From c552aa24f410052506d725c65741e8fa8c709bae Mon Sep 17 00:00:00 2001 From: Samuel Ortion Date: Fri, 10 Nov 2023 13:39:13 +0100 Subject: [PATCH] feat: Add some stuff on generalized linear models --- .gitattributes | 0 Makefile | 0 content/chapters/include.tex | 2 +- content/chapters/part1/0.tex | 0 content/chapters/part1/1.tex | 106 ++++++---- content/chapters/part1/2.tex | 182 ++++++++++++++++++ content/chapters/part1/3.tex | 25 +++ content/chapters/part1/4.tex | 0 content/chapters/part2/0.tex | 0 content/chapters/part2/1.tex | 4 +- content/conclusion.tex | 0 content/introduction.tex | 0 definitions.tex | 0 figures/plots/linear_regression.R | 0 figures/plots/linear_regression_linear.pdf | Bin 130 -> 19542 bytes .../plots/linear_regression_non_linear.pdf | Bin 130 -> 20288 bytes figures/plots/logistic_curve.pdf | Bin 0 -> 37982 bytes figures/plots/logistic_curve.tex | 23 +++ figures/schemes/.gitattributes | 0 figures/schemes/base_plan.pdf | Bin 129 -> 3108 bytes 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b/content/chapters/part1/1.tex @@ -60,12 +60,6 @@ In order to estimate the parameters, we can use penalties (additional terms). Lasso regression, Elastic Net, etc. -\subsection{Statistical Analysis Workflow} - -\begin{enumerate}[label={\bfseries\color{primary}Step \arabic*.}] - \item Graphical representation; - \item ... -\end{enumerate} \[ Y = X \beta + \varepsilon, \] @@ -145,21 +139,18 @@ $\Y - \X \hat{\beta} \perp \X \hat{\beta} - \Y \One$ if $\One \in V$, so \begin{figure} \centering - \includestandalone{figures/schemes/orthogonal_projection} + \includegraphics{figures/schemes/orthogonal_projection.pdf} \caption{Orthogonal projection of $\Y$ on plan generated by the base described by $\X$. $\color{blue}a$ corresponds to $\norm{\X\hat{\beta} - \bar{\Y}}^2$ and $\color{blue}b$ corresponds to $\hat{\varepsilon} = \norm{\Y - \hat{\beta}\X}^2$} and $\color{blue}c$ corresponds to $\norm{Y - \bar{Y}}^2$. \label{fig:scheme-orthogonal-projection} \end{figure} \begin{figure} \centering - \includestandalone{figures/schemes/ordinary_least_squares} + \includegraphics{figures/schemes/ordinary_least_squares.pdf} \caption{Ordinary least squares and regression line with simulated data.} \label{fig:ordinary-least-squares} \end{figure} - - - \begin{definition}[Model dimension] Let $\M$ be a model. The dimension of $\M$ is the dimension of the subspace generated by $\X$, that is the number of parameters in the $\beta$ vector. @@ -169,22 +160,21 @@ $\Y - \X \hat{\beta} \perp \X \hat{\beta} - \Y \One$ if $\One \in V$, so \section{Gaussian vectors} - \begin{definition}[Normal distribution] - + $X \sim \Norm(\mu, \sigma^{2})$, with density function $f$ + \[ + f(x) = \frac{1}{\sigma \sqrt{2\pi}}e^{-\frac{1}{2}(\frac{x-\mu}{\sigma})^{2}} + \] \end{definition} \begin{definition}[Gaussian vector] - A random vector $\Y \in \RR[n]$ is a gaussian vector if every linear combination of its component is ... + A random vector $\Y \in \RR[n]$ is a gaussian vector if every linear combination of its component is a gaussian random variable. \end{definition} \begin{property} $m = \EE(Y) = (m_1, \ldots, m_n)^T$, where $m_i = \EE(Y_i)$ - - ... - \[ \Y \sim \Norm_n(m, \Sigma) \] @@ -193,8 +183,6 @@ $\Y - \X \hat{\beta} \perp \X \hat{\beta} - \Y \One$ if $\One \in V$, so \Sigma = \E\left[(\Y -m)(\Y - m)^T\right]. \] - - \end{property} \begin{remark} @@ -261,24 +249,25 @@ Covariance is really sensitive to scale of variables. For instance, if we measur \[ \RR[n] = V_1 \overset{\perp}{\oplus} V_2. \] - \item If $Z_1, Z_2$ are orthogonal of $\mathbf{Z}$ on $V_1$ and $V_2$ i.e. $Z_1 = \Pi_{V_1}(\mathbf{Z}) = \Pi_1 \Y$ and $Z_2 = \Pi_{V_2} (\mathbf{Z}) = \Pi_2 \Y$... - (\textcolor{red}{look to the slides}) + \item If $Z_1, Z_2$ are orthogonal of $\mathbf{Z}$ on $V_1$ and $V_2$ i.e. $Z_1 = \Pi_{V_1}(\mathbf{Z}) = \Pi_1 \Y$ and $Z_2 = \Pi_{V_2} (\mathbf{Z}) = \Pi_2 \Y$ ($\Pi_{1}$ and $\Pi_{2}$ being projection matrices) + then: + \item $z_{1}$, $Z_{2}$ are independent gaussian vectors, $Z_{1} \sim \Norm_{n_{1}} (0_{n}, \Pi_{1})$ and $Z_{2} \sim \Norm(0_{n_{2}}, \Pi_{2})$. + + In particular $\norm{Z_{1}} \sim \chi^{2}(n_{1})$ and $\norm{Z_{2}} \sim \chi^{2}(n_{2})$. \end{itemize} $Z_2 = \Pi_{V_1}(\Z)$ is the projection of $\Z$ on subspace $V_1$. \dots - - \end{theorem} \begin{property}[Estimators properties in the linear model] According to \autoref{thm:cochran}, \[ \hat{m} \text{ is independent from $\hat{\sigma}^2$} - \]\dots + \] \[ - \frac{\norm{\Y - \Pi_V(\Y)}^2}{...} \sim + \norm{\Y - \Pi_V(\Y)}^2 = \norm{\varepsilon - \Pi_{V}(\varepsilon)}^{2} = \norm{\Pi_{V}^{\perp} (\varepsilon)}^{2} \] $\hat{m} = \X \hat{\beta}$ @@ -303,40 +292,34 @@ Covariance is really sensitive to scale of variables. For instance, if we measur \begin{align*} \hat{m} &= \X \hat{\beta} = \X(\X^T\X)^{-1} \X^T \Y \\ - \text{so} \\ + \intertext{so} \\ &= \Pi_V \Y \end{align*} According to Cochran theorem, we can deduce that the estimator of the predicted value $\hat{m}$ is independent $\hat{\sigma}^2$ -All the sum of squares follows a $\chi^2$ distribution: -\[ - ... -\] +All the sum of squares follows a $\chi^2$ distribution. -\begin{property} -\end{property} +\subsection{Estimators properties} -\subsection{Estimators consistency} - -If $q < n$, \begin{itemize} - \item $\hat{\sigma}^2 \overunderset{\PP}{n\to\infty} \sigma^{*2}$. - \item If $(\X^T\X)^{-1}$... - \item ... + \item $\hat{m}$ is unbiased and estimator of $m$; + \item $\EE(\hat{\sigma}^{2}) = \sigma^{2}(n-q)/n$ $\hat{\sigma}^{2}$ is a biased estimator of $\sigma^{2}$. + \[ + S^{2} = \frac{1}{n-q} \norm{\Y - \Pi_{V}}^{2} + \] + is an unbiased estimator of $\sigma²$. \end{itemize} We can derive statistical test from these properties. - \section{Statistical tests} \subsection{Student $t$-test} - \[ - \frac{\hat{\theta}-\theta}{\sqrt{\frac{\widehat{\VVar}(\hat{\theta})}{n}}} \underset{H_0}{\sim} t + \frac{\hat{\theta}-\theta}{\sqrt{\frac{\widehat{\VVar}(\hat{\theta})}{n}}} \underset{H_0}{\sim} t_{n-q} \] where @@ -627,3 +610,44 @@ Different methods: Usually $AIC$ have smaller penalty than $BIC$, thus $AIC$ criterion tends to select models with more variables than $BIC$ criterion. +\subsection{Stepwise} + +\begin{description} + \item[forward] Add new predictor iteratively, beginning with the most contributing predictors. + \item[backward] Remove predictors iteratively. + \item[stepwise] Combination of forward and backward selection. We start by no predictors. We add predictor. Before adding the predictor, we check whether all previously predictors remain meaningful. +\end{description} + +The problem with this iterative regression, is that at each step we make a test. We have to reduce the confidence level for multiple test. + +In practice, the multiple testing problem is not taken into account in these approaches. + +We can use information criteria or model comparison in these methods. + +\section{Predictions} + +Let $X_i$ the $i$-th row of the matrix $\X$. The observed value $Y_i$ can be estimated by: +\[ + \hat{Y}_i = (\X \hat{\beta})_i = X_i \hat{\beta} +\] + +\begin{align*} + \EE (\hat{Y}_i) &= (\X \beta)_i = X_i \beta \\ + \sigma^{-1} (\X \hat{\beta} - \X \beta) \sim \Norm (0_{p+1}, (\X^T \X)^{-1}), \qquad \text{and} \\ + \Var(\hat{Y}_i) = ... \\ + S^2 = \norm{...} +\end{align*} + + +\paragraph{Prediction Confidence Interval} +We can build confidence interval for predicted values $(\X \hat{\beta})_i$ + +\dots + +\paragraph{Prediction error of $Y$} + + +\paragraph{Prediction interval for a new observation $Y_{n+1}$} + + + diff --git a/content/chapters/part1/2.tex b/content/chapters/part1/2.tex old mode 100644 new mode 100755 index 532bfb7..83fa3fd --- a/content/chapters/part1/2.tex +++ b/content/chapters/part1/2.tex @@ -1,4 +1,186 @@ \chapter{Generalized Linear Model} +\begin{example} + + \begin{description} + \item[Ex. 1 - Credit Carb Default] + Let $Y_i$ be a boolean random variable following a Bernoulli distribution. + \item[Ex. 2 - Horseshoe Crabs] + Let $Y_i$, be the number of satellites males. + + $Y_i$ can be described as following a Poisson distribution. + \end{description} +\end{example} + +\begin{remark} + A Poisson distribution can be viewed as an approximation of binomial distribution when $n$ is high and $p$ low. +\end{remark} + + +We will consider the following relation: +\[ + \EE(Y_i) = g^{-1} X_i \beta, +\] +equivalently: +\[ + g(\EE(Y_i)) = X_i \beta. +\] + +\begin{itemize} + \item $\beta$ is estimated by the maximum likelihood; + \item $g$ is called the link function. +\end{itemize} + +\begin{remark} + In standard linear model, the OLS estimator is the estimator of maximum of likelihood. +\end{remark} + \section{Logistic Regression} +\begin{align*} + & \log(\frac{\Pi}{1 - \Pi}) & = \X \beta \\ + \Leftrightarrow & e^{\ln \frac{\Pi}{1 - \Pi}} = e^{\X \beta} \\ + \Leftrightarrow & \frac{\Pi}{1 - \Pi} = e^{\X \beta} \\ + \Leftrightarrow & \Pi = (1 - \Pi) e^{\X\beta} \\ + \Leftrightarrow & \Pi = e^{\X \beta} - \Pi e^{\X\beta} \\ + \Leftrightarrow & \Pi + \Pi e^{\X\beta} = e^{\X \beta} \\ + \Leftrightarrow & \Pi (1 - e^{\X\beta}) = e^{\X \beta} \\ + \Leftrightarrow & \Pi = \frac{e^{\X\beta}}{1 + e^{\X \beta}} +\end{align*} + + +\section{Maximum Likelihood estimator} + +log-likelihood: the probability to observe what we observe. + +Estimate $\beta$ by $\hat{\beta}$ such that $\forall \beta \in \RR[p+1]$: +\[ + L_n (\hat{\beta}) \geq L_n (\beta) +\] + +These estimators are consistent, but not necessarily unbiased. + + +\section{Test for each single coordinate} + + + +\begin{example}[Payment Default] + Let $Y_i$ be the default value for individual $i$. + + \[ + \log (\frac{\Pi (X)}{1 - \Pi (X)}) = \beta_0 + \beta_1 \text{student} + \beta_2 \text{balance} + \beta_3 \text{income} + \] + + In this example, only $\beta_0$ and $\beta_2$ are significantly different from 0. +\end{example} + +\begin{remark} + We do not add $\varepsilon_i$, because $\log(\frac{\Pi (X)}{1 - \Pi (X)})$ corresponds to the expectation. +\end{remark} + +\subsection{Comparison of nested models} + +To test $H_0:\: \beta_0 = \ldots = \beta_p = 0$, we use the likelihood ratio test: +\[ + T_n = -2 \log (\mathcal{L}^{\texttt{null}}) + 2 \log (\mathcal{L}(\hat{\beta})) \underset{H_0}{\overunderset{\mathcal{L}}{n \to \infty}{\longrightarrow}} \chi^2(p). +\] + +\begin{remark}[Family of Tests] + \begin{itemize} + \item Comparison of estimated values and values under the null hypothesis; + \item Likelihood ratio test; + \item Based on the slope on the derivative. + \end{itemize} +\end{remark} + +\section{Relative risk} + +$RR_i$ is the probably to have the disease, conditional to the predictor $X_{i1}$ over the probability of having the disease, conditional to the predictor $X_{i2}$. + +\[ + RR(j) = \frac{\Prob(Y_{i_1} = 1 \: | \: X_{i_1})}{\Prob(Y_{i_2} = 1) \: | \: X_{i_2}} = \frac{\EE(Y_{i_1})}{\EE(Y_{i_2})}. +\] + +$\pi(X_i)$ is the probability of having the disease, according to $X_i$. + +The relative risk can be written as\dots + +\section{Odds} + +Quantity providing a measure of the likelihood of a particular outcome: +\[ + odd = \frac{\pi(X_i)}{1 - \pi(X_i)} +\] + +\[ + odds = \exp(X_i \beta) +\] +odds is the ratio of people having the disease, if Y represent the disease, over the people not having the disease. + +\section{Odds Ratio} + +\begin{align*} + OR(j) =\frac{odds(X_{i_1})}{odds(X_{i_2})} & = \frac{\frac{\pi{X_{i_1}}}{1 - \pi(X_{i_1})}}{\frac{\pi{X_{i_2}}}{1 - \pi(X_{i_2})}} +\end{align*} + +The OR can be written as: +\[ + OR(j) = \exp(\beta_j) +\] + +\begin{exercise} + Show that $OR(j) = \exp(\beta_j)$. +\end{exercise} + +\begin{align*} + OR(j) & = \frac{odds(X_{i_1})}{odds(X_{i_2})} \\ + & = \frac{\exp(X_{i_1} \beta)}{\exp(X_{i_2} \beta)} \\ +\end{align*} + + \[ + \log \left( + \frac{\Prob(Y=1 \: |\: X_{i_1})}{1 - \Prob(Y=1 \: |\: X_{i_1})}\right) + = \beta_0 + \beta_1 X_1^{(1)} + \beta_2 X_2^{(1)} + \ldots + \beta_p X_p^{(1)} +\] + Similarly +\[ + \log \left( + \frac{\Prob(Y=1 \: |\: X_{i_2})}{1 - \Prob(Y=1 \: |\: X_{i_2})}\right) + = \beta_0 + \beta_1 X_1^{(2)} + \beta_2 X_2^{(2)} + \ldots + \beta_p X_p^{(2)} +\] + We substract both equations: + + \begin{align*} + &\log \left( + \frac{\Prob(Y=1 \: |\: X_{i_1})}{1 - \Prob(Y=1 \: |\: X_{i_1})} \right) - \log \left(\frac{\Prob(Y=1 \: |\: X_{i_2})}{1 - \Prob(Y=1 \: |\: X_{i_2})}\right) \\ + & = \beta_0 + \beta_1 X_1^{(1)} + \beta_2 X_2^{(1)} + \ldots + \beta_p X_p^{(1)} - \beta_0 + \beta_1 X_1^{(2)} + \beta_2 X_2^{(2)} + \ldots + \beta_p X_p^{(2)} \\ + & = \log OR(j) \\ + & = \cancel{(\beta_0 - \beta_0)} + \beta_1 \cancel{(X_1^{(1)} - X_1^{(2)})} + \beta_2 \cancel{(X_2^{(1)} - X_2^{(2)})} + \ldots + \beta_j \cancelto{1}{(X_j^{(1)} - X_j^{(2)})} + \ldots + \beta_p \cancel{(X_p^{(1)} - X_p^{(2)})} \\ + &\Leftrightarrow \log (OR_j) = \beta_j \\ + &\Leftrightarrow OR(j) = \exp(\beta_j) + \end{align*} + +OR is not equal to RR, except in the particular case of probability (?) + +If OR is significantly different from 1, the $\exp(\beta_j)$ is significantly different from 1, thus $\beta_j$ is significantly different from 0. + +If we have more than two classes, we do not know what means $X_{i_1} - X_{i_2} = 0$. We will have to take a reference class, and compare successively each class with the reference class. + +$\hat{\pi}(X_{+}) = \hat{\Prob(X=1 \: | X_{i1})}$ for a new individual. + + +\section{Poisson model} + +Let $Y_{i} \sim \mathcal{P}(\lambda_{i})$, corresponding to a counting. + +\begin{align*} + \EE(Y_{i}) & = g^{-1}(X_{i} \beta) \\ + \Leftrightarrow g(\EE(Y_{i})) = X_{i} \beta +\end{align*} + +where $g(x) = \ln(x)$, and $g^{-1}(x) = e^{x}$. + +\[ + \lambda_{i} = \EE(Y_{i}) = \Var(Y_{i}) +\] diff --git a/content/chapters/part1/3.tex b/content/chapters/part1/3.tex new file mode 100755 index 0000000..cc758aa --- /dev/null +++ b/content/chapters/part1/3.tex @@ -0,0 +1,25 @@ +\chapter{Tests Reminders} + +\section{$\chi^2$ test of independence} + + +\section{$\chi^2$ test of goodness of fit} + +Check if the observations is in adequation with a particular distribution. + +\begin{example}[Mendel experiments] + Let $AB$, $Ab$, $aB$, $ab$ be the four possible genotypes of peas: colors and grain shape. + \begin{tabular} + \toprule + AB & Ab & aB & ab \\ + \midrule + 315 & 108 & 101 & 32 \\ + \bottomrule + \end{tabular} +\end{example} + +The test statistics is: +\[ + D_{k,n} = \sum_{i=1}^{k} \frac{(N_i - np_i)^2}{np_i} \underoverset{H_0}{\mathcal{L}} \chi^2_{(n-1)(q-1)??} +\] + diff --git a/content/chapters/part1/4.tex b/content/chapters/part1/4.tex new file mode 100644 index 0000000..e69de29 diff --git a/content/chapters/part2/0.tex b/content/chapters/part2/0.tex old mode 100644 new mode 100755 diff --git a/content/chapters/part2/1.tex b/content/chapters/part2/1.tex old mode 100644 new mode 100755 index 0e5f075..5e168cd --- a/content/chapters/part2/1.tex +++ b/content/chapters/part2/1.tex @@ -68,7 +68,7 @@ Let $u = \begin{pmatrix} \begin{figure} \centering - \includestandalone{figures/schemes/vector_orthogonality} + \includegraphics{figures/schemes/vector_orthogonality.pdf} \caption{Scalar product of two orthogonal vectors.} \label{fig:scheme-orthogonal-scalar-product} \end{figure} @@ -215,6 +215,6 @@ The number of columns has to be the same as the dimension of the vector to which \begin{figure} \centering - \includestandalone{figures/schemes/coordinates_systems} + \includegraphics{figures/schemes/coordinates_systems.pdf} \caption{Coordinate systems} \end{figure} diff --git a/content/conclusion.tex b/content/conclusion.tex old mode 100644 new mode 100755 diff --git a/content/introduction.tex b/content/introduction.tex old mode 100644 new mode 100755 diff --git a/definitions.tex b/definitions.tex old mode 100644 new mode 100755 diff --git a/figures/plots/linear_regression.R b/figures/plots/linear_regression.R old mode 100644 new mode 100755 diff --git a/figures/plots/linear_regression_linear.pdf b/figures/plots/linear_regression_linear.pdf old mode 100644 new mode 100755 index 0be2ed94c9786b0fe7a86f0b34d8d540810c1af9..9555fc58375f6f3b86e2ed5b789a11c18eb7b82b GIT binary patch literal 19542 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+\documentclass[margin=0.5cm]{standalone} +\usepackage{tikz} +\usepackage{pgfplots} +\pgfplotsset{compat=1.18} + +\begin{document} +\begin{tikzpicture} + \begin{axis}[ + title={Logit function}, + xlabel={$x$}, + ylabel={$y$}, + domain=-5:5, + samples=200, + legend style={at={(0.95,0.05)},anchor=south east} +] +\newcommand{\Lvar}{1} +\newcommand{\kvar}{1} +\newcommand{\xvar}{0} +\addplot [blue] {\Lvar / (1 + exp(-\kvar*(x-\xvar)))}; +\addlegendentry{$L = \Lvar, k=\kvar, x_0=\xvar$}; +\end{axis} +\end{tikzpicture} +\end{document} \ No newline at end of file diff --git a/figures/schemes/.gitattributes b/figures/schemes/.gitattributes old mode 100644 new mode 100755 diff --git a/figures/schemes/base_plan.pdf b/figures/schemes/base_plan.pdf old mode 100644 new mode 100755 index b5c74c066e2f12ff9e07394fe85f4b54d5181976..04d165f375ed39809041488e92ab1121bc1f95fa GIT binary patch literal 3108 zcma)83pAA57pGK=M_y5gPm^meJxU@8caYM<>rNp_ 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diff --git a/references.bib b/references.bib old mode 100644 new mode 100755 diff --git a/scripts/matrix_product.lua b/scripts/matrix_product.lua old mode 100644 new mode 100755

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