.003*V14. Furthermore, the “scale()” command is used within the calclda() function in order to standardise the value of a discriminant function (eg. the first discriminant function) so that its mean value (over all the wine samples) is 0.\n","\n","We can use the function calclda() to calculate the values of the first discriminant function for each sample in our wine data:"],"metadata":{"id":"aMQVdEn63xNQ"}},{"cell_type":"code","source":["calclda(wine[2:14], wine.lda$scaling[,1])"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":329},"id":"NUw0QCy43xCY","executionInfo":{"status":"ok","timestamp":1717436838799,"user_tz":-120,"elapsed":371,"user":{"displayName":"toni Monleon","userId":"05269440626861479105"}},"outputId":"cd4c2c68-8f4e-4ca3-d2e9-280bdd0eff12"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/html":["\n","
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wine"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":242},"id":"6ql8gJDD3wqr","executionInfo":{"status":"ok","timestamp":1717498354587,"user_tz":-120,"elapsed":405,"user":{"displayName":"toni Monleon","userId":"05269440626861479105"}},"outputId":"fe2a6ffb-42cd-4cf1-d6a7-d214c7cab6b2"},"execution_count":65,"outputs":[{"output_type":"display_data","data":{"text/html":["
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5.04368276760459\n\\item[158] 4.86980829358846\n\\item[159] 5.61316558149678\n\\item[160] 5.67046736558865\n\\item[161] 5.37413512851206\n\\item[162] 3.09975377348889\n\\item[163] 3.35888137267014\n\\item[164] 3.04007194055343\n\\item[165] 4.94861302797183\n\\item[166] 4.54504458482222\n\\item[167] 5.27255843767018\n\\item[168] 5.13016116818695\n\\item[169] 4.30468082481976\n\\item[170] 5.08336782476797\n\\item[171] 4.06743570994416\n\\item[172] 5.74212960950077\n\\item[173] 4.48205140046182\n\\item[174] 4.29150757881199\n\\item[175] 4.50329623424154\n\\item[176] 5.04747032773162\n\\item[177] 4.27615504775291\n\\item[178] 5.53808609820185\n\\end{description*}\n","text/plain":[" 1 2 3 4 5 6 \n","-4.70024401 -4.30195811 -3.42071952 -4.20575366 -1.50998168 -4.51868934 \n"," 7 8 9 10 11 12 \n","-4.52737794 -4.14834781 -3.86082876 -3.36662444 -4.80587907 -3.42807646 \n"," 13 14 15 16 17 18 \n","-3.66610246 -5.58824635 -5.50131449 -3.18475189 -3.28936988 -2.99809262 \n"," 19 20 21 22 23 24 \n","-5.24640372 -3.13653106 -3.57747791 -1.69077135 -4.83515033 -3.09588961 \n"," 25 26 27 28 29 30 \n","-3.32164716 -2.14482223 -3.98242850 -2.68591432 -3.56309464 -3.17301573 \n"," 31 32 33 34 35 36 \n","-2.99626797 -3.56866244 -3.38506383 -3.52753750 -2.85190852 -2.79411996 \n"," 37 38 39 40 41 42 \n","-2.75808511 -2.17734477 -3.02926382 -3.27105228 -2.92065533 -2.23721062 \n"," 43 44 45 46 47 48 \n","-4.69972568 -1.23036133 -2.58203904 -2.58312049 -3.88887889 -3.44975356 \n"," 49 50 51 52 53 54 \n","-2.34223331 -3.52062596 -3.21840912 -4.38214896 -4.36311727 -3.51917293 \n"," 55 56 57 58 59 60 \n","-3.12277475 -1.80240540 -2.87378754 -3.61690518 -3.73868551 1.58618749 \n"," 61 62 63 64 65 66 \n"," 0.79967216 2.38015446 -0.45917726 -0.50726885 0.39398359 -0.92256616 \n"," 67 68 69 70 71 72 \n","-1.95549377 -0.34732815 0.20371212 -0.24831914 1.17987999 -1.07718925 \n"," 73 74 75 76 77 78 \n"," 0.64100179 -1.74684421 -0.34721117 1.14274222 0.18665882 0.90052500 \n"," 79 80 81 82 83 84 \n","-0.70709551 -0.59562833 -0.55761818 -1.80430417 0.23077079 2.03482711 \n"," 85 86 87 88 89 90 \n","-0.62113021 -1.03372742 0.76598781 0.35042568 0.15324508 -0.14962842 \n"," 91 92 93 94 95 96 \n"," 0.48079504 1.39689016 0.91972331 -0.59102937 0.49411386 -1.62614426 \n"," 97 98 99 100 101 102 \n"," 2.00044562 -1.00534818 -2.07121314 -1.63815890 -1.05894340 0.02594549 \n"," 103 104 105 106 107 108 \n","-0.21887407 1.36437640 -1.12901245 -0.21263094 -0.77946884 0.61546732 \n"," 109 110 111 112 113 114 \n"," 0.22550192 -2.03869851 0.79274716 0.30229545 -0.50664882 0.99837397 \n"," 115 116 117 118 119 120 \n","-0.21954922 -0.37131517 0.05545894 -0.09137874 1.79755252 -0.17405009 \n"," 121 122 123 124 125 126 \n","-1.17870281 -3.21054390 0.62605202 0.03366613 -0.69930080 -0.72061079 \n"," 127 128 129 130 131 132 \n","-0.51933512 1.17030045 0.10824791 1.12319783 2.24632419 3.28527755 \n"," 133 134 135 136 137 138 \n"," 4.07236441 3.86691235 3.45088333 3.71583899 3.92220510 4.85161020 \n"," 139 140 141 142 143 144 \n"," 3.54993389 3.76889174 2.66942250 2.32491492 3.17712883 2.88964418 \n"," 145 146 147 148 149 150 \n"," 3.78325562 3.04411324 4.70697017 4.85021393 4.98359184 4.86968293 \n"," 151 152 153 154 155 156 \n"," 4.59869190 5.67447884 5.32986123 5.03401031 4.52080087 5.09783710 \n"," 157 158 159 160 161 162 \n"," 5.04368277 4.86980829 5.61316558 5.67046737 5.37413513 3.09975377 \n"," 163 164 165 166 167 168 \n"," 3.35888137 3.04007194 4.94861303 4.54504458 5.27255844 5.13016117 \n"," 169 170 171 172 173 174 \n"," 4.30468082 5.08336782 4.06743571 5.74212961 4.48205140 4.29150758 \n"," 175 176 177 178 \n"," 4.50329623 5.04747033 4.27615505 5.53808610 "]},"metadata":{}}]},{"cell_type":"markdown","source":["We see that they do agree.\n","\n","It doesn’t matter whether the input variables for linear discriminant analysis are standardised or not, unlike for principal components analysis in which it is often necessary to standardise the input variables. However, using standardised variables in linear discriminant analysis makes it easier to interpret the loadings in a linear discriminant function.\n","\n","----\n","**THIS SECTION IS ONLY TO EXPAND THEORETICAL CONCEPTS AND TO LEARN HOW THIS STATISTICAL METHOD WORKS**\n","\n","In linear discriminant analysis, the **standardised version of an input variable** is defined so that it has mean zero and within-groups variance of 1. Thus, we can calculate the “group-standardised” variable by subtracting the mean from each value of the variable, and dividing by the within-groups standard deviation. To calculate the group-standardised version of a set of variables, we can use the function “groupStandardise()” below:"],"metadata":{"id":"-x85nVCa3wgH"}},{"cell_type":"code","source":["#upload this two functions in order to calculate the group-standardised versions of the chemical concentrations in wine samples\n","\n","#If we want to calculate the within-groups variance for a particular variable (for example, for a particular chemical’s concentration),\n","#we can use the function “calcWithinGroupsVariance()” below:\n","calcWithinGroupsVariance <- function(variable,groupvariable)\n"," {\n"," # find out how many values the group variable can take\n"," groupvariable2 <- as.factor(groupvariable[[1]])\n"," levels <- levels(groupvariable2)\n"," numlevels <- length(levels)\n"," # get the mean and standard deviation for each group:\n"," numtotal <- 0\n"," denomtotal <- 0\n"," for (i in 1:numlevels)\n"," {\n"," leveli <- levels[i]\n"," levelidata <- variable[groupvariable==leveli,]\n"," levelilength <- length(levelidata)\n"," # get the standard deviation for group i:\n"," sdi <- sd(levelidata)\n"," numi <- (levelilength - 1)*(sdi * sdi)\n"," denomi <- levelilength\n"," numtotal <- numtotal + numi\n"," denomtotal <- denomtotal + denomi\n"," }\n"," # calculate the within-groups variance\n"," Vw <- numtotal / (denomtotal - numlevels)\n"," return(Vw)\n"," }\n","\n","groupStandardise <- function(variables, groupvariable)\n"," {\n"," # find out how many variables we have\n"," variables <- as.data.frame(variables)\n"," numvariables <- length(variables)\n"," # find the variable names\n"," variablenames <- colnames(variables)\n"," # calculate the group-standardised version of each variable\n"," for (i in 1:numvariables)\n"," {\n"," variablei <- variables[i]\n"," variablei_name <- variablenames[i]\n"," variablei_Vw <- calcWithinGroupsVariance(variablei, groupvariable)\n"," #variablei_mean <- mean(variablei)\n"," variablei_mean <- mean(unlist(variablei))\n"," variablei_new <- (variablei - variablei_mean)/(sqrt(variablei_Vw))\n"," data_length <- nrow(variablei)\n"," if (i == 1) { variables_new <- data.frame(row.names=seq(1,data_length)) }\n"," variables_new[`variablei_name`] <- variablei_new\n"," }\n"," return(variables_new)\n"," }"],"metadata":{"id":"USYAwsNA3wRW","executionInfo":{"status":"ok","timestamp":1717498358168,"user_tz":-120,"elapsed":432,"user":{"displayName":"toni Monleon","userId":"05269440626861479105"}}},"execution_count":66,"outputs":[]},{"cell_type":"markdown","source":["For example, we can use the “groupStandardise()” function to calculate the group-standardised versions of the chemical concentrations in wine samples:"],"metadata":{"id":"eT8fIBcZ4WoP"}},{"cell_type":"code","source":["groupstandardisedconcentrations <- groupStandardise(wine[2:14], wine[1])\n","#original data\n","print(\"real data---------------------------------\")\n","head(wine[2:14])\n","print(\"standarized data---------------------------------\")\n","head(groupstandardisedconcentrations) #a sample of the results obtained for group-standardised versions of the chemical concentrations in wine samples:"],"metadata":{"id":"FCMDKwXxmuZZ","executionInfo":{"status":"ok","timestamp":1717498362938,"user_tz":-120,"elapsed":370,"user":{"displayName":"toni Monleon","userId":"05269440626861479105"}},"colab":{"base_uri":"https://localhost:8080/","height":592},"outputId":"b97a4f84-c4fc-4350-8852-4497f14fc5c1"},"execution_count":67,"outputs":[{"output_type":"stream","name":"stdout","text":["[1] \"real data---------------------------------\"\n"]},{"output_type":"display_data","data":{"text/html":["\n","\n","\n","\t\n","\t\n","\n","\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\n","
A data.frame: 6 × 13
V2V3V4V5V6V7V8V9V10V11V12V13V14
<dbl><dbl><dbl><dbl><int><dbl><dbl><dbl><dbl><dbl><dbl><dbl><int>
114.231.712.4315.61272.803.060.282.295.641.043.921065
213.201.782.1411.21002.652.760.261.284.381.053.401050
313.162.362.6718.61012.803.240.302.815.681.033.171185
414.371.952.5016.81133.853.490.242.187.800.863.451480
513.242.592.8721.01182.802.690.391.824.321.042.93 735
614.201.762.4515.21123.273.390.341.976.751.052.851450
\n"],"text/markdown":"\nA data.frame: 6 × 13\n\n| | V2 <dbl> | V3 <dbl> | V4 <dbl> | V5 <dbl> | V6 <int> | V7 <dbl> | V8 <dbl> | V9 <dbl> | V10 <dbl> | V11 <dbl> | V12 <dbl> | V13 <dbl> | V14 <int> |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | 14.23 | 1.71 | 2.43 | 15.6 | 127 | 2.80 | 3.06 | 0.28 | 2.29 | 5.64 | 1.04 | 3.92 | 1065 |\n| 2 | 13.20 | 1.78 | 2.14 | 11.2 | 100 | 2.65 | 2.76 | 0.26 | 1.28 | 4.38 | 1.05 | 3.40 | 1050 |\n| 3 | 13.16 | 2.36 | 2.67 | 18.6 | 101 | 2.80 | 3.24 | 0.30 | 2.81 | 5.68 | 1.03 | 3.17 | 1185 |\n| 4 | 14.37 | 1.95 | 2.50 | 16.8 | 113 | 3.85 | 3.49 | 0.24 | 2.18 | 7.80 | 0.86 | 3.45 | 1480 |\n| 5 | 13.24 | 2.59 | 2.87 | 21.0 | 118 | 2.80 | 2.69 | 0.39 | 1.82 | 4.32 | 1.04 | 2.93 | 735 |\n| 6 | 14.20 | 1.76 | 2.45 | 15.2 | 112 | 3.27 | 3.39 | 0.34 | 1.97 | 6.75 | 1.05 | 2.85 | 1450 |\n\n","text/latex":"A data.frame: 6 × 13\n\\begin{tabular}{r|lllllllllllll}\n & V2 & V3 & V4 & V5 & V6 & V7 & V8 & V9 & V10 & V11 & V12 & V13 & V14\\\\\n & & & & & & & & & & & & & \\\\\n\\hline\n\t1 & 14.23 & 1.71 & 2.43 & 15.6 & 127 & 2.80 & 3.06 & 0.28 & 2.29 & 5.64 & 1.04 & 3.92 & 1065\\\\\n\t2 & 13.20 & 1.78 & 2.14 & 11.2 & 100 & 2.65 & 2.76 & 0.26 & 1.28 & 4.38 & 1.05 & 3.40 & 1050\\\\\n\t3 & 13.16 & 2.36 & 2.67 & 18.6 & 101 & 2.80 & 3.24 & 0.30 & 2.81 & 5.68 & 1.03 & 3.17 & 1185\\\\\n\t4 & 14.37 & 1.95 & 2.50 & 16.8 & 113 & 3.85 & 3.49 & 0.24 & 2.18 & 7.80 & 0.86 & 3.45 & 1480\\\\\n\t5 & 13.24 & 2.59 & 2.87 & 21.0 & 118 & 2.80 & 2.69 & 0.39 & 1.82 & 4.32 & 1.04 & 2.93 & 735\\\\\n\t6 & 14.20 & 1.76 & 2.45 & 15.2 & 112 & 3.27 & 3.39 & 0.34 & 1.97 & 6.75 & 1.05 & 2.85 & 1450\\\\\n\\end{tabular}\n","text/plain":[" V2 V3 V4 V5 V6 V7 V8 V9 V10 V11 V12 V13 V14 \n","1 14.23 1.71 2.43 15.6 127 2.80 3.06 0.28 2.29 5.64 1.04 3.92 1065\n","2 13.20 1.78 2.14 11.2 100 2.65 2.76 0.26 1.28 4.38 1.05 3.40 1050\n","3 13.16 2.36 2.67 18.6 101 2.80 3.24 0.30 2.81 5.68 1.03 3.17 1185\n","4 14.37 1.95 2.50 16.8 113 3.85 3.49 0.24 2.18 7.80 0.86 3.45 1480\n","5 13.24 2.59 2.87 21.0 118 2.80 2.69 0.39 1.82 4.32 1.04 2.93 735\n","6 14.20 1.76 2.45 15.2 112 3.27 3.39 0.34 1.97 6.75 1.05 2.85 1450"]},"metadata":{}},{"output_type":"stream","name":"stdout","text":["[1] \"standarized data---------------------------------\"\n"]},{"output_type":"display_data","data":{"text/html":["\n","\n","\n","\t\n","\t\n","\n","\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\n","
A data.frame: 6 × 13
V2V3V4V5V6V7V8V9V10V11V12V13V14
<dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl>
12.4015561-0.66484481 0.2469729-1.37648482.028021071.15443841.966572-0.7499851 1.4090287 0.3849640 0.52752923.2628559 1.84560386
20.3894860-0.59054248-0.8812344-2.93145790.019226910.81145961.394190-0.9332347-0.6266124-0.4485919 0.59143301.9660080 1.75857629
30.3113474 0.02510536 1.1806618-0.31627580.093626691.15443842.310001-0.5667355 2.4570816 0.4114261 0.46362541.3924022 2.54182446
42.6750413-0.41009398 0.5192989-0.95240120.986424103.55529012.786986-1.1164843 1.1873252 1.8139170-0.62273862.0907049 4.25336674
50.4676247 0.26924157 1.9587359 0.53189131.358423021.15443841.260634 0.2578878 0.4617502-0.4882851 0.52752920.7938570-0.06900276
62.3429521-0.61177172 0.3247804-1.51784590.912024322.22910542.596192-0.2002362 0.7640731 1.1192871 0.59143300.5943419 4.07931159
\n"],"text/markdown":"\nA data.frame: 6 × 13\n\n| | V2 <dbl> | V3 <dbl> | V4 <dbl> | V5 <dbl> | V6 <dbl> | V7 <dbl> | V8 <dbl> | V9 <dbl> | V10 <dbl> | V11 <dbl> | V12 <dbl> | V13 <dbl> | V14 <dbl> |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | 2.4015561 | -0.66484481 | 0.2469729 | -1.3764848 | 2.02802107 | 1.1544384 | 1.966572 | -0.7499851 | 1.4090287 | 0.3849640 | 0.5275292 | 3.2628559 | 1.84560386 |\n| 2 | 0.3894860 | -0.59054248 | -0.8812344 | -2.9314579 | 0.01922691 | 0.8114596 | 1.394190 | -0.9332347 | -0.6266124 | -0.4485919 | 0.5914330 | 1.9660080 | 1.75857629 |\n| 3 | 0.3113474 | 0.02510536 | 1.1806618 | -0.3162758 | 0.09362669 | 1.1544384 | 2.310001 | -0.5667355 | 2.4570816 | 0.4114261 | 0.4636254 | 1.3924022 | 2.54182446 |\n| 4 | 2.6750413 | -0.41009398 | 0.5192989 | -0.9524012 | 0.98642410 | 3.5552901 | 2.786986 | -1.1164843 | 1.1873252 | 1.8139170 | -0.6227386 | 2.0907049 | 4.25336674 |\n| 5 | 0.4676247 | 0.26924157 | 1.9587359 | 0.5318913 | 1.35842302 | 1.1544384 | 1.260634 | 0.2578878 | 0.4617502 | -0.4882851 | 0.5275292 | 0.7938570 | -0.06900276 |\n| 6 | 2.3429521 | -0.61177172 | 0.3247804 | -1.5178459 | 0.91202432 | 2.2291054 | 2.596192 | -0.2002362 | 0.7640731 | 1.1192871 | 0.5914330 | 0.5943419 | 4.07931159 |\n\n","text/latex":"A data.frame: 6 × 13\n\\begin{tabular}{r|lllllllllllll}\n & V2 & V3 & V4 & V5 & V6 & V7 & V8 & V9 & V10 & V11 & V12 & V13 & V14\\\\\n & & & & & & & & & & & & & \\\\\n\\hline\n\t1 & 2.4015561 & -0.66484481 & 0.2469729 & -1.3764848 & 2.02802107 & 1.1544384 & 1.966572 & -0.7499851 & 1.4090287 & 0.3849640 & 0.5275292 & 3.2628559 & 1.84560386\\\\\n\t2 & 0.3894860 & -0.59054248 & -0.8812344 & -2.9314579 & 0.01922691 & 0.8114596 & 1.394190 & -0.9332347 & -0.6266124 & -0.4485919 & 0.5914330 & 1.9660080 & 1.75857629\\\\\n\t3 & 0.3113474 & 0.02510536 & 1.1806618 & -0.3162758 & 0.09362669 & 1.1544384 & 2.310001 & -0.5667355 & 2.4570816 & 0.4114261 & 0.4636254 & 1.3924022 & 2.54182446\\\\\n\t4 & 2.6750413 & -0.41009398 & 0.5192989 & -0.9524012 & 0.98642410 & 3.5552901 & 2.786986 & -1.1164843 & 1.1873252 & 1.8139170 & -0.6227386 & 2.0907049 & 4.25336674\\\\\n\t5 & 0.4676247 & 0.26924157 & 1.9587359 & 0.5318913 & 1.35842302 & 1.1544384 & 1.260634 & 0.2578878 & 0.4617502 & -0.4882851 & 0.5275292 & 0.7938570 & -0.06900276\\\\\n\t6 & 2.3429521 & -0.61177172 & 0.3247804 & -1.5178459 & 0.91202432 & 2.2291054 & 2.596192 & -0.2002362 & 0.7640731 & 1.1192871 & 0.5914330 & 0.5943419 & 4.07931159\\\\\n\\end{tabular}\n","text/plain":[" V2 V3 V4 V5 V6 V7 V8 \n","1 2.4015561 -0.66484481 0.2469729 -1.3764848 2.02802107 1.1544384 1.966572\n","2 0.3894860 -0.59054248 -0.8812344 -2.9314579 0.01922691 0.8114596 1.394190\n","3 0.3113474 0.02510536 1.1806618 -0.3162758 0.09362669 1.1544384 2.310001\n","4 2.6750413 -0.41009398 0.5192989 -0.9524012 0.98642410 3.5552901 2.786986\n","5 0.4676247 0.26924157 1.9587359 0.5318913 1.35842302 1.1544384 1.260634\n","6 2.3429521 -0.61177172 0.3247804 -1.5178459 0.91202432 2.2291054 2.596192\n"," V9 V10 V11 V12 V13 V14 \n","1 -0.7499851 1.4090287 0.3849640 0.5275292 3.2628559 1.84560386\n","2 -0.9332347 -0.6266124 -0.4485919 0.5914330 1.9660080 1.75857629\n","3 -0.5667355 2.4570816 0.4114261 0.4636254 1.3924022 2.54182446\n","4 -1.1164843 1.1873252 1.8139170 -0.6227386 2.0907049 4.25336674\n","5 0.2578878 0.4617502 -0.4882851 0.5275292 0.7938570 -0.06900276\n","6 -0.2002362 0.7640731 1.1192871 0.5914330 0.5943419 4.07931159"]},"metadata":{}}]},{"cell_type":"markdown","source":["We can then use the lda() function to perform linear discriminant analysis on the group-standardised variables:"],"metadata":{"id":"WNPZZQTL4Vfi"}},{"cell_type":"code","source":["wine.lda2 <- lda(wine$V1 ~ groupstandardisedconcentrations$V2 + groupstandardisedconcentrations$V3 +\n"," groupstandardisedconcentrations$V4 + groupstandardisedconcentrations$V5 +\n"," groupstandardisedconcentrations$V6 + groupstandardisedconcentrations$V7 +\n"," groupstandardisedconcentrations$V8 + groupstandardisedconcentrations$V9 +\n"," groupstandardisedconcentrations$V10 + groupstandardisedconcentrations$V11 +\n"," groupstandardisedconcentrations$V12 + groupstandardisedconcentrations$V13 +\n"," groupstandardisedconcentrations$V14) #LDA with standarized variables\n"," wine.lda2"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"0E0LEXjD4gZw","executionInfo":{"status":"ok","timestamp":1717498367458,"user_tz":-120,"elapsed":472,"user":{"displayName":"toni Monleon","userId":"05269440626861479105"}},"outputId":"cfdbf01e-c59e-4482-efc9-c79fdd65b208"},"execution_count":68,"outputs":[{"output_type":"display_data","data":{"text/plain":["Call:\n","lda(wine$V1 ~ groupstandardisedconcentrations$V2 + groupstandardisedconcentrations$V3 + \n"," groupstandardisedconcentrations$V4 + groupstandardisedconcentrations$V5 + \n"," groupstandardisedconcentrations$V6 + groupstandardisedconcentrations$V7 + \n"," groupstandardisedconcentrations$V8 + groupstandardisedconcentrations$V9 + \n"," groupstandardisedconcentrations$V10 + groupstandardisedconcentrations$V11 + \n"," groupstandardisedconcentrations$V12 + groupstandardisedconcentrations$V13 + \n"," groupstandardisedconcentrations$V14)\n","\n","Prior probabilities of groups:\n"," 1 2 3 \n","0.3314607 0.3988764 0.2696629 \n","\n","Group means:\n"," groupstandardisedconcentrations$V2 groupstandardisedconcentrations$V3\n","1 1.4536284 -0.3456866\n","2 -1.4101790 -0.4284827\n","3 0.2991382 1.0587038\n"," groupstandardisedconcentrations$V4 groupstandardisedconcentrations$V5\n","1 0.3465400 -0.8685428\n","2 -0.4735675 0.2626082\n","3 0.2745297 0.6791426\n"," groupstandardisedconcentrations$V6 groupstandardisedconcentrations$V7\n","1 0.49084588 1.24628698\n","2 -0.38630431 -0.08286167\n","3 -0.03192294 -1.40932820\n"," groupstandardisedconcentrations$V8 groupstandardisedconcentrations$V9\n","1 1.81846383 -0.65836029\n","2 0.09840279 0.01656613\n","3 -2.38074924 0.78473046\n"," groupstandardisedconcentrations$V10 groupstandardisedconcentrations$V11\n","1 0.62162263 0.311072\n","2 0.07937552 -1.304231\n","3 -0.88148744 1.546815\n"," groupstandardisedconcentrations$V12 groupstandardisedconcentrations$V13\n","1 0.6683341 1.3619677\n","2 0.6315753 0.4331141\n","3 -1.7556992 -2.3147332\n"," groupstandardisedconcentrations$V14\n","1 2.1398259\n","2 -1.3192580\n","3 -0.6788001\n","\n","Coefficients of linear discriminants:\n"," LD1 LD2\n","groupstandardisedconcentrations$V2 -0.20650463 0.446280119\n","groupstandardisedconcentrations$V3 0.15568586 0.287697336\n","groupstandardisedconcentrations$V4 -0.09486893 0.602988809\n","groupstandardisedconcentrations$V5 0.43802089 -0.414203541\n","groupstandardisedconcentrations$V6 -0.02907934 -0.006219863\n","groupstandardisedconcentrations$V7 0.27030186 -0.014088108\n","groupstandardisedconcentrations$V8 -0.87067265 -0.257868714\n","groupstandardisedconcentrations$V9 -0.16325474 -0.178003512\n","groupstandardisedconcentrations$V10 0.06653116 -0.152364015\n","groupstandardisedconcentrations$V11 0.53670086 0.382782544\n","groupstandardisedconcentrations$V12 -0.12801061 -0.237174509\n","groupstandardisedconcentrations$V13 -0.46414916 0.020523349\n","groupstandardisedconcentrations$V14 -0.46385409 0.491738050\n","\n","Proportion of trace:\n"," LD1 LD2 \n","0.6875 0.3125 "]},"metadata":{}}]},{"cell_type":"markdown","source":["It makes sense to interpret the loadings calculated using the group-standardised variables rather than the loadings for the original (unstandardised) variables.\n","\n","In the first discriminant function calculated for the group-standardised variables, the largest loadings (in absolute) value are given to V8 (-0.871), V11 (0.537), V13 (-0.464), V14 (-0.464), and V5 (0.438). The loadings for V8, V13 and V14 are negative, while those for V11 and V5 are positive. **Therefore, the discriminant function seems to represent a contrast between the concentrations of V8, V13 and V14, and the concentrations of V11 and V5.**\n","\n","We saw above that the individual variables which gave the greatest separations between the groups were V8, V14, V13, V2 and V11. These were mostly the same variables that had the largest loadings in the linear discriminant function (loading for V8: -0.871, for V14: -0.464, for V13: -0.464, for V11: 0.537).\n","\n","We found in https://little-book-of-r-for-multivariate-analysis.readthedocs.io/en/latest/src/multivariateanalysis.html that variables V8 and V11 have a negative between-groups covariance (-60.41) and a positive within-groups covariance (0.29). When the between-groups covariance and within-groups covariance for two variables have opposite signs, it indicates that a better separation between groups can be obtained by using a linear combination of those two variables than by using either variable on its own.\n","\n","Thus, given that the two variables V8 and V11 have between-groups and within-groups covariances of opposite signs, and that these are two of the variables that gave the greatest separations between groups when used individually, it is not surprising that these are the two variables that have the largest loadings in the first discriminant function.\n","\n","Note that although the loadings for the group-standardised variables are easier to interpret than the loadings for the unstandardised variables, the values of the discriminant function are the same regardless of whether we standardise the input variables or not.\n","\n","----\n","**end**\n","\n","\n","For example, for wine data, we can calculate the value of the first discriminant function calculated using the unstandardised and group-standardised variables by typing:"],"metadata":{"id":"9y0L2GTd4U2Y"}},{"cell_type":"code","source":["wine.lda.values <- predict(wine.lda, wine[2:14])\n","wine.lda.values$x[,1] # values for the first discriminant function, using the unstandardised data"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":242},"id":"yZW91Ueq4Ur2","executionInfo":{"status":"ok","timestamp":1717498374593,"user_tz":-120,"elapsed":377,"user":{"displayName":"toni Monleon","userId":"05269440626861479105"}},"outputId":"608eecf6-59d3-4c9f-f846-366ee0eba7e6"},"execution_count":69,"outputs":[{"output_type":"display_data","data":{"text/html":["
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data"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":242},"id":"_gS6ZV574tq9","executionInfo":{"status":"ok","timestamp":1717498378494,"user_tz":-120,"elapsed":566,"user":{"displayName":"toni Monleon","userId":"05269440626861479105"}},"outputId":"05f01e72-af55-4ad8-974f-d2ffdbf55eca"},"execution_count":70,"outputs":[{"output_type":"display_data","data":{"text/html":["
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same."],"metadata":{"id":"8I1xEZlC4Ugg"}},{"cell_type":"markdown","source":["**Separation Achieved by the Discriminant Functions**\n","\n","To calculate the separation achieved by each discriminant function, we first need to calculate the value of each discriminant function, by substituting the variables’ values into the linear combination for the discriminant function (eg. -0.403*V2 - 0.165*V3 - 0.369*V4 + 0.155*V5 - 0.002*V6 + 0.618*V7 - 1.661*V8 - 1.496*V9 + 0.134*V10 + 0.355*V11 - 0.818*V12 - 1.158*V13 - 0.003*V14 for the first discriminant function), and then scaling the values of the discriminant function so that their mean is zero.\n","\n","As mentioned above, we can do this using the “predict()” function in R. For example, to calculate the value of the discriminant functions for the wine data, we type:"],"metadata":{"id":"FjnocLyr41l9"}},{"cell_type":"code","source":[" #remember tht we used this to scale data\n"," standardisedconcentrations <- as.data.frame(scale(wine[2:14]))"],"metadata":{"id":"f187xkc7Hv9B","executionInfo":{"status":"ok","timestamp":1717498383060,"user_tz":-120,"elapsed":367,"user":{"displayName":"toni Monleon","userId":"05269440626861479105"}}},"execution_count":71,"outputs":[]},{"cell_type":"code","source":["wine.lda.values <- predict(wine.lda, standardisedconcentrations) #using the standardised data to obtain predictions in the plot of LD1, LD2\n","(wine.lda.values)"],"metadata":{"id":"VYhEFh-I4UVd","colab":{"base_uri":"https://localhost:8080/","height":1000},"executionInfo":{"status":"ok","timestamp":1717498387680,"user_tz":-120,"elapsed":442,"user":{"displayName":"toni Monleon","userId":"05269440626861479105"}},"outputId":"64748aa1-3d3e-4c55-80ce-58932cc026a6"},"execution_count":72,"outputs":[{"output_type":"display_data","data":{"text/html":["
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  115. 2
  116. 2
  117. 2
  118. 2
  119. 2
  120. 2
  121. 2
  122. 2
  123. 2
  124. 2
  125. 2
  126. 2
  127. 2
  128. 2
  129. 2
  130. 2
  131. 3
  132. 3
  133. 3
  134. 3
  135. 3
  136. 3
  137. 3
  138. 3
  139. 3
  140. 3
  141. 3
  142. 3
  143. 3
  144. 3
  145. 3
  146. 3
  147. 3
  148. 3
  149. 3
  150. 3
  151. 3
  152. 3
  153. 3
  154. 3
  155. 3
  156. 3
  157. 3
  158. 3
  159. 3
  160. 3
  161. 3
  162. 3
  163. 3
  164. 3
  165. 3
  166. 3
  167. 3
  168. 3
  169. 3
  170. 3
  171. 3
  172. 3
  173. 3
  174. 3
  175. 3
  176. 3
  177. 3
  178. 3
\n","\n","
\n","\t\n","\t\tLevels:\n","\t\n","\t\n","\t
  1. '1'
  2. '2'
  3. '3'
\n","
\n","\t
$posterior
\n","\t\t
\n","\n","\n","\t\n","\n","\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\n","
A matrix: 178 × 3 of type dbl
123
11.00000003.261633e-093.641123e-18
20.99999963.583115e-078.733373e-17
30.99999772.321357e-067.823824e-14
41.00000003.726442e-121.334086e-16
50.92511797.488190e-022.171038e-07
61.00000003.509635e-111.291915e-17
71.00000002.700215e-111.200162e-17
81.00000001.905769e-102.304906e-16
90.99999996.006630e-082.436593e-15
100.99999909.839361e-071.156433e-13
111.00000007.882912e-101.560206e-18
120.99999991.013615e-076.790111e-14
131.00000003.178992e-081.063435e-14
141.00000001.223118e-103.713231e-21
151.00000002.485909e-136.100898e-21
161.00000001.166982e-084.124099e-13
171.00000001.376802e-081.859659e-13
180.99999039.693950e-062.068110e-12
191.00000003.895138e-134.347985e-20
200.99999946.131886e-076.646280e-13
210.99994925.075849e-052.562782e-14
220.99358756.412399e-035.452579e-08
230.99999981.748064e-071.444852e-18
240.99979802.019753e-041.062379e-12
250.99964173.582615e-041.915839e-13
260.96889403.110601e-021.717736e-09
271.00000001.588585e-089.262203e-16
280.99993496.506220e-052.377313e-11
290.99999277.346039e-062.714336e-14
300.99999346.575811e-065.362422e-13
⋮⋮⋮⋮
1497.236065e-162.140096e-101.0000000
1501.704439e-158.851366e-101.0000000
1511.401212e-142.253888e-091.0000000
1523.346918e-182.381377e-111.0000000
1534.195729e-171.674300e-081.0000000
1545.032898e-166.421173e-111.0000000
1552.069701e-146.419086e-060.9999936
1562.974894e-161.480965e-101.0000000
1574.795601e-162.379184e-111.0000000
1581.569685e-151.613223e-081.0000000
1596.138686e-182.856824e-131.0000000
1603.632449e-183.959640e-121.0000000
1613.039034e-176.637100e-091.0000000
1621.560237e-091.256253e-060.9999987
1631.789043e-101.129453e-040.9998871
1642.344863e-091.161007e-050.9999884
1659.656814e-161.332647e-101.0000000
1661.971666e-144.010267e-081.0000000
1678.333929e-173.794619e-121.0000000
1682.393434e-163.966000e-111.0000000
1691.449826e-131.005755e-091.0000000
1703.794601e-161.460398e-121.0000000
1717.200244e-131.264093e-050.9999874
1721.902559e-187.559555e-111.0000000
1734.071087e-141.126758e-111.0000000
1741.798520e-131.861473e-111.0000000
1753.005318e-141.457729e-091.0000000
1765.033243e-161.463164e-121.0000000
1771.816681e-139.687823e-101.0000000
1781.105427e-173.148141e-131.0000000
\n","
\n","\t
$x
\n","\t\t
\n","\n","\n","\t\n","\n","\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\n","
A matrix: 178 × 2 of type dbl
LD1LD2
1-4.7002441.9791383
2-4.3019581.1704129
3-3.4207201.4291014
4-4.2057544.0028715
5-1.5099820.4512239
6-4.5186893.2131376
7-4.5273783.2691218
8-4.1483483.1041177
9-3.8608291.9533826
10-3.3666241.6786433
11-4.8058792.2353627
12-3.4280762.1751094
13-3.6661022.2624896
14-5.5882462.0547877
15-5.5013143.6130487
16-3.1847522.8895253
17-3.2893702.7658427
18-2.9980931.4251113
19-5.2464043.7098266
20-3.1365311.9768992
21-3.5774780.5624599
22-1.6907710.9134214
23-4.8351500.9147628
24-3.0958900.6173589
25-3.3216470.2984773
26-2.1448220.1636925
27-3.9824282.1751568
28-2.6859141.2185092
29-3.5630951.0381765
30-3.1730161.3778962
⋮⋮⋮
1494.983592 2.034955195
1504.869683 1.808328611
1514.598692 1.872242276
1525.674479 1.825802705
1535.329861 0.582185152
1545.034010 2.277320759
1554.520801-0.006734202
1565.097837 2.001620301
1575.043683 2.511903302
1584.869808 1.091580738
1595.613166 2.984393323
1605.670467 2.273069964
1615.374135 0.762472235
1623.099754 1.941064843
1633.358881 0.548689610
1643.040072 1.456988981
1654.948613 2.189924581
1664.545045 1.219898447
1675.272558 2.716230611
1685.130161 2.291725363
1694.304681 2.391125305
1705.083368 3.157666652
1714.067436 0.318921921
1725.742130 1.467081651
1734.482051 3.307083817
1744.291508 3.390331907
1754.503296 2.083545915
1765.047470 3.196231361
1774.276155 2.431387976
1785.538086 3.042057095
\n","
\n","
\n"],"text/markdown":"$class\n: 1. 1\n2. 1\n3. 1\n4. 1\n5. 1\n6. 1\n7. 1\n8. 1\n9. 1\n10. 1\n11. 1\n12. 1\n13. 1\n14. 1\n15. 1\n16. 1\n17. 1\n18. 1\n19. 1\n20. 1\n21. 1\n22. 1\n23. 1\n24. 1\n25. 1\n26. 1\n27. 1\n28. 1\n29. 1\n30. 1\n31. 1\n32. 1\n33. 1\n34. 1\n35. 1\n36. 1\n37. 1\n38. 1\n39. 1\n40. 1\n41. 1\n42. 1\n43. 1\n44. 1\n45. 1\n46. 1\n47. 1\n48. 1\n49. 1\n50. 1\n51. 1\n52. 1\n53. 1\n54. 1\n55. 1\n56. 1\n57. 1\n58. 1\n59. 1\n60. 2\n61. 2\n62. 2\n63. 2\n64. 2\n65. 2\n66. 2\n67. 2\n68. 2\n69. 2\n70. 2\n71. 2\n72. 2\n73. 2\n74. 2\n75. 2\n76. 2\n77. 2\n78. 2\n79. 2\n80. 2\n81. 2\n82. 2\n83. 2\n84. 2\n85. 2\n86. 2\n87. 2\n88. 2\n89. 2\n90. 2\n91. 2\n92. 2\n93. 2\n94. 2\n95. 2\n96. 2\n97. 2\n98. 2\n99. 2\n100. 2\n101. 2\n102. 2\n103. 2\n104. 2\n105. 2\n106. 2\n107. 2\n108. 2\n109. 2\n110. 2\n111. 2\n112. 2\n113. 2\n114. 2\n115. 2\n116. 2\n117. 2\n118. 2\n119. 2\n120. 2\n121. 2\n122. 2\n123. 2\n124. 2\n125. 2\n126. 2\n127. 2\n128. 2\n129. 2\n130. 2\n131. 3\n132. 3\n133. 3\n134. 3\n135. 3\n136. 3\n137. 3\n138. 3\n139. 3\n140. 3\n141. 3\n142. 3\n143. 3\n144. 3\n145. 3\n146. 3\n147. 3\n148. 3\n149. 3\n150. 3\n151. 3\n152. 3\n153. 3\n154. 3\n155. 3\n156. 3\n157. 3\n158. 3\n159. 3\n160. 3\n161. 3\n162. 3\n163. 3\n164. 3\n165. 3\n166. 3\n167. 3\n168. 3\n169. 3\n170. 3\n171. 3\n172. 3\n173. 3\n174. 3\n175. 3\n176. 3\n177. 3\n178. 3\n\n\n\n**Levels**: 1. '1'\n2. '2'\n3. '3'\n\n\n\n$posterior\n: \nA matrix: 178 × 3 of type dbl\n\n| | 1 | 2 | 3 |\n|---|---|---|---|\n| 1 | 1.0000000 | 3.261633e-09 | 3.641123e-18 |\n| 2 | 0.9999996 | 3.583115e-07 | 8.733373e-17 |\n| 3 | 0.9999977 | 2.321357e-06 | 7.823824e-14 |\n| 4 | 1.0000000 | 3.726442e-12 | 1.334086e-16 |\n| 5 | 0.9251179 | 7.488190e-02 | 2.171038e-07 |\n| 6 | 1.0000000 | 3.509635e-11 | 1.291915e-17 |\n| 7 | 1.0000000 | 2.700215e-11 | 1.200162e-17 |\n| 8 | 1.0000000 | 1.905769e-10 | 2.304906e-16 |\n| 9 | 0.9999999 | 6.006630e-08 | 2.436593e-15 |\n| 10 | 0.9999990 | 9.839361e-07 | 1.156433e-13 |\n| 11 | 1.0000000 | 7.882912e-10 | 1.560206e-18 |\n| 12 | 0.9999999 | 1.013615e-07 | 6.790111e-14 |\n| 13 | 1.0000000 | 3.178992e-08 | 1.063435e-14 |\n| 14 | 1.0000000 | 1.223118e-10 | 3.713231e-21 |\n| 15 | 1.0000000 | 2.485909e-13 | 6.100898e-21 |\n| 16 | 1.0000000 | 1.166982e-08 | 4.124099e-13 |\n| 17 | 1.0000000 | 1.376802e-08 | 1.859659e-13 |\n| 18 | 0.9999903 | 9.693950e-06 | 2.068110e-12 |\n| 19 | 1.0000000 | 3.895138e-13 | 4.347985e-20 |\n| 20 | 0.9999994 | 6.131886e-07 | 6.646280e-13 |\n| 21 | 0.9999492 | 5.075849e-05 | 2.562782e-14 |\n| 22 | 0.9935875 | 6.412399e-03 | 5.452579e-08 |\n| 23 | 0.9999998 | 1.748064e-07 | 1.444852e-18 |\n| 24 | 0.9997980 | 2.019753e-04 | 1.062379e-12 |\n| 25 | 0.9996417 | 3.582615e-04 | 1.915839e-13 |\n| 26 | 0.9688940 | 3.110601e-02 | 1.717736e-09 |\n| 27 | 1.0000000 | 1.588585e-08 | 9.262203e-16 |\n| 28 | 0.9999349 | 6.506220e-05 | 2.377313e-11 |\n| 29 | 0.9999927 | 7.346039e-06 | 2.714336e-14 |\n| 30 | 0.9999934 | 6.575811e-06 | 5.362422e-13 |\n| ⋮ | ⋮ | ⋮ | ⋮ |\n| 149 | 7.236065e-16 | 2.140096e-10 | 1.0000000 |\n| 150 | 1.704439e-15 | 8.851366e-10 | 1.0000000 |\n| 151 | 1.401212e-14 | 2.253888e-09 | 1.0000000 |\n| 152 | 3.346918e-18 | 2.381377e-11 | 1.0000000 |\n| 153 | 4.195729e-17 | 1.674300e-08 | 1.0000000 |\n| 154 | 5.032898e-16 | 6.421173e-11 | 1.0000000 |\n| 155 | 2.069701e-14 | 6.419086e-06 | 0.9999936 |\n| 156 | 2.974894e-16 | 1.480965e-10 | 1.0000000 |\n| 157 | 4.795601e-16 | 2.379184e-11 | 1.0000000 |\n| 158 | 1.569685e-15 | 1.613223e-08 | 1.0000000 |\n| 159 | 6.138686e-18 | 2.856824e-13 | 1.0000000 |\n| 160 | 3.632449e-18 | 3.959640e-12 | 1.0000000 |\n| 161 | 3.039034e-17 | 6.637100e-09 | 1.0000000 |\n| 162 | 1.560237e-09 | 1.256253e-06 | 0.9999987 |\n| 163 | 1.789043e-10 | 1.129453e-04 | 0.9998871 |\n| 164 | 2.344863e-09 | 1.161007e-05 | 0.9999884 |\n| 165 | 9.656814e-16 | 1.332647e-10 | 1.0000000 |\n| 166 | 1.971666e-14 | 4.010267e-08 | 1.0000000 |\n| 167 | 8.333929e-17 | 3.794619e-12 | 1.0000000 |\n| 168 | 2.393434e-16 | 3.966000e-11 | 1.0000000 |\n| 169 | 1.449826e-13 | 1.005755e-09 | 1.0000000 |\n| 170 | 3.794601e-16 | 1.460398e-12 | 1.0000000 |\n| 171 | 7.200244e-13 | 1.264093e-05 | 0.9999874 |\n| 172 | 1.902559e-18 | 7.559555e-11 | 1.0000000 |\n| 173 | 4.071087e-14 | 1.126758e-11 | 1.0000000 |\n| 174 | 1.798520e-13 | 1.861473e-11 | 1.0000000 |\n| 175 | 3.005318e-14 | 1.457729e-09 | 1.0000000 |\n| 176 | 5.033243e-16 | 1.463164e-12 | 1.0000000 |\n| 177 | 1.816681e-13 | 9.687823e-10 | 1.0000000 |\n| 178 | 1.105427e-17 | 3.148141e-13 | 1.0000000 |\n\n\n$x\n: \nA matrix: 178 × 2 of type dbl\n\n| | LD1 | LD2 |\n|---|---|---|\n| 1 | -4.700244 | 1.9791383 |\n| 2 | -4.301958 | 1.1704129 |\n| 3 | -3.420720 | 1.4291014 |\n| 4 | -4.205754 | 4.0028715 |\n| 5 | -1.509982 | 0.4512239 |\n| 6 | -4.518689 | 3.2131376 |\n| 7 | -4.527378 | 3.2691218 |\n| 8 | -4.148348 | 3.1041177 |\n| 9 | -3.860829 | 1.9533826 |\n| 10 | -3.366624 | 1.6786433 |\n| 11 | -4.805879 | 2.2353627 |\n| 12 | -3.428076 | 2.1751094 |\n| 13 | -3.666102 | 2.2624896 |\n| 14 | -5.588246 | 2.0547877 |\n| 15 | -5.501314 | 3.6130487 |\n| 16 | -3.184752 | 2.8895253 |\n| 17 | -3.289370 | 2.7658427 |\n| 18 | -2.998093 | 1.4251113 |\n| 19 | -5.246404 | 3.7098266 |\n| 20 | -3.136531 | 1.9768992 |\n| 21 | -3.577478 | 0.5624599 |\n| 22 | -1.690771 | 0.9134214 |\n| 23 | -4.835150 | 0.9147628 |\n| 24 | -3.095890 | 0.6173589 |\n| 25 | -3.321647 | 0.2984773 |\n| 26 | -2.144822 | 0.1636925 |\n| 27 | -3.982428 | 2.1751568 |\n| 28 | -2.685914 | 1.2185092 |\n| 29 | -3.563095 | 1.0381765 |\n| 30 | -3.173016 | 1.3778962 |\n| ⋮ | ⋮ | ⋮ |\n| 149 | 4.983592 | 2.034955195 |\n| 150 | 4.869683 | 1.808328611 |\n| 151 | 4.598692 | 1.872242276 |\n| 152 | 5.674479 | 1.825802705 |\n| 153 | 5.329861 | 0.582185152 |\n| 154 | 5.034010 | 2.277320759 |\n| 155 | 4.520801 | -0.006734202 |\n| 156 | 5.097837 | 2.001620301 |\n| 157 | 5.043683 | 2.511903302 |\n| 158 | 4.869808 | 1.091580738 |\n| 159 | 5.613166 | 2.984393323 |\n| 160 | 5.670467 | 2.273069964 |\n| 161 | 5.374135 | 0.762472235 |\n| 162 | 3.099754 | 1.941064843 |\n| 163 | 3.358881 | 0.548689610 |\n| 164 | 3.040072 | 1.456988981 |\n| 165 | 4.948613 | 2.189924581 |\n| 166 | 4.545045 | 1.219898447 |\n| 167 | 5.272558 | 2.716230611 |\n| 168 | 5.130161 | 2.291725363 |\n| 169 | 4.304681 | 2.391125305 |\n| 170 | 5.083368 | 3.157666652 |\n| 171 | 4.067436 | 0.318921921 |\n| 172 | 5.742130 | 1.467081651 |\n| 173 | 4.482051 | 3.307083817 |\n| 174 | 4.291508 | 3.390331907 |\n| 175 | 4.503296 | 2.083545915 |\n| 176 | 5.047470 | 3.196231361 |\n| 177 | 4.276155 | 2.431387976 |\n| 178 | 5.538086 | 3.042057095 |\n\n\n\n\n","text/latex":"\\begin{description}\n\\item[\\$class] \\begin{enumerate*}\n\\item 1\n\\item 1\n\\item 1\n\\item 1\n\\item 1\n\\item 1\n\\item 1\n\\item 1\n\\item 1\n\\item 1\n\\item 1\n\\item 1\n\\item 1\n\\item 1\n\\item 1\n\\item 1\n\\item 1\n\\item 1\n\\item 1\n\\item 1\n\\item 1\n\\item 1\n\\item 1\n\\item 1\n\\item 1\n\\item 1\n\\item 1\n\\item 1\n\\item 1\n\\item 1\n\\item 1\n\\item 1\n\\item 1\n\\item 1\n\\item 1\n\\item 1\n\\item 1\n\\item 1\n\\item 1\n\\item 1\n\\item 1\n\\item 1\n\\item 1\n\\item 1\n\\item 1\n\\item 1\n\\item 1\n\\item 1\n\\item 1\n\\item 1\n\\item 1\n\\item 1\n\\item 1\n\\item 1\n\\item 1\n\\item 1\n\\item 1\n\\item 1\n\\item 1\n\\item 2\n\\item 2\n\\item 2\n\\item 2\n\\item 2\n\\item 2\n\\item 2\n\\item 2\n\\item 2\n\\item 2\n\\item 2\n\\item 2\n\\item 2\n\\item 2\n\\item 2\n\\item 2\n\\item 2\n\\item 2\n\\item 2\n\\item 2\n\\item 2\n\\item 2\n\\item 2\n\\item 2\n\\item 2\n\\item 2\n\\item 2\n\\item 2\n\\item 2\n\\item 2\n\\item 2\n\\item 2\n\\item 2\n\\item 2\n\\item 2\n\\item 2\n\\item 2\n\\item 2\n\\item 2\n\\item 2\n\\item 2\n\\item 2\n\\item 2\n\\item 2\n\\item 2\n\\item 2\n\\item 2\n\\item 2\n\\item 2\n\\item 2\n\\item 2\n\\item 2\n\\item 2\n\\item 2\n\\item 2\n\\item 2\n\\item 2\n\\item 2\n\\item 2\n\\item 2\n\\item 2\n\\item 2\n\\item 2\n\\item 2\n\\item 2\n\\item 2\n\\item 2\n\\item 2\n\\item 2\n\\item 2\n\\item 2\n\\item 3\n\\item 3\n\\item 3\n\\item 3\n\\item 3\n\\item 3\n\\item 3\n\\item 3\n\\item 3\n\\item 3\n\\item 3\n\\item 3\n\\item 3\n\\item 3\n\\item 3\n\\item 3\n\\item 3\n\\item 3\n\\item 3\n\\item 3\n\\item 3\n\\item 3\n\\item 3\n\\item 3\n\\item 3\n\\item 3\n\\item 3\n\\item 3\n\\item 3\n\\item 3\n\\item 3\n\\item 3\n\\item 3\n\\item 3\n\\item 3\n\\item 3\n\\item 3\n\\item 3\n\\item 3\n\\item 3\n\\item 3\n\\item 3\n\\item 3\n\\item 3\n\\item 3\n\\item 3\n\\item 3\n\\item 3\n\\end{enumerate*}\n\n\\emph{Levels}: \\begin{enumerate*}\n\\item '1'\n\\item '2'\n\\item '3'\n\\end{enumerate*}\n\n\\item[\\$posterior] A matrix: 178 × 3 of type dbl\n\\begin{tabular}{r|lll}\n & 1 & 2 & 3\\\\\n\\hline\n\t1 & 1.0000000 & 3.261633e-09 & 3.641123e-18\\\\\n\t2 & 0.9999996 & 3.583115e-07 & 8.733373e-17\\\\\n\t3 & 0.9999977 & 2.321357e-06 & 7.823824e-14\\\\\n\t4 & 1.0000000 & 3.726442e-12 & 1.334086e-16\\\\\n\t5 & 0.9251179 & 7.488190e-02 & 2.171038e-07\\\\\n\t6 & 1.0000000 & 3.509635e-11 & 1.291915e-17\\\\\n\t7 & 1.0000000 & 2.700215e-11 & 1.200162e-17\\\\\n\t8 & 1.0000000 & 1.905769e-10 & 2.304906e-16\\\\\n\t9 & 0.9999999 & 6.006630e-08 & 2.436593e-15\\\\\n\t10 & 0.9999990 & 9.839361e-07 & 1.156433e-13\\\\\n\t11 & 1.0000000 & 7.882912e-10 & 1.560206e-18\\\\\n\t12 & 0.9999999 & 1.013615e-07 & 6.790111e-14\\\\\n\t13 & 1.0000000 & 3.178992e-08 & 1.063435e-14\\\\\n\t14 & 1.0000000 & 1.223118e-10 & 3.713231e-21\\\\\n\t15 & 1.0000000 & 2.485909e-13 & 6.100898e-21\\\\\n\t16 & 1.0000000 & 1.166982e-08 & 4.124099e-13\\\\\n\t17 & 1.0000000 & 1.376802e-08 & 1.859659e-13\\\\\n\t18 & 0.9999903 & 9.693950e-06 & 2.068110e-12\\\\\n\t19 & 1.0000000 & 3.895138e-13 & 4.347985e-20\\\\\n\t20 & 0.9999994 & 6.131886e-07 & 6.646280e-13\\\\\n\t21 & 0.9999492 & 5.075849e-05 & 2.562782e-14\\\\\n\t22 & 0.9935875 & 6.412399e-03 & 5.452579e-08\\\\\n\t23 & 0.9999998 & 1.748064e-07 & 1.444852e-18\\\\\n\t24 & 0.9997980 & 2.019753e-04 & 1.062379e-12\\\\\n\t25 & 0.9996417 & 3.582615e-04 & 1.915839e-13\\\\\n\t26 & 0.9688940 & 3.110601e-02 & 1.717736e-09\\\\\n\t27 & 1.0000000 & 1.588585e-08 & 9.262203e-16\\\\\n\t28 & 0.9999349 & 6.506220e-05 & 2.377313e-11\\\\\n\t29 & 0.9999927 & 7.346039e-06 & 2.714336e-14\\\\\n\t30 & 0.9999934 & 6.575811e-06 & 5.362422e-13\\\\\n\t⋮ & ⋮ & ⋮ & ⋮\\\\\n\t149 & 7.236065e-16 & 2.140096e-10 & 1.0000000\\\\\n\t150 & 1.704439e-15 & 8.851366e-10 & 1.0000000\\\\\n\t151 & 1.401212e-14 & 2.253888e-09 & 1.0000000\\\\\n\t152 & 3.346918e-18 & 2.381377e-11 & 1.0000000\\\\\n\t153 & 4.195729e-17 & 1.674300e-08 & 1.0000000\\\\\n\t154 & 5.032898e-16 & 6.421173e-11 & 1.0000000\\\\\n\t155 & 2.069701e-14 & 6.419086e-06 & 0.9999936\\\\\n\t156 & 2.974894e-16 & 1.480965e-10 & 1.0000000\\\\\n\t157 & 4.795601e-16 & 2.379184e-11 & 1.0000000\\\\\n\t158 & 1.569685e-15 & 1.613223e-08 & 1.0000000\\\\\n\t159 & 6.138686e-18 & 2.856824e-13 & 1.0000000\\\\\n\t160 & 3.632449e-18 & 3.959640e-12 & 1.0000000\\\\\n\t161 & 3.039034e-17 & 6.637100e-09 & 1.0000000\\\\\n\t162 & 1.560237e-09 & 1.256253e-06 & 0.9999987\\\\\n\t163 & 1.789043e-10 & 1.129453e-04 & 0.9998871\\\\\n\t164 & 2.344863e-09 & 1.161007e-05 & 0.9999884\\\\\n\t165 & 9.656814e-16 & 1.332647e-10 & 1.0000000\\\\\n\t166 & 1.971666e-14 & 4.010267e-08 & 1.0000000\\\\\n\t167 & 8.333929e-17 & 3.794619e-12 & 1.0000000\\\\\n\t168 & 2.393434e-16 & 3.966000e-11 & 1.0000000\\\\\n\t169 & 1.449826e-13 & 1.005755e-09 & 1.0000000\\\\\n\t170 & 3.794601e-16 & 1.460398e-12 & 1.0000000\\\\\n\t171 & 7.200244e-13 & 1.264093e-05 & 0.9999874\\\\\n\t172 & 1.902559e-18 & 7.559555e-11 & 1.0000000\\\\\n\t173 & 4.071087e-14 & 1.126758e-11 & 1.0000000\\\\\n\t174 & 1.798520e-13 & 1.861473e-11 & 1.0000000\\\\\n\t175 & 3.005318e-14 & 1.457729e-09 & 1.0000000\\\\\n\t176 & 5.033243e-16 & 1.463164e-12 & 1.0000000\\\\\n\t177 & 1.816681e-13 & 9.687823e-10 & 1.0000000\\\\\n\t178 & 1.105427e-17 & 3.148141e-13 & 1.0000000\\\\\n\\end{tabular}\n\n\\item[\\$x] A matrix: 178 × 2 of type dbl\n\\begin{tabular}{r|ll}\n & LD1 & LD2\\\\\n\\hline\n\t1 & -4.700244 & 1.9791383\\\\\n\t2 & -4.301958 & 1.1704129\\\\\n\t3 & -3.420720 & 1.4291014\\\\\n\t4 & -4.205754 & 4.0028715\\\\\n\t5 & -1.509982 & 0.4512239\\\\\n\t6 & -4.518689 & 3.2131376\\\\\n\t7 & -4.527378 & 3.2691218\\\\\n\t8 & -4.148348 & 3.1041177\\\\\n\t9 & -3.860829 & 1.9533826\\\\\n\t10 & -3.366624 & 1.6786433\\\\\n\t11 & -4.805879 & 2.2353627\\\\\n\t12 & -3.428076 & 2.1751094\\\\\n\t13 & -3.666102 & 2.2624896\\\\\n\t14 & -5.588246 & 2.0547877\\\\\n\t15 & -5.501314 & 3.6130487\\\\\n\t16 & -3.184752 & 2.8895253\\\\\n\t17 & -3.289370 & 2.7658427\\\\\n\t18 & -2.998093 & 1.4251113\\\\\n\t19 & -5.246404 & 3.7098266\\\\\n\t20 & -3.136531 & 1.9768992\\\\\n\t21 & -3.577478 & 0.5624599\\\\\n\t22 & -1.690771 & 0.9134214\\\\\n\t23 & -4.835150 & 0.9147628\\\\\n\t24 & -3.095890 & 0.6173589\\\\\n\t25 & -3.321647 & 0.2984773\\\\\n\t26 & -2.144822 & 0.1636925\\\\\n\t27 & -3.982428 & 2.1751568\\\\\n\t28 & -2.685914 & 1.2185092\\\\\n\t29 & -3.563095 & 1.0381765\\\\\n\t30 & -3.173016 & 1.3778962\\\\\n\t⋮ & ⋮ & ⋮\\\\\n\t149 & 4.983592 & 2.034955195\\\\\n\t150 & 4.869683 & 1.808328611\\\\\n\t151 & 4.598692 & 1.872242276\\\\\n\t152 & 5.674479 & 1.825802705\\\\\n\t153 & 5.329861 & 0.582185152\\\\\n\t154 & 5.034010 & 2.277320759\\\\\n\t155 & 4.520801 & -0.006734202\\\\\n\t156 & 5.097837 & 2.001620301\\\\\n\t157 & 5.043683 & 2.511903302\\\\\n\t158 & 4.869808 & 1.091580738\\\\\n\t159 & 5.613166 & 2.984393323\\\\\n\t160 & 5.670467 & 2.273069964\\\\\n\t161 & 5.374135 & 0.762472235\\\\\n\t162 & 3.099754 & 1.941064843\\\\\n\t163 & 3.358881 & 0.548689610\\\\\n\t164 & 3.040072 & 1.456988981\\\\\n\t165 & 4.948613 & 2.189924581\\\\\n\t166 & 4.545045 & 1.219898447\\\\\n\t167 & 5.272558 & 2.716230611\\\\\n\t168 & 5.130161 & 2.291725363\\\\\n\t169 & 4.304681 & 2.391125305\\\\\n\t170 & 5.083368 & 3.157666652\\\\\n\t171 & 4.067436 & 0.318921921\\\\\n\t172 & 5.742130 & 1.467081651\\\\\n\t173 & 4.482051 & 3.307083817\\\\\n\t174 & 4.291508 & 3.390331907\\\\\n\t175 & 4.503296 & 2.083545915\\\\\n\t176 & 5.047470 & 3.196231361\\\\\n\t177 & 4.276155 & 2.431387976\\\\\n\t178 & 5.538086 & 3.042057095\\\\\n\\end{tabular}\n\n\\end{description}\n","text/plain":["$class\n"," [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n"," [38] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2\n"," [75] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2\n","[112] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3\n","[149] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3\n","Levels: 1 2 3\n","\n","$posterior\n"," 1 2 3\n","1 1.000000e+00 3.261633e-09 3.641123e-18\n","2 9.999996e-01 3.583115e-07 8.733373e-17\n","3 9.999977e-01 2.321357e-06 7.823824e-14\n","4 1.000000e+00 3.726442e-12 1.334086e-16\n","5 9.251179e-01 7.488190e-02 2.171038e-07\n","6 1.000000e+00 3.509635e-11 1.291915e-17\n","7 1.000000e+00 2.700215e-11 1.200162e-17\n","8 1.000000e+00 1.905769e-10 2.304906e-16\n","9 9.999999e-01 6.006630e-08 2.436593e-15\n","10 9.999990e-01 9.839361e-07 1.156433e-13\n","11 1.000000e+00 7.882912e-10 1.560206e-18\n","12 9.999999e-01 1.013615e-07 6.790111e-14\n","13 1.000000e+00 3.178992e-08 1.063435e-14\n","14 1.000000e+00 1.223118e-10 3.713231e-21\n","15 1.000000e+00 2.485909e-13 6.100898e-21\n","16 1.000000e+00 1.166982e-08 4.124099e-13\n","17 1.000000e+00 1.376802e-08 1.859659e-13\n","18 9.999903e-01 9.693950e-06 2.068110e-12\n","19 1.000000e+00 3.895138e-13 4.347985e-20\n","20 9.999994e-01 6.131886e-07 6.646280e-13\n","21 9.999492e-01 5.075849e-05 2.562782e-14\n","22 9.935875e-01 6.412399e-03 5.452579e-08\n","23 9.999998e-01 1.748064e-07 1.444852e-18\n","24 9.997980e-01 2.019753e-04 1.062379e-12\n","25 9.996417e-01 3.582615e-04 1.915839e-13\n","26 9.688940e-01 3.110601e-02 1.717736e-09\n","27 1.000000e+00 1.588585e-08 9.262203e-16\n","28 9.999349e-01 6.506220e-05 2.377313e-11\n","29 9.999927e-01 7.346039e-06 2.714336e-14\n","30 9.999934e-01 6.575811e-06 5.362422e-13\n","31 9.999852e-01 1.484746e-05 2.121715e-12\n","32 1.000000e+00 3.179670e-08 2.242335e-14\n","33 9.995655e-01 4.344811e-04 1.185090e-13\n","34 9.999995e-01 4.919632e-07 3.310376e-14\n","35 9.999869e-01 1.306947e-05 6.385151e-12\n","36 9.973176e-01 2.682405e-03 1.146146e-11\n","37 9.999882e-01 1.184203e-05 1.306068e-11\n","38 9.991533e-01 8.467217e-04 1.250642e-09\n","39 9.912901e-01 8.709907e-03 1.944354e-12\n","40 1.000000e+00 2.871314e-08 2.182948e-13\n","41 9.983632e-01 1.636779e-03 4.296391e-12\n","42 9.989880e-01 1.011957e-03 7.945497e-10\n","43 1.000000e+00 2.899414e-10 3.422129e-18\n","44 8.115443e-01 1.884540e-01 1.667243e-06\n","45 9.404681e-01 5.953191e-02 5.974783e-11\n","46 9.999999e-01 9.191235e-08 4.367056e-11\n","47 1.000000e+00 3.633049e-08 1.938931e-15\n","48 9.999846e-01 1.537223e-05 6.595729e-14\n","49 9.999158e-01 8.415562e-05 3.325537e-10\n","50 9.999999e-01 1.098285e-07 3.350431e-14\n","51 9.999549e-01 4.508965e-05 3.992197e-13\n","52 1.000000e+00 4.361162e-09 4.191204e-17\n","53 1.000000e+00 2.975169e-09 4.798328e-17\n","54 1.000000e+00 2.335804e-09 3.050225e-14\n","55 9.999968e-01 3.168385e-06 7.722646e-13\n","56 9.992167e-01 7.832445e-04 2.202455e-08\n","57 9.999959e-01 4.127994e-06 5.233342e-12\n","58 1.000000e+00 3.325554e-08 1.551762e-14\n","59 1.000000e+00 1.095210e-08 5.925886e-15\n","60 2.496185e-09 9.999788e-01 2.122437e-05\n","61 2.520506e-06 9.999544e-01 4.304963e-05\n","62 9.708421e-09 9.653154e-01 3.468461e-02\n","63 3.954589e-04 9.996042e-01 3.837475e-07\n","64 1.239069e-07 9.999999e-01 1.036785e-10\n","65 3.049453e-08 9.999999e-01 2.630717e-08\n","66 8.148179e-04 9.991852e-01 2.231901e-08\n","67 1.124532e-04 9.998875e-01 1.195564e-12\n","68 7.916083e-07 9.999992e-01 2.142607e-09\n","69 6.608591e-03 9.924414e-01 9.499911e-04\n","70 2.880586e-07 9.999997e-01 1.710365e-09\n","71 5.517252e-06 9.982998e-01 1.694708e-03\n","72 2.206427e-04 9.997794e-01 1.917175e-09\n","73 3.460675e-06 9.999791e-01 1.739046e-05\n","74 2.460929e-03 9.975391e-01 1.188193e-10\n","75 7.877233e-05 9.999210e-01 1.882415e-07\n","76 6.880809e-10 9.999998e-01 2.032388e-07\n","77 9.508745e-08 9.999999e-01 1.626127e-08\n","78 3.060615e-07 9.999877e-01 1.198380e-05\n","79 1.864429e-05 9.999814e-01 2.946738e-09\n","80 2.790988e-06 9.999972e-01 1.090616e-09\n","81 3.005681e-10 1.000000e+00 2.015746e-13\n","82 1.020957e-02 9.897904e-01 3.053515e-10\n","83 4.047302e-08 9.999999e-01 9.930801e-09\n","84 4.499907e-07 8.972724e-01 1.027272e-01\n","85 8.249501e-05 9.999175e-01 2.417933e-08\n","86 1.475382e-05 9.999852e-01 1.924970e-10\n","87 5.478239e-10 1.000000e+00 9.099192e-09\n","88 5.426103e-09 1.000000e+00 3.515237e-09\n","89 1.096344e-06 9.999988e-01 1.358075e-07\n","90 7.759467e-08 9.999999e-01 1.016527e-09\n","91 6.193200e-08 9.999998e-01 1.018648e-07\n","92 2.887574e-09 9.999943e-01 5.740564e-06\n","93 4.805769e-08 9.999977e-01 2.292493e-06\n","94 4.239848e-07 9.999996e-01 1.806643e-10\n","95 1.137306e-08 1.000000e+00 2.169411e-08\n","96 6.165573e-04 9.993834e-01 7.789350e-11\n","97 9.084754e-07 8.438891e-01 1.561100e-01\n","98 3.301184e-07 9.999997e-01 5.936754e-12\n","99 1.517906e-04 9.998482e-01 6.599749e-13\n","100 2.814446e-07 9.999997e-01 4.000107e-14\n","101 1.568781e-06 9.999984e-01 1.793826e-11\n","102 6.144220e-07 9.999994e-01 2.917636e-08\n","103 2.692405e-05 9.999729e-01 1.769766e-07\n","104 1.580737e-11 1.000000e+00 2.823873e-08\n","105 3.269330e-05 9.999673e-01 2.012488e-10\n","106 9.387183e-08 9.999999e-01 7.552388e-10\n","107 5.534996e-06 9.999945e-01 5.195827e-10\n","108 1.781183e-07 9.999990e-01 7.981622e-07\n","109 7.737830e-10 1.000000e+00 2.031331e-10\n","110 1.231762e-03 9.987682e-01 6.488025e-12\n","111 2.044858e-10 1.000000e+00 4.282228e-09\n","112 3.885179e-08 9.999999e-01 1.650185e-08\n","113 6.886971e-04 9.993108e-01 4.576745e-07\n","114 2.523764e-10 1.000000e+00 2.536709e-08\n","115 2.201920e-07 9.999998e-01 1.641537e-09\n","116 5.771512e-13 1.000000e+00 1.911248e-15\n","117 5.154331e-09 1.000000e+00 3.495310e-10\n","118 4.675000e-09 1.000000e+00 1.032802e-10\n","119 8.376742e-07 9.693670e-01 3.063215e-02\n","120 3.765197e-07 9.999996e-01 3.918935e-09\n","121 6.406351e-05 9.999359e-01 2.646662e-10\n","122 3.081851e-03 9.969181e-01 2.009740e-15\n","123 3.676696e-07 9.999979e-01 1.751668e-06\n","124 3.396939e-05 9.999645e-01 1.533926e-06\n","125 4.475589e-06 9.999955e-01 7.806592e-10\n","126 1.811570e-07 9.999998e-01 2.929478e-11\n","127 4.442480e-07 9.999996e-01 3.273236e-10\n","128 2.405754e-10 9.999999e-01 9.030136e-08\n","129 1.138345e-09 1.000000e+00 1.205023e-10\n","130 1.329868e-06 9.997235e-01 2.751561e-04\n","131 8.923808e-07 6.153941e-02 9.384597e-01\n","132 3.217713e-10 8.597404e-05 9.999140e-01\n","133 6.794305e-13 2.509693e-05 9.999749e-01\n","134 3.488842e-12 1.273963e-05 9.999873e-01\n","135 8.028169e-11 1.674312e-03 9.983257e-01\n","136 1.128387e-11 2.193992e-05 9.999781e-01\n","137 2.367710e-12 2.337355e-06 9.999977e-01\n","138 1.654617e-15 4.076869e-07 9.999996e-01\n","139 4.245868e-11 1.099872e-05 9.999890e-01\n","140 7.197671e-12 6.872235e-05 9.999313e-01\n","141 3.995118e-08 2.135381e-04 9.997864e-01\n","142 6.317509e-07 3.699549e-05 9.999624e-01\n","143 7.751488e-10 3.171845e-05 9.999683e-01\n","144 7.268153e-09 7.589190e-05 9.999241e-01\n","145 7.884375e-12 4.731136e-08 1.000000e+00\n","146 2.152927e-09 7.834667e-05 9.999217e-01\n","147 6.018765e-15 1.744298e-09 1.000000e+00\n","148 2.073985e-15 1.909049e-10 1.000000e+00\n","149 7.236065e-16 2.140096e-10 1.000000e+00\n","150 1.704439e-15 8.851366e-10 1.000000e+00\n","151 1.401212e-14 2.253888e-09 1.000000e+00\n","152 3.346918e-18 2.381377e-11 1.000000e+00\n","153 4.195729e-17 1.674300e-08 1.000000e+00\n","154 5.032898e-16 6.421173e-11 1.000000e+00\n","155 2.069701e-14 6.419086e-06 9.999936e-01\n","156 2.974894e-16 1.480965e-10 1.000000e+00\n","157 4.795601e-16 2.379184e-11 1.000000e+00\n","158 1.569685e-15 1.613223e-08 1.000000e+00\n","159 6.138686e-18 2.856824e-13 1.000000e+00\n","160 3.632449e-18 3.959640e-12 1.000000e+00\n","161 3.039034e-17 6.637100e-09 1.000000e+00\n","162 1.560237e-09 1.256253e-06 9.999987e-01\n","163 1.789043e-10 1.129453e-04 9.998871e-01\n","164 2.344863e-09 1.161007e-05 9.999884e-01\n","165 9.656814e-16 1.332647e-10 1.000000e+00\n","166 1.971666e-14 4.010267e-08 1.000000e+00\n","167 8.333929e-17 3.794619e-12 1.000000e+00\n","168 2.393434e-16 3.966000e-11 1.000000e+00\n","169 1.449826e-13 1.005755e-09 1.000000e+00\n","170 3.794601e-16 1.460398e-12 1.000000e+00\n","171 7.200244e-13 1.264093e-05 9.999874e-01\n","172 1.902559e-18 7.559555e-11 1.000000e+00\n","173 4.071087e-14 1.126758e-11 1.000000e+00\n","174 1.798520e-13 1.861473e-11 1.000000e+00\n","175 3.005318e-14 1.457729e-09 1.000000e+00\n","176 5.033243e-16 1.463164e-12 1.000000e+00\n","177 1.816681e-13 9.687823e-10 1.000000e+00\n","178 1.105427e-17 3.148141e-13 1.000000e+00\n","\n","$x\n"," LD1 LD2\n","1 -4.70024401 1.979138347\n","2 -4.30195811 1.170412858\n","3 -3.42071952 1.429101388\n","4 -4.20575366 4.002871483\n","5 -1.50998168 0.451223898\n","6 -4.51868934 3.213137563\n","7 -4.52737794 3.269121791\n","8 -4.14834781 3.104117653\n","9 -3.86082876 1.953382629\n","10 -3.36662444 1.678643269\n","11 -4.80587907 2.235362714\n","12 -3.42807646 2.175109393\n","13 -3.66610246 2.262489607\n","14 -5.58824635 2.054787732\n","15 -5.50131449 3.613048652\n","16 -3.18475189 2.889525284\n","17 -3.28936988 2.765842660\n","18 -2.99809262 1.425111322\n","19 -5.24640372 3.709826553\n","20 -3.13653106 1.976899222\n","21 -3.57747791 0.562459905\n","22 -1.69077135 0.913421363\n","23 -4.83515033 0.914762801\n","24 -3.09588961 0.617358884\n","25 -3.32164716 0.298477344\n","26 -2.14482223 0.163692467\n","27 -3.98242850 2.175156795\n","28 -2.68591432 1.218509238\n","29 -3.56309464 1.038176511\n","30 -3.17301573 1.377896245\n","31 -2.99626797 1.324198961\n","32 -3.56866244 2.340654779\n","33 -3.38506383 0.201234260\n","34 -3.52753750 1.715927389\n","35 -2.85190852 1.470707713\n","36 -2.79411996 0.237930930\n","37 -2.75808511 1.569704208\n","38 -2.17734477 1.010364553\n","39 -3.02926382 -0.235095828\n","40 -3.27105228 2.604045903\n","41 -2.92065533 0.255233429\n","42 -2.23721062 0.919461164\n","43 -4.69972568 2.560753392\n","44 -1.23036133 0.422595151\n","45 -2.58203904 -0.350291948\n","46 -2.58312049 2.876865723\n","47 -3.88887889 2.051604078\n","48 -3.44975356 0.951839171\n","49 -2.34223331 1.432589499\n","50 -3.52062596 2.081553565\n","51 -3.21840912 0.879128696\n","52 -4.38214896 2.164715728\n","53 -4.36311727 2.271829285\n","54 -3.51917293 3.007373725\n","55 -3.12277475 1.593566695\n","56 -1.80240540 1.330061557\n","57 -2.87378754 1.729899425\n","58 -3.61690518 2.291157529\n","59 -3.73868551 2.460118029\n","60 1.58618749 -2.423844156\n","61 0.79967216 -1.394064606\n","62 2.38015446 -1.451886585\n","63 -0.45917726 -1.190453649\n","64 -0.50726885 -3.166624034\n","65 0.39398359 -2.779841704\n","66 -0.92256616 -1.388723683\n","67 -1.95549377 -2.693606293\n","68 -0.34732815 -2.592899030\n","69 0.20371212 0.019621350\n","70 -0.24831914 -2.756176101\n","71 1.17987999 -0.900342766\n","72 -1.07718925 -1.826701180\n","73 0.64100179 -1.445313675\n","74 -1.74684421 -1.784558589\n","75 -0.34721117 -1.488106819\n","76 1.14274222 -3.089248998\n","77 0.18665882 -2.673170958\n","78 0.90052500 -1.819423568\n","79 -0.70709551 -2.123044489\n","80 -0.59562833 -2.489622452\n","81 -0.55761818 -4.653037777\n","82 -1.80430417 -1.487149451\n","83 0.23077079 -2.842875470\n","84 2.03482711 -0.790320030\n","85 -0.62113021 -1.696895883\n","86 -1.03372742 -2.441437623\n","87 0.76598781 -3.446414023\n","88 0.35042568 -3.229356981\n","89 0.15324508 -2.112877671\n","90 -0.14962842 -2.991932096\n","91 0.48079504 -2.540024080\n","92 1.39689016 -2.540822912\n","93 0.91972331 -2.248596651\n","94 -0.59102937 -2.938453935\n","95 0.49411386 -2.936310763\n","96 -1.62614426 -2.020495450\n","97 2.00044562 -0.634484640\n","98 -1.00534818 -3.331125861\n","99 -2.07121314 -2.714454204\n","100 -1.63815890 -3.877396541\n","101 -1.05894340 -2.999872629\n","102 0.02594549 -2.354113884\n","103 -0.21887407 -1.642896008\n","104 1.36437640 -3.817471743\n","105 -1.12901245 -2.326852449\n","106 -0.21263094 -2.996775815\n","107 -0.77946884 -2.472773916\n","108 0.61546732 -2.178239872\n","109 0.22550192 -3.797341593\n","110 -2.03869851 -2.185325224\n","111 0.79274716 -3.661575980\n","112 0.30229545 -2.795278730\n","113 -0.50664882 -1.095273408\n","114 0.99837397 -3.445986749\n","115 -0.21954922 -2.797597691\n","116 -0.37131517 -6.005610308\n","117 0.05545894 -3.478469699\n","118 -0.09137874 -3.619777332\n","119 1.79755252 -0.850121765\n","120 -0.17405009 -2.632249713\n","121 -1.17870281 -2.205192262\n","122 -3.21054390 -2.905311694\n","123 0.62605202 -1.995708659\n","124 0.03366613 -1.384359759\n","125 -0.69930080 -2.459439566\n","126 -0.72061079 -3.246665203\n","127 -0.51933512 -2.869693250\n","128 1.17030045 -3.319478636\n","129 0.10824791 -3.798761426\n","130 1.12319783 -1.287848151\n","131 2.24632419 0.187347873\n","132 3.28527755 0.696086249\n","133 4.07236441 0.144257515\n","134 3.86691235 0.535033573\n","135 3.45088333 -0.217345358\n","136 3.71583899 0.565101301\n","137 3.92220510 0.893526217\n","138 4.85161020 0.314068518\n","139 3.54993389 0.915963299\n","140 3.76889174 0.225541129\n","141 2.66942250 1.141090760\n","142 2.32491492 1.948483302\n","143 3.17712883 1.059853166\n","144 2.88964418 1.157059225\n","145 3.78325562 2.007393044\n","146 3.04411324 0.981243704\n","147 4.70697017 1.817782774\n","148 4.85021393 2.208182127\n","149 4.98359184 2.034955195\n","150 4.86968293 1.808328611\n","151 4.59869190 1.872242276\n","152 5.67447884 1.825802705\n","153 5.32986123 0.582185152\n","154 5.03401031 2.277320759\n","155 4.52080087 -0.006734202\n","156 5.09783710 2.001620301\n","157 5.04368277 2.511903302\n","158 4.86980829 1.091580738\n","159 5.61316558 2.984393323\n","160 5.67046737 2.273069964\n","161 5.37413513 0.762472235\n","162 3.09975377 1.941064843\n","163 3.35888137 0.548689610\n","164 3.04007194 1.456988981\n","165 4.94861303 2.189924581\n","166 4.54504458 1.219898447\n","167 5.27255844 2.716230611\n","168 5.13016117 2.291725363\n","169 4.30468082 2.391125305\n","170 5.08336782 3.157666652\n","171 4.06743571 0.318921921\n","172 5.74212961 1.467081651\n","173 4.48205140 3.307083817\n","174 4.29150758 3.390331907\n","175 4.50329623 2.083545915\n","176 5.04747033 3.196231361\n","177 4.27615505 2.431387976\n","178 5.53808610 3.042057095\n"]},"metadata":{}}]},{"cell_type":"markdown","source":["The returned variable has a named element “x” which is a matrix containing the linear discriminant functions: the first column of x contains the first discriminant function, the second column of x contains the second discriminant function, and so on (if there are more discriminant functions).\n","\n","**Proportion of trace of each discriminant component:**\n","\n","Proportion of trace:\n"," LD1 LD2\n","0.6875 0.3125\n","\n","\n","----\n","**THIS SECTION IS ONLY TO EXPAND THEORETICAL CONCEPTS AND TO LEARN HOW THIS STATISTICAL METHOD WORKS**\n","\n","We can therefore calculate the separations achieved by the two linear discriminant functions for the wine data by using the “calcSeparations()” function (see above), which calculates the separation as the ratio of the between-groups variance to the within-groups variance:"],"metadata":{"id":"ZwS1MloU4UJi"}},{"cell_type":"code","source":["#uplod this function in order to calculate the between-groups variance for a particular variable (eg. V2) using the function “calcBetweenGroupsVariance()” below:\n","calcBetweenGroupsVariance <- function(variable,groupvariable)\n"," {\n"," # find out how many values the group variable can take\n"," groupvariable2 <- as.factor(groupvariable[[1]])\n"," levels <- levels(groupvariable2)\n"," numlevels <- length(levels)\n"," # calculate the overall grand mean:\n"," grandmean <- mean(unlist(variable))\n"," # get the mean and standard deviation for each group:\n"," numtotal <- 0\n"," denomtotal <- 0\n"," for (i in 1:numlevels)\n"," {\n"," leveli <- levels[i]\n"," levelidata <- variable[groupvariable==leveli,]\n"," levelilength <- length(levelidata)\n"," # get the mean and standard deviation for group i:\n"," #meani <- mean(levelidata)\n"," meani <- mean(unlist(levelidata))\n"," sdi <- sd(levelidata)\n"," numi <- levelilength * ((meani - grandmean)^2)\n"," denomi <- levelilength\n"," numtotal <- numtotal + numi\n"," denomtotal <- denomtotal + denomi\n"," }\n"," # calculate the between-groups variance\n"," Vb <- numtotal / (numlevels - 1)\n"," Vb <- Vb[[1]]\n"," return(Vb)\n"," }\n","\n","\n","#If you want to calculate the separations achieved by all of the variables in a multivariate data set, you can use the function “calcSeparations()” below:\n","calcSeparations <- function(variables,groupvariable)\n"," {\n"," # find out how many variables we have\n"," variables <- as.data.frame(variables)\n"," numvariables <- length(variables)\n"," # find the variable names\n"," variablenames <- colnames(variables)\n"," # calculate the separation for each variable\n"," for (i in 1:numvariables)\n"," {\n"," variablei <- variables[i]\n"," variablename <- variablenames[i]\n"," Vw <- calcWithinGroupsVariance(variablei, groupvariable)\n"," Vb <- calcBetweenGroupsVariance(variablei, groupvariable)\n"," sep <- Vb/Vw\n"," print(paste(\"variable\",variablename,\"Vw=\",Vw,\"Vb=\",Vb,\"separation=\",sep))\n"," }\n"," }\n","\n","calcSeparations(wine.lda.values$x,wine[1])"],"metadata":{"id":"j8mOs1Yx4T9-","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1717498396667,"user_tz":-120,"elapsed":968,"user":{"displayName":"toni Monleon","userId":"05269440626861479105"}},"outputId":"bc5e821a-0469-474a-c588-3803e4d14293"},"execution_count":73,"outputs":[{"output_type":"stream","name":"stdout","text":["[1] \"variable LD1 Vw= 1 Vb= 794.652200566216 separation= 794.652200566216\"\n","[1] \"variable LD2 Vw= 1 Vb= 361.241041493455 separation= 361.241041493455\"\n"]}]},{"cell_type":"markdown","source":["As mentioned above, the loadings for each discriminant function are calculated in such a way that the within-group variance (Vw) for each group (wine cultivar here) is equal to 1, as we see in the output from calcSeparations() above.\n","\n","The output from calcSeparations() tells us that the separation achieved by the first (best) discriminant function is 794.7, and the separation achieved by the second (second best) discriminant function is 361.2.\n","\n","Therefore, the total separation is the sum of these, which is (794.652200566216+361.241041493455=1155.893) 1155.89, rounded to two decimal places. Therefore, the “percentage separation” achieved by the first discriminant function is (794.652200566216*100/1155.893=) 68.75%, and the percentage separation achieved by the second discriminant function is (361.241041493455*100/1155.893=) 31.25%.\n","\n","----\n","**end**\n","\n","\n","\n","The “**proportion of trace**” that is printed when you type “wine.lda” (the variable returned by the lda() function) is the percentage separation achieved by each discriminant function. For example, for the wine data we get the same values as just calculated (68.75% and 31.25%):"],"metadata":{"id":"dkxB48la4Ttb"}},{"cell_type":"code","source":["wine.lda"],"metadata":{"id":"cLAUTAYL5D6n","colab":{"base_uri":"https://localhost:8080/","height":718},"executionInfo":{"status":"ok","timestamp":1717498402258,"user_tz":-120,"elapsed":388,"user":{"displayName":"toni Monleon","userId":"05269440626861479105"}},"outputId":"1baea8ac-8899-41e1-d9d0-46dddd5adaad"},"execution_count":74,"outputs":[{"output_type":"display_data","data":{"text/plain":["Call:\n","lda(wine$V1 ~ wine$V2 + wine$V3 + wine$V4 + wine$V5 + wine$V6 + \n"," wine$V7 + wine$V8 + wine$V9 + wine$V10 + wine$V11 + wine$V12 + \n"," wine$V13 + wine$V14)\n","\n","Prior probabilities of groups:\n"," 1 2 3 \n","0.3314607 0.3988764 0.2696629 \n","\n","Group means:\n"," wine$V2 wine$V3 wine$V4 wine$V5 wine$V6 wine$V7 wine$V8 wine$V9\n","1 13.74475 2.010678 2.455593 17.03729 106.3390 2.840169 2.9823729 0.290000\n","2 12.27873 1.932676 2.244789 20.23803 94.5493 2.258873 2.0808451 0.363662\n","3 13.15375 3.333750 2.437083 21.41667 99.3125 1.678750 0.7814583 0.447500\n"," wine$V10 wine$V11 wine$V12 wine$V13 wine$V14\n","1 1.899322 5.528305 1.0620339 3.157797 1115.7119\n","2 1.630282 3.086620 1.0562817 2.785352 519.5070\n","3 1.153542 7.396250 0.6827083 1.683542 629.8958\n","\n","Coefficients of linear discriminants:\n"," LD1 LD2\n","wine$V2 -0.403399781 0.8717930699\n","wine$V3 0.165254596 0.3053797325\n","wine$V4 -0.369075256 2.3458497486\n","wine$V5 0.154797889 -0.1463807654\n","wine$V6 -0.002163496 -0.0004627565\n","wine$V7 0.618052068 -0.0322128171\n","wine$V8 -1.661191235 -0.4919980543\n","wine$V9 -1.495818440 -1.6309537953\n","wine$V10 0.134092628 -0.3070875776\n","wine$V11 0.355055710 0.2532306865\n","wine$V12 -0.818036073 -1.5156344987\n","wine$V13 -1.157559376 0.0511839665\n","wine$V14 -0.002691206 0.0028529846\n","\n","Proportion of trace:\n"," LD1 LD2 \n","0.6875 0.3125 "]},"metadata":{}}]},{"cell_type":"markdown","source":["Therefore, the first discriminant function does achieve a good separation between the three groups (three cultivars), but the second discriminant function does improve the separation of the groups by quite a large amount, so is it worth using the second discriminant function as well. Therefore, to achieve a good separation of the groups (cultivars), it is necessary to use both of the first two discriminant functions.\n","\n","We found above that the largest separation achieved for any of the individual variables (individual chemical concentrations) was 233.9 for V8, which is quite a lot less than 794.7, the separation achieved by the first discriminant function. Therefore, the effect of using more than one variable to calculate the discriminant function is that we can find a discriminant function that achieves a far greater separation between groups than achieved by any one variable alone.\n","\n","The variable returned by the lda() function also has a named element “svd”, which contains the ratio of between- and within-group standard deviations for the linear discriminant variables, that is, the square root of the “separation” value that we calculated using calcSeparations() above. When we calculate the square of the value stored in “svd”, we should get the same value as found using calcSeparations():"],"metadata":{"id":"zHj5zPOM5IA4"}},{"cell_type":"code","source":["(wine.lda$svd)^2"],"metadata":{"id":"qVnoiqom5LUN","colab":{"base_uri":"https://localhost:8080/","height":34},"executionInfo":{"status":"ok","timestamp":1717498405643,"user_tz":-120,"elapsed":375,"user":{"displayName":"toni Monleon","userId":"05269440626861479105"}},"outputId":"bbd91475-eb71-4cf7-a9da-33f9a6eb33a1"},"execution_count":75,"outputs":[{"output_type":"display_data","data":{"text/html":["\n","
  1. 794.652200566215
  2. 361.241041493455
\n"],"text/markdown":"1. 794.652200566215\n2. 361.241041493455\n\n\n","text/latex":"\\begin{enumerate*}\n\\item 794.652200566215\n\\item 361.241041493455\n\\end{enumerate*}\n","text/plain":["[1] 794.6522 361.2410"]},"metadata":{}}]},{"cell_type":"markdown","source":["A Stacked Histogram of the LDA Values\n","\n","A nice way of displaying the results of a linear discriminant analysis (LDA) is to make a stacked histogram of the values of the discriminant function for the samples from different groups (different wine cultivars in our example).\n","\n","We can do this using the “ldahist()” function in R. For example, to make a stacked histogram of the **first discriminant function’s values** for wine samples of the three different wine cultivars, we type:"],"metadata":{"id":"j42j3BfP5QfP"}},{"cell_type":"code","source":["ldahist(data = wine.lda.values$x[,1], g=wine$V1) #histogram for the three cultivars"],"metadata":{"id":"92LQtPkK5UXG","colab":{"base_uri":"https://localhost:8080/","height":437},"executionInfo":{"status":"ok","timestamp":1717498407404,"user_tz":-120,"elapsed":475,"user":{"displayName":"toni Monleon","userId":"05269440626861479105"}},"outputId":"07e3d541-2898-4d46-f763-fbf9bd08826c"},"execution_count":76,"outputs":[{"output_type":"display_data","data":{"text/plain":["plot without title"],"image/png":"iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAIAAAByhViMAAAACXBIWXMAABJ0AAASdAHeZh94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\n4x1bWVnZu3fveH+ajxw5U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dWl5eXltb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\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/fXX3/V6XQul8s/ISsrq6ys7OzZs3q9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\nX7o8qYsxsMoAAAAAAElFTkSuQmCC"},"metadata":{"image/png":{"width":420,"height":420}}}]},{"cell_type":"markdown","source":["We can see from the histogram that cultivars 1 and 3 are well separated by the first discriminant function, since the values for the first cultivar are between -6 and -1, while the values for cultivar 3 are between 2 and 6, and so there is no overlap in values.\n","\n","However, the separation achieved by the linear discriminant function on the training set may be an overestimate. To get a more accurate idea of how well the first discriminant function separates the groups, we would need to see a stacked histogram of the values for the three cultivars using some unseen “test set”, that is, using a set of data that was not used to calculate the linear discriminant function.\n","\n","We see that the first discriminant function separates cultivars 1 and 3 very well, but does not separate cultivars 1 and 2, or cultivars 2 and 3, so well.\n","\n","We therefore investigate whether the **second discriminant function** separates those cultivars, by making a stacked histogram of the second discriminant function’s values:"],"metadata":{"id":"akQgm2V75XhQ"}},{"cell_type":"code","source":["ldahist(data = wine.lda.values$x[,2], g=wine$V1)"],"metadata":{"id":"UAfkZxVE5aGF","colab":{"base_uri":"https://localhost:8080/","height":437},"executionInfo":{"status":"ok","timestamp":1717498410754,"user_tz":-120,"elapsed":441,"user":{"displayName":"toni Monleon","userId":"05269440626861479105"}},"outputId":"dceff9d1-230c-484d-ee99-2b6f4ff27e42"},"execution_count":77,"outputs":[{"output_type":"display_data","data":{"text/plain":["plot without 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ubxw/flzY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\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orA/XZff/11YWFhcnKy2WzOzs7u7+8P\nbD9p0qSMjAyn07l48eJPPvlk6K7efffd6urq8vJyh8MRGRm5YsWK7du3h+xIAWAog9/vl50B\nAF4/n89nNP57UiIzM7OsrGzt2rV/Yz+dnZ3jx4/3eDyRkZGvNSAAvH5MxQJQkNfrTUhICMy0\n2u32K1euBD4hCwBqYyoWgILCw8Nra2u3bNlSUVERERGxb98+s9ksOxQABB1TsQAAAIpgKhYA\nAEARFDsAAABFUOwAAAAUQbEDAABQBMUOAABAERQ7AAAARVDsAAAAFEGxAwAAUATFDgAAQBEU\nOwAAAEVQ7AAAABRBsQMAAFAExQ4AAEARFDsAAABFUOwAAAAUQbEDAABQBMUOAABAEf8C1ha4\nS8ViNiQAAAAASUVORK5CYII="},"metadata":{"image/png":{"width":420,"height":420}}}]},{"cell_type":"markdown","source":["We see that the second discriminant function separates cultivars 1 and 2 quite well, although there is a little overlap in their values. Furthermore, the second discriminant function also separates cultivars 2 and 3 quite well, although again there is a little overlap in their values so it is not perfect.\n","\n","Thus, we see that two discriminant functions are necessary to separate the cultivars, as was discussed above (see the discussion of percentage separation above)."],"metadata":{"id":"5IpBtoHz5dYH"}},{"cell_type":"markdown","source":["Scatterplots of the Discriminant Functions\n","\n","We can obtain a scatterplot of the best two discriminant functions, with the data points labelled by cultivar, by typing:"],"metadata":{"id":"3ez_Fk355gC1"}},{"cell_type":"code","source":["plot(wine.lda.values$x[,1],wine.lda.values$x[,2]) # make a scatterplot\n","abline(h = 0, v = 0, lty = 2, col = 8)\n","text(wine.lda.values$x[,1],wine.lda.values$x[,2],wine$V1,cex=0.7,pos=4,col=\"red\") # add labels"],"metadata":{"id":"rM6znToc5j5Q","colab":{"base_uri":"https://localhost:8080/","height":437},"executionInfo":{"status":"ok","timestamp":1717498415375,"user_tz":-120,"elapsed":609,"user":{"displayName":"toni Monleon","userId":"05269440626861479105"}},"outputId":"6189f611-1afd-48d4-c070-9987d5163667"},"execution_count":78,"outputs":[{"output_type":"display_data","data":{"text/plain":["plot without 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AgwNjY+evSooD5iYhQRQZaWdTVGgFqHqA6Ew2+//fbff/9NmTIl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\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dV5d6vRNbsbN25ISkryPEzm5eXVo0cP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\nRU5ErkJggg=="},"metadata":{"image/png":{"width":420,"height":420}}}]},{"cell_type":"markdown","source":["From the scatterplot of the first two discriminant functions, we can see that the wines from the three cultivars are well separated in the scatterplot. The first discriminant function (x-axis) separates cultivars 1 and 3 very well, but doesn’t not perfectly separate cultivars 1 and 3, or cultivars 2 and 3.\n","\n","The second discriminant function (y-axis) achieves a fairly good separation of cultivars 1 and 3, and cultivars 2 and 3, although it is not totally perfect.\n","\n","To achieve a very good separation of the three cultivars, it would be best to use both the first and second discriminant functions together, since the first discriminant function can separate cultivars 1 and 3 very well, and the second discriminant function can separate cultivars 1 and 2, and cultivars 2 and 3, reasonably well."],"metadata":{"id":"aFLyk2Sa5pMn"}},{"cell_type":"markdown","source":["Allocation Rules and Misclassification Rate\n","We can calculate the mean values of the discriminant functions for each of the three cultivars using the “printMeanAndSdByGroup()” function (see above):"],"metadata":{"id":"3-9LIGK65wa1"}},{"cell_type":"code","source":["printMeanAndSdByGroup <- function(variables,groupvariable)\n"," {\n"," # find the names of the variables\n"," variablenames <- c(names(groupvariable),names(as.data.frame(variables)))\n"," # within each group, find the mean of each variable\n"," groupvariable <- groupvariable[,1] # ensures groupvariable is not a list\n"," means <- aggregate(as.matrix(variables) ~ groupvariable, FUN = mean)\n"," names(means) <- variablenames\n"," print(paste(\"Means:\"))\n"," print(means)\n"," # within each group, find the standard deviation of each variable:\n"," sds <- aggregate(as.matrix(variables) ~ groupvariable, FUN = sd)\n"," names(sds) <- variablenames\n"," print(paste(\"Standard deviations:\"))\n"," print(sds)\n"," # within each group, find the number of samples:\n"," samplesizes <- aggregate(as.matrix(variables) ~ groupvariable, FUN = length)\n"," names(samplesizes) <- variablenames\n"," print(paste(\"Sample sizes:\"))\n"," print(samplesizes)\n"," }\n","\n","printMeanAndSdByGroup(wine.lda.values$x,wine[1])"],"metadata":{"id":"7eXDCP3f5zGJ","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1717498419358,"user_tz":-120,"elapsed":369,"user":{"displayName":"toni Monleon","userId":"05269440626861479105"}},"outputId":"97955a9e-4eb0-4a1c-8ec3-31c86db8de15"},"execution_count":79,"outputs":[{"output_type":"stream","name":"stdout","text":["[1] \"Means:\"\n"," V1 LD1 LD2\n","1 1 -3.42248851 1.691674\n","2 2 -0.07972623 -2.472656\n","3 3 4.32473717 1.578120\n","[1] \"Standard deviations:\"\n"," V1 LD1 LD2\n","1 1 0.9394626 1.0176394\n","2 2 1.0839318 0.9973165\n","3 3 0.9404188 0.9818673\n","[1] \"Sample sizes:\"\n"," V1 LD1 LD2\n","1 1 59 59\n","2 2 71 71\n","3 3 48 48\n"]}]},{"cell_type":"markdown","source":["We find that the mean value of the first discriminant function is -3.42248851 for cultivar 1, -0.07972623 for cultivar 2, and 4.32473717 for cultivar 3. The mid-way point between the mean values for cultivars 1 and 2 is (-3.42248851-0.07972623)/2=-1.751107, and the mid-way point between the mean values for cultivars 2 and 3 is (-0.07972623+4.32473717)/2 = 2.122505.\n","\n","Therefore, we can use the following allocation rule:\n","\n","if the first discriminant function is <= -1.751107, predict the sample to be from cultivar 1\n","if the first discriminant function is > -1.751107 and <= 2.122505, predict the sample to be from cultivar 2\n","if the first discriminant function is > 2.122505, predict the sample to be from cultivar 3\n","We can examine the accuracy of this allocation rule by using the “calcAllocationRuleAccuracy()” function below:"],"metadata":{"id":"0lhrKbFd52W4"}},{"cell_type":"code","source":["calcAllocationRuleAccuracy <- function(ldavalue, groupvariable, cutoffpoints)\n"," {\n"," # find out how many values the group variable can take\n"," groupvariable2 <- as.factor(groupvariable[[1]])\n"," levels <- levels(groupvariable2)\n"," numlevels <- length(levels)\n"," # calculate the number of true positives and false negatives for each group\n"," numlevels <- length(levels)\n"," for (i in 1:numlevels)\n"," {\n"," leveli <- levels[i]\n"," levelidata <- ldavalue[groupvariable==leveli]\n"," # see how many of the samples from this group are classified in each group\n"," for (j in 1:numlevels)\n"," {\n"," levelj <- levels[j]\n"," if (j == 1)\n"," {\n"," cutoff1 <- cutoffpoints[1]\n"," cutoff2 <- \"NA\"\n"," results <- summary(levelidata <= cutoff1)\n"," }\n"," else if (j == numlevels)\n"," {\n"," cutoff1 <- cutoffpoints[(numlevels-1)]\n"," cutoff2 <- \"NA\"\n"," results <- summary(levelidata > cutoff1)\n"," }\n"," else\n"," {\n"," cutoff1 <- cutoffpoints[(j-1)]\n"," cutoff2 <- cutoffpoints[(j)]\n"," results <- summary(levelidata > cutoff1 & levelidata <= cutoff2)\n"," }\n"," trues <- results[\"TRUE\"]\n"," trues <- trues[[1]]\n"," print(paste(\"Number of samples of group\",leveli,\"classified as group\",levelj,\" : \",\n"," trues,\"(cutoffs:\",cutoff1,\",\",cutoff2,\")\"))\n"," }\n"," }\n"," }"],"metadata":{"id":"UBeB_tIA57W2","executionInfo":{"status":"ok","timestamp":1717498422991,"user_tz":-120,"elapsed":366,"user":{"displayName":"toni Monleon","userId":"05269440626861479105"}}},"execution_count":80,"outputs":[]},{"cell_type":"markdown","source":["For example, to calculate the accuracy for the wine data based on the allocation rule for the first discriminant function, we type:"],"metadata":{"id":"5yg2e8w06Bod"}},{"cell_type":"code","source":["calcAllocationRuleAccuracy(wine.lda.values$x[,1], wine[1], c(-1.751107, 2.122505))"],"metadata":{"id":"9inli6NC6MM8","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1717436954042,"user_tz":-120,"elapsed":360,"user":{"displayName":"toni Monleon","userId":"05269440626861479105"}},"outputId":"560876d7-4afa-4bb5-d604-f530ea9461b4"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["[1] \"Number of samples of group 1 classified as group 1 : 56 (cutoffs: -1.751107 , NA )\"\n","[1] \"Number of samples of group 1 classified as group 2 : 3 (cutoffs: -1.751107 , 2.122505 )\"\n","[1] \"Number of samples of group 1 classified as group 3 : NA (cutoffs: 2.122505 , NA )\"\n","[1] \"Number of samples of group 2 classified as group 1 : 5 (cutoffs: -1.751107 , NA )\"\n","[1] \"Number of samples of group 2 classified as group 2 : 65 (cutoffs: -1.751107 , 2.122505 )\"\n","[1] \"Number of samples of group 2 classified as group 3 : 1 (cutoffs: 2.122505 , NA )\"\n","[1] \"Number of samples of group 3 classified as group 1 : NA (cutoffs: -1.751107 , NA )\"\n","[1] \"Number of samples of group 3 classified as group 2 : NA (cutoffs: -1.751107 , 2.122505 )\"\n","[1] \"Number of samples of group 3 classified as group 3 : 48 (cutoffs: 2.122505 , NA )\"\n"]}]},{"cell_type":"markdown","source":["This can be displayed in a “confusion matrix”:\n","\n","\n","There are 3+5+1=9 wine samples that are misclassified, out of (56+3+5+65+1+48=) 178 wine samples: 3 samples from cultivar 1 are predicted to be from cultivar 2, 5 samples from cultivar 2 are predicted to be from cultivar 1, and 1 sample from cultivar 2 is predicted to be from cultivar 3. Therefore, the misclassification rate is 9/178, or 5.1%. The misclassification rate is quite low, and therefore the accuracy of the allocation rule appears to be relatively high.\n","\n","However, this is probably an underestimate of the misclassification rate, as the allocation rule was based on this data (this is the “training set”). If we calculated the misclassification rate for a separate “test set” consisting of data other than that used to make the allocation rule, we would probably get a higher estimate of the misclassification rate."],"metadata":{"id":"iHYU7-k36F58"}},{"cell_type":"markdown","source":["# Multivariate analysis as a method to detect patterns visually\n","\n","Multivariate analysis deals with datasets containing multiple variables. Visual techniques like heatmaps are instrumental in spotting patterns within this complexity.\n","\n","One powerful tool in R for creating heatmaps is the pheatmap package. It excels at displaying data matrices with color intensity representing the values. This allows for easy identification of clusters, trends, and correlations between variables.\n","\n","For instance, a heatmap might depict gene expression levels across different samples. Rows would represent genes, columns samples, and color intensity the expression level. This visualization can reveal co-expressed genes or groups of samples with similar expression patterns.\n","\n","Overall, heatmaps, generated by tools like pheatmap, provide a valuable asset for visually exploring and interpreting complex data in multivariate analysis."],"metadata":{"id":"vstupYVr_k9_"}},{"cell_type":"code","source":["#example in genomics using pheatmap\n","#install.packages(\"pheatmap\")\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"uE87vjdr_uFg","executionInfo":{"status":"ok","timestamp":1717433478965,"user_tz":-120,"elapsed":4330,"user":{"displayName":"toni Monleon","userId":"05269440626861479105"}},"outputId":"e830c715-2d1c-4f43-fe69-e3260defb7c9"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["Installing package into ‘/usr/local/lib/R/site-library’\n","(as ‘lib’ is unspecified)\n","\n"]}]},{"cell_type":"markdown","source":["The pheatmap function is similar to the base R heatmap function, but provides more control over the resulting graph. You can pass a numeric array containing the values ​​to be plotted.\n"],"metadata":{"id":"3w-jUmrhBhOg"}},{"cell_type":"code","source":["\n","test = matrix(rnorm(200), 20, 10)\n","test[1:10, seq(1, 10, 2)] = test[1:10, seq(1, 10, 2)] + 3\n","test[11:20, seq(2, 10, 2)] = test[11:20, seq(2, 10, 2)] + 2\n","test[15:20, seq(2, 10, 2)] = test[15:20, seq(2, 10, 2)] + 4\n","colnames(test) = paste(\"Test\", 1:10, sep = \"\")\n","rownames(test) = paste(\"Gene\", 1:20, sep = \"\")\n","head(test)\n","str(test)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":329},"id":"KpDyv2uuA0AO","executionInfo":{"status":"ok","timestamp":1717498477563,"user_tz":-120,"elapsed":369,"user":{"displayName":"toni Monleon","userId":"05269440626861479105"}},"outputId":"16a38766-040a-4194-90ad-cb0881e6ee2b"},"execution_count":82,"outputs":[{"output_type":"display_data","data":{"text/html":["\n","\n","\n","\t\n","\n","\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\n","
A matrix: 6 × 10 of type dbl
Test1Test2Test3Test4Test5Test6Test7Test8Test9Test10
Gene14.467464 0.17856823.305706-0.075647472.926653-0.32024494.422311 0.55053633.409704-0.1572565
Gene21.240993-0.18143813.851446-2.279343923.120521-1.12048572.267523 1.05994472.174386 0.6207172
Gene31.115418 1.14268711.858533 0.639592003.799567 0.34079773.128884-0.78958076.341822-0.5037813
Gene42.078916-1.38801412.805428 3.098141903.810446-0.38223151.725684 1.44893853.517572-0.8149256
Gene53.070339-0.17615543.545671 0.137550902.131952 0.42576382.890156-0.75911011.757645-0.5514269
Gene63.648709-0.21725173.633000-1.678171953.873452-1.18415961.672412 1.12326853.976695-0.2519187
\n"],"text/markdown":"\nA matrix: 6 × 10 of type dbl\n\n| | Test1 | Test2 | Test3 | Test4 | Test5 | Test6 | Test7 | Test8 | Test9 | Test10 |\n|---|---|---|---|---|---|---|---|---|---|---|\n| Gene1 | 4.467464 | 0.1785682 | 3.305706 | -0.07564747 | 2.926653 | -0.3202449 | 4.422311 | 0.5505363 | 3.409704 | -0.1572565 |\n| Gene2 | 1.240993 | -0.1814381 | 3.851446 | -2.27934392 | 3.120521 | -1.1204857 | 2.267523 | 1.0599447 | 2.174386 | 0.6207172 |\n| Gene3 | 1.115418 | 1.1426871 | 1.858533 | 0.63959200 | 3.799567 | 0.3407977 | 3.128884 | -0.7895807 | 6.341822 | -0.5037813 |\n| Gene4 | 2.078916 | -1.3880141 | 2.805428 | 3.09814190 | 3.810446 | -0.3822315 | 1.725684 | 1.4489385 | 3.517572 | -0.8149256 |\n| Gene5 | 3.070339 | -0.1761554 | 3.545671 | 0.13755090 | 2.131952 | 0.4257638 | 2.890156 | -0.7591101 | 1.757645 | -0.5514269 |\n| Gene6 | 3.648709 | -0.2172517 | 3.633000 | -1.67817195 | 3.873452 | -1.1841596 | 1.672412 | 1.1232685 | 3.976695 | -0.2519187 |\n\n","text/latex":"A matrix: 6 × 10 of type dbl\n\\begin{tabular}{r|llllllllll}\n & Test1 & Test2 & Test3 & Test4 & Test5 & Test6 & Test7 & Test8 & Test9 & Test10\\\\\n\\hline\n\tGene1 & 4.467464 & 0.1785682 & 3.305706 & -0.07564747 & 2.926653 & -0.3202449 & 4.422311 & 0.5505363 & 3.409704 & -0.1572565\\\\\n\tGene2 & 1.240993 & -0.1814381 & 3.851446 & -2.27934392 & 3.120521 & -1.1204857 & 2.267523 & 1.0599447 & 2.174386 & 0.6207172\\\\\n\tGene3 & 1.115418 & 1.1426871 & 1.858533 & 0.63959200 & 3.799567 & 0.3407977 & 3.128884 & -0.7895807 & 6.341822 & -0.5037813\\\\\n\tGene4 & 2.078916 & -1.3880141 & 2.805428 & 3.09814190 & 3.810446 & -0.3822315 & 1.725684 & 1.4489385 & 3.517572 & -0.8149256\\\\\n\tGene5 & 3.070339 & -0.1761554 & 3.545671 & 0.13755090 & 2.131952 & 0.4257638 & 2.890156 & -0.7591101 & 1.757645 & -0.5514269\\\\\n\tGene6 & 3.648709 & -0.2172517 & 3.633000 & -1.67817195 & 3.873452 & -1.1841596 & 1.672412 & 1.1232685 & 3.976695 & -0.2519187\\\\\n\\end{tabular}\n","text/plain":[" Test1 Test2 Test3 Test4 Test5 Test6 Test7 \n","Gene1 4.467464 0.1785682 3.305706 -0.07564747 2.926653 -0.3202449 4.422311\n","Gene2 1.240993 -0.1814381 3.851446 -2.27934392 3.120521 -1.1204857 2.267523\n","Gene3 1.115418 1.1426871 1.858533 0.63959200 3.799567 0.3407977 3.128884\n","Gene4 2.078916 -1.3880141 2.805428 3.09814190 3.810446 -0.3822315 1.725684\n","Gene5 3.070339 -0.1761554 3.545671 0.13755090 2.131952 0.4257638 2.890156\n","Gene6 3.648709 -0.2172517 3.633000 -1.67817195 3.873452 -1.1841596 1.672412\n"," Test8 Test9 Test10 \n","Gene1 0.5505363 3.409704 -0.1572565\n","Gene2 1.0599447 2.174386 0.6207172\n","Gene3 -0.7895807 6.341822 -0.5037813\n","Gene4 1.4489385 3.517572 -0.8149256\n","Gene5 -0.7591101 1.757645 -0.5514269\n","Gene6 1.1232685 3.976695 -0.2519187"]},"metadata":{}},{"output_type":"stream","name":"stdout","text":[" num [1:20, 1:10] 4.47 1.24 1.12 2.08 3.07 ...\n"," - attr(*, \"dimnames\")=List of 2\n"," ..$ : chr [1:20] \"Gene1\" \"Gene2\" \"Gene3\" \"Gene4\" ...\n"," ..$ : chr [1:10] \"Test1\" \"Test2\" \"Test3\" \"Test4\" ...\n"]}]},{"cell_type":"code","source":["#library(ComplexHeatmap)\n","library(pheatmap)\n","pheatmap(test) # this is ComplexHeatmap::pheatmap"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":437},"id":"AyFF85IR40cL","executionInfo":{"status":"ok","timestamp":1717498502954,"user_tz":-120,"elapsed":598,"user":{"displayName":"toni Monleon","userId":"05269440626861479105"}},"outputId":"ce86d5ac-be6d-4530-cd2c-28df8dc1c9e7"},"execution_count":83,"outputs":[{"output_type":"display_data","data":{"text/plain":["plot without title"],"image/png":"iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAIAAAByhViMAAAACXBIWXMAABJ0AAASdAHeZh94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\nGz/vtIa0WP3v266T1Fvl5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IWxRrT+uF\nltpDWtfhMlVVhBAt9cdkvUHrWlx2ta1FZ+sNba69/q6Dj6QqX55ubL/coXUhLuu52iuE+K8P\n64P0/nf52uXWTlnY9n7YqXUh7lBVceJEx/nGHq0LcVlbW58Q4r33GiVJ61Jcd/Fiz5AhvVpX\ncRMh2PWnqqrU06Va/e9/vtrbIwmh77midSHuUIRq672qXmnRuhCXqYoqhOhqbxOS//2RVq09\nkqqGqV1aF+KOHklc7byiWv0vYVitQghxuaVNlv3vr7S1zyaEuNCial2IO1RVtLX1dHf636/3\nnj5JCHHh3BV/DHZXe6WgIL/8JeMCL3xWbEpKiuND0lxCsOtPluXwMckhw6K0LsRlbac+7Tx/\nxjRhmtaFuKOj5l1DRIwpfrzWhbhM6b16+eN3pPhkKXSI1rW4TPniiMHamTTtPq0Lcccnf66c\nMC548p0mrQtx2bmm3t1vX/7B/TFms//9Bv6/bzZKqnJ/xjCtC3HHCy+d/87t1qRbFa0Lcdln\nZ3Xvfxr08D19suR/kfqDY0E9+qFaV3ET8b85BgAAAHwj/3u9CAAAMIhInl+KdRszdgAAAAGC\nGTsAAIAB8MLFE25jxg4AACBAEOwAAAAChH8vxcqy/Oyzz/72t7/9th2amprq6up8WRIAALi5\nDKalWP8Odv/yL/9y5MgRJzusXr06JibGZ/UAAABoyL+DXUpKSkpKipMdCgoKjEajz+oBAAA3\nncE0Y8d77AAAgAb27NkzZ86cqKgovV4fHh4+bdq0HTt2+OC4zz//fGho6EMPPeSDY/kewQ4A\nAPhabm7u7NmzOzo6CgsLX3vttZKSErPZvHDhwkWLFnnvoC0tLXPnzi0sLBwyxP8+BPIG+fdS\nLAAA8DuVlZWlpaX5+fnFxcWOxiVLlhQWFhYXFy9btmzq1KleOm5HR8fHH388a9YsT47LUiwA\nALhplZWVJSQkFBYW9mtfu3ZtS0uLI9WpqlpWVjZx4kSj0RgTE7N8+fKOjg77pvj4+HXr1pWW\nlo4cOdJkMk2ZMmXfvn3X7XXffff9+c9/joqK8slZaoNgBwAAfKe7u/vgwYPp6ek6Xf+JLlmW\nQ0NDHU/Xr1+fl5e3aNGiY8eOVVRUVFZWZmZm2jcFBwdv27attbX16NGjjY2NkZGRCxYssNls\nznvFxsbKsheSjyy79uVNN9FSbGNj4/79+6+7m6IoPigGAICbU1NTk9VqTUxMdLQoitLe3u54\nqtfrTSZTV1dXWVnZ4sWLV65cKYRISEgoKSmZN2/e4cOHk5OTZVk2m81FRUWSJPOq8IsAACAA\nSURBVAkhsrKy5s+f39DQEBkZ6aSXz89VAzdRsCstLS0vLzebzc53e/rpp31TDwAANyH7nJnB\nYHC01NfXjxkzxvF01qxZ1dXVNTU1XV1dGRkZjvaZM2cKIQ4dOmSPaJMmTbKnOiGExWIRQrS2\ntjY0NDjvFfBuomBns9nuv//+119/3fluFRUVvqkHAICbUHR0tMFgOHHihKMlNjZ279699sf5\n+fn2B21tbUKIzMzMfoun58+ftz+4dtHWTlXV6/byisF08cRNFOwAAIDmDAZDWlpaVVVVeXl5\nSEiIECI0NHT69On2rREREVarVXw1Cbd58+YZM2Zc2z0iIsLJ4O71CiQEOwAA4FN5eXn33ntv\ndnb2li1bHMupQojm5ua6urr4+HghxIQJE4xGY0NDQ1JSkn2r1Wo9derU8OHDnYzsXq+BYsYO\nAADctDIyMoqLi9esWXPkyJGsrKz4+Pi2trYDBw5s3749JCRk69atQgij0bhixYpNmzbFxcWl\np6e3t7eXlJRUV1cfP358xIgR3zay8141NTWtra1CiI6OjosXL7733ntCiFGjRsXFxfnq1L2O\nYAcAAHwtPz8/LS2tvLy8oKCgubk5LCxs7Nixa9asWbp0qeMyx6KiIovFsnHjxuzsbIvFkpKS\nsn//fiep7rq9Vq1a9c4779h3q6ursy/XlpSU5OTkePNcfYpgBwAANJCampqamupkB0mScnJy\nvjF11dbWXvt09uzZqqpet9fbb789gHqdGERLsdygGAAAIEAwYwcAAOA+RXUtTemk6+/jNmbs\nAAAAAgTBDgAAIECwFAsAAOA+xcWLJ7x6qQUzdgAAAAGCGTsAAAD3uXrxhFcxYwcAABAgCHYA\nAAABYhBNHnrJqVOnDh06JIS4cOFCb2/vdfdXVbW3rVm1Wb1fmodZuzuEqlhbzmtdiFsURbna\n2XupQes6XKZarUII0dEsejq1rsV1fVcVxXr53Bda1+EOVVFbL9s+P92jdSEua73cJ4Q4faYr\nJGQQ3a3+Bl3ttqmq8vmpbq0LcYeiiIuXdUFevYeYd1xsk4QQdedlyf9qF+1dcpDZpnUV3qWo\ng+j/suT4CA5/8cQTT1y4cGHnzp03svPkyZMPHz7seJqYmFhXV+e8y9atW/3uewIAwKAVHh7+\nyCOPaF2FF3VbP3Zp/9CgyV6qRAT8jN3HH//39/pnP/vZ6dOnr9tFp9P13XKnMEd6sSwvaTop\nXz5nnjxT6zrc0VHzrhI2QkSP1boQ11l7pRN/uXPaLLNlmNaluOzk3/6z+cLFvpEpWhfiDv3J\nfd9JvDo5sU/rQlx2rkXe/beQe38402gK1boWl73/1gGDcvn+6f5XuRDihVev/P14W1Kc/710\n/+wL+f2j8mMP6mU/nLH7oMbWI/vhn1RXKIMpTfEeOwAAgEGhoqJi8uTJZrM5ISFh6dKlzc3N\nro5AsAMAANDepk2bHnvssZkzZ1ZVVa1evXrnzp1uLGEPoslDAAAAv+ORiycURXnmmWceffTR\nkpISIUR6errVas3Ozj579mxcXNyNj0OwAwAA0JgkSQcOHAgLC3O0jBo1SgjR3NxMsAMAAPAR\nj8zYSZJkT3IOu3fvjoiIGDdunEvjEOwAAAAGl127dj333HNbt24NCQlxqSMXTwAAAAwi27dv\nf/jhh/Pz8x977DFX+zJjBwAA4D5V9WSaevrpp5988skNGzasWrXKje4EOwAAgEGhsLCwuLh4\n586dP/rRj9wbgaVYAACggT179syZMycqKkqv14eHh0+bNm3Hjh3ePuiLL76YlJQUHByckJBQ\nXFzskQ8RVYTOpa9vG6eqquqXv/zlH/7wB7dTnWDGDgAA+F5ubm5paemMGTMKCwujo6MbGxur\nqqoWLlz4zjvvbNu2zUsH3bRpU05OTm5u7n333ffuu++uW7eur69v/fr1XjqcS3p7e1etWpWa\nmmo2m9977z1H++jRo2+99dYbH4dgBwAAfKqysrK0tDQ/P7+4uNjRuGTJEvtC5LJly6ZOnerx\ng9pstl/+8pf/9E//9Oyzzwohpk2bdvHixQ0bNuTm5hqNRo8fzlX19fV1dXV1dXUzZsy4tn3T\npk0rVqy48XFYigUAAD5VVlaWkJBQWFjYr33t2rUtLS2OVKeqallZ2cSJE41GY0xMzPLlyzs6\nOuyb4uPj161bV1paOnLkSJPJNGXKlH379jnvdfr06ba2tlmzZjkOl5mZ2dXV9cEHHwzwdBQ1\nyKWvbxwkKSlJ/SYupTpBsAMAAL7U3d198ODB9PR0na7/u81kWQ4NDXU8Xb9+fV5e3qJFi44d\nO1ZRUVFZWZmZmWnfFBwcvG3bttbW1qNHjzY2NkZGRi5YsMBmsznp1dvba+/oGD86OloIcfLk\nSS+fsU/dXEuxnZ2d9fX1zvfxyPsoAQDAN2pqarJarYmJiY4WRVHa29sdT/V6vclk6urqKisr\nW7x48cqVK4UQCQkJJSUl8+bNO3z4cHJysizLZrO5qKhIkiQhRFZW1vz58xsaGiIjI7+t17hx\n44KCgj788MO5c+faD3T48GEhxJUrVwZ4Rh755AlPuYmCndlsfuutt/p9XsfXPffcc0xjAgDg\nJbIsCyEMBoOjpb6+fsyYMY6ns2bNqq6urqmp6erqysjIcLTPnDlTCHHo0KHk5GQhxKRJk+yp\nTghhsViEEK2trQ0NDU56Pf744//6r/969913z5o1629/+5s9F+r1eq+er4/dRMFu/fr1WVlZ\n191t7969Vh9UAwDATSk6OtpgMJw4ccLREhsbu3fvXvvj/Px8+4O2tjYhRGZmpj0IOpw/f97+\n4NpFWztVVZ33KikpaW1ttc/YxcfH//rXv7bfb8VzJ6e9myjYBQUFXTvx+22uvcYYAAB4lsFg\nSEtLq6qqKi8vt38Qamho6PTp0+1bIyIirFar+GoSbvPmzf2uEo2IiHAyuPNeZrP55Zdf/vWv\nf93R0REfH2+/bMI+/zcQ33Y9hCZYdQQAAD6Vl5d34cKF7Ozsfu9rb25urqursz+eMGGC0Whs\naGhI+sro0aN1Ot3w4cOdjOy816uvvrpv377hw4ePHDlSluUXXnghKSlp/Pjx3jtT3/vvjHny\n5Mkf/OAHfX19GlZzI5qbm+Pj47WuAgAAuCkjI6O4uHjNmjVHjhzJysqKj49va2s7cODA9u3b\nQ0JCtm7dKoQwGo0rVqzYtGlTXFxcenp6e3t7SUlJdXX18ePHR4wY8W0jO+/15ptvvvHGG7/5\nzW9Gjx69a9euf//3f3/jjTcGfjpOPkzC9/472H3xxRcnT558/vnnNazmRvzHf/zHte+4BAAA\nfic/Pz8tLa28vLygoKC5uTksLGzs2LFr1qxZunSp2Wy271NUVGSxWDZu3JidnW2xWFJSUvbv\n3+8k1V23129+85ugoKDly5e3tbWNHz++srLy+9//vtdP1bf+x6qwJElLlizRqpQbdOTIkQsX\nLmhdBQAAGJDU1NTU1FQnO0iSlJOTk5OT8/VNtbW11z6dPXu2Y1XXSS+j0bhly5YtW7YMoOrB\nbhC93Q8AAMDvcPEEAAAAPG8QZUwAAAC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Nvb6xqJjo4+fPgw81ij0TAPzGYzISQ/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is possible add metadata"],"metadata":{"id":"SyPTBkMgBm1-"}},{"cell_type":"code","source":["#The next one is an example for setting annotations (you should be familiar with how to set these data frames and color list if you are a pheatmap user).\n","\n","#ceel type factor:\n","annotation_col = data.frame(\n"," #we add two factors in columns:CellType & Time\n"," CellType = factor(rep(c(\"CT1\", \"CT2\"), 5)),\n"," Time = 1:5\n",")\n","rownames(annotation_col) = paste(\"Test\", 1:10, sep = \"\")\n","annotation_col\n","\n","annotation_row = data.frame(\n"," #one factor in rows: GeneClass\n"," GeneClass = factor(rep(c(\"Path1\", \"Path2\", \"Path3\"), c(10, 4, 6)))\n",")\n","rownames(annotation_row) = paste(\"Gene\", 1:20, sep = \"\")\n","annotation_row\n","\n","ann_colors = list(\n"," Time = c(\"white\", \"firebrick\"),\n"," CellType = c(CT1 = \"#1B9E77\", CT2 = \"#D95F02\"),\n"," GeneClass = c(Path1 = \"#7570B3\", Path2 = \"#E7298A\", Path3 = \"#66A61E\")\n",")\n","ann_colors\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"gClXuT0zAQ6e","executionInfo":{"status":"ok","timestamp":1717498621431,"user_tz":-120,"elapsed":390,"user":{"displayName":"toni Monleon","userId":"05269440626861479105"}},"outputId":"2c38cfd5-5782-4868-eab4-72943e4508d9"},"execution_count":87,"outputs":[{"output_type":"display_data","data":{"text/html":["\n","\n","\n","\t\n","\t\n","\n","\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\n","
A data.frame: 10 × 2
CellTypeTime
<fct><int>
Test1CT11
Test2CT22
Test3CT13
Test4CT24
Test5CT15
Test6CT21
Test7CT12
Test8CT23
Test9CT14
Test10CT25
\n"],"text/markdown":"\nA data.frame: 10 × 2\n\n| | CellType <fct> | Time <int> |\n|---|---|---|\n| Test1 | CT1 | 1 |\n| Test2 | CT2 | 2 |\n| Test3 | CT1 | 3 |\n| Test4 | CT2 | 4 |\n| Test5 | CT1 | 5 |\n| Test6 | CT2 | 1 |\n| Test7 | CT1 | 2 |\n| Test8 | CT2 | 3 |\n| Test9 | CT1 | 4 |\n| Test10 | CT2 | 5 |\n\n","text/latex":"A data.frame: 10 × 2\n\\begin{tabular}{r|ll}\n & CellType & Time\\\\\n & & \\\\\n\\hline\n\tTest1 & CT1 & 1\\\\\n\tTest2 & CT2 & 2\\\\\n\tTest3 & CT1 & 3\\\\\n\tTest4 & CT2 & 4\\\\\n\tTest5 & CT1 & 5\\\\\n\tTest6 & CT2 & 1\\\\\n\tTest7 & CT1 & 2\\\\\n\tTest8 & CT2 & 3\\\\\n\tTest9 & CT1 & 4\\\\\n\tTest10 & CT2 & 5\\\\\n\\end{tabular}\n","text/plain":[" CellType Time\n","Test1 CT1 1 \n","Test2 CT2 2 \n","Test3 CT1 3 \n","Test4 CT2 4 \n","Test5 CT1 5 \n","Test6 CT2 1 \n","Test7 CT1 2 \n","Test8 CT2 3 \n","Test9 CT1 4 \n","Test10 CT2 5 "]},"metadata":{}},{"output_type":"display_data","data":{"text/html":["\n","\n","\n","\t\n","\t\n","\n","\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\n","
A data.frame: 20 × 1
GeneClass
<fct>
Gene1Path1
Gene2Path1
Gene3Path1
Gene4Path1
Gene5Path1
Gene6Path1
Gene7Path1
Gene8Path1
Gene9Path1
Gene10Path1
Gene11Path2
Gene12Path2
Gene13Path2
Gene14Path2
Gene15Path3
Gene16Path3
Gene17Path3
Gene18Path3
Gene19Path3
Gene20Path3
\n"],"text/markdown":"\nA data.frame: 20 × 1\n\n| | GeneClass <fct> |\n|---|---|\n| Gene1 | Path1 |\n| Gene2 | Path1 |\n| Gene3 | Path1 |\n| Gene4 | Path1 |\n| Gene5 | Path1 |\n| Gene6 | Path1 |\n| Gene7 | Path1 |\n| Gene8 | Path1 |\n| Gene9 | Path1 |\n| Gene10 | Path1 |\n| Gene11 | Path2 |\n| Gene12 | Path2 |\n| Gene13 | Path2 |\n| Gene14 | Path2 |\n| Gene15 | Path3 |\n| Gene16 | Path3 |\n| Gene17 | Path3 |\n| Gene18 | Path3 |\n| Gene19 | Path3 |\n| Gene20 | Path3 |\n\n","text/latex":"A data.frame: 20 × 1\n\\begin{tabular}{r|l}\n & GeneClass\\\\\n & \\\\\n\\hline\n\tGene1 & Path1\\\\\n\tGene2 & Path1\\\\\n\tGene3 & Path1\\\\\n\tGene4 & Path1\\\\\n\tGene5 & Path1\\\\\n\tGene6 & Path1\\\\\n\tGene7 & Path1\\\\\n\tGene8 & Path1\\\\\n\tGene9 & Path1\\\\\n\tGene10 & Path1\\\\\n\tGene11 & Path2\\\\\n\tGene12 & Path2\\\\\n\tGene13 & Path2\\\\\n\tGene14 & Path2\\\\\n\tGene15 & Path3\\\\\n\tGene16 & Path3\\\\\n\tGene17 & Path3\\\\\n\tGene18 & Path3\\\\\n\tGene19 & Path3\\\\\n\tGene20 & Path3\\\\\n\\end{tabular}\n","text/plain":[" GeneClass\n","Gene1 Path1 \n","Gene2 Path1 \n","Gene3 Path1 \n","Gene4 Path1 \n","Gene5 Path1 \n","Gene6 Path1 \n","Gene7 Path1 \n","Gene8 Path1 \n","Gene9 Path1 \n","Gene10 Path1 \n","Gene11 Path2 \n","Gene12 Path2 \n","Gene13 Path2 \n","Gene14 Path2 \n","Gene15 Path3 \n","Gene16 Path3 \n","Gene17 Path3 \n","Gene18 Path3 \n","Gene19 Path3 \n","Gene20 Path3 "]},"metadata":{}},{"output_type":"display_data","data":{"text/html":["
\n","\t
$Time
\n","\t\t
\n","
  1. 'white'
  2. 'firebrick'
\n","
\n","\t
$CellType
\n","\t\t
CT1
'#1B9E77'
CT2
'#D95F02'
\n","
\n","\t
$GeneClass
\n","\t\t
Path1
'#7570B3'
Path2
'#E7298A'
Path3
'#66A61E'
\n","
\n","
\n"],"text/markdown":"$Time\n: 1. 'white'\n2. 'firebrick'\n\n\n\n$CellType\n: CT1\n: '#1B9E77'CT2\n: '#D95F02'\n\n\n$GeneClass\n: Path1\n: '#7570B3'Path2\n: '#E7298A'Path3\n: '#66A61E'\n\n\n\n\n","text/latex":"\\begin{description}\n\\item[\\$Time] \\begin{enumerate*}\n\\item 'white'\n\\item 'firebrick'\n\\end{enumerate*}\n\n\\item[\\$CellType] \\begin{description*}\n\\item[CT1] '\\#1B9E77'\n\\item[CT2] '\\#D95F02'\n\\end{description*}\n\n\\item[\\$GeneClass] \\begin{description*}\n\\item[Path1] '\\#7570B3'\n\\item[Path2] '\\#E7298A'\n\\item[Path3] '\\#66A61E'\n\\end{description*}\n\n\\end{description}\n","text/plain":["$Time\n","[1] \"white\" \"firebrick\"\n","\n","$CellType\n"," CT1 CT2 \n","\"#1B9E77\" \"#D95F02\" \n","\n","$GeneClass\n"," Path1 Path2 Path3 \n","\"#7570B3\" \"#E7298A\" \"#66A61E\" \n"]},"metadata":{}}]},{"cell_type":"code","source":["#cluster plot with metadata for cols and rows\n","#perfect to detect patterns\n","pheatmap(test, annotation_col = annotation_col, annotation_row = annotation_row,\n"," annotation_colors = ann_colors)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":437},"id":"M7m0SbaM5BYk","executionInfo":{"status":"ok","timestamp":1717498627415,"user_tz":-120,"elapsed":1040,"user":{"displayName":"toni Monleon","userId":"05269440626861479105"}},"outputId":"07a03385-33e4-45dc-da67-742bc6889edb"},"execution_count":88,"outputs":[{"output_type":"display_data","data":{"text/plain":["plot without 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AAOA19NhZ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\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MVitlO0Sx07dgwJCdmxY8ekSZOYLV9/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\nWMiDPMiDPMgDwCY4x84EOzs7pVLZp/HMmTO2trbmGK6goOChh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Proposed exercises\n","\n","\n","* Students are asked to perform a complete multivariate analysis of the data of Spartina alterniflora, which will be found on the Biost3 server (multivariate section), perform: 1) data visualization, dimension reduction, regression model, classification model and others.\n","\n"],"metadata":{"id":"8T7XDXLizdRr"}},{"cell_type":"markdown","source":["Try to do the examples in:\n","\n","\n","\n","* https://cran.r-project.org/doc/contrib/Faraway-PRA.pdf\n","* https://www.r-bloggers.com/2021/10/multiple-linear-regression-made-simple/\n","* Examples with PCA and LDA in: https://jcoliver.github.io/learn-r/003-intro-multivariate.html\n","* Example of PCA with IRIS: https://rpubs.com/amos593/419546\n","* Examples of this book in: https://www.webpages.uidaho.edu/~stevel/519/An%20Intro%20to%20Applied%20Multi%20Stat%20with%20R%20by%20Everitt%20et%20al.pdf\n","* Lasso regression: https://www.r-bloggers.com/2020/06/understanding-lasso-and-ridge-regression-2/\n","\n","\n"],"metadata":{"id":"0oWfRmMP_hor"}},{"cell_type":"markdown","source":["#Other topics related to multivariate data analysis in Bioinformatics\n","\n","see in https://monashbioinformaticsplatform.github.io/RNAseq-DE-analysis-with-R/RNAseq_DE_analysis_with_R.html\n","\n","RNAseq data analysis in R - Notebook\n","\n","\n","#Other multivariate methods: MDS, Cluster analysis, distances, maps\n","\n","See exercises in: https://richardlent.github.io/post/multivariate-analysis-with-r/\n","\n","\n","\n","Multivariate Statistical Analysis using the R package chemometrics, see in https://cran.r-project.org/web/packages/chemometrics/vignettes/chemometrics-vignette.pdf\n","\n","Multivariate Analysis in R: https://web.stanford.edu/class/bios221/labs/multivariate/lab_5_multivariate.html\n","\n","\n","\n"],"metadata":{"id":"Zu5iQp8kobnp"}}]}