#' ---	
#' title: "Data Science and Predictive Analytics (UMich HS650)"	
#' subtitle: "<h2><u>Regularized Linear Modeling and Controlled Variable Selection</u></h2>"	
#' author: "<h3>SOCR/MIDAS (Ivo Dinov)</h3>"	
#' date: "`r format(Sys.time(), '%B %Y')`"	
#' tags: [DSPA, SOCR, MIDAS, Big Data, Predictive Analytics] 	
#' output:	
#'   html_document:	
#'     theme: spacelab	
#'     highlight: tango	
#'     includes:	
#'       before_body: SOCR_header.html	
#'       after_body: SOCR_footer_tracker.html	
#'     toc: true	
#'     number_sections: true	
#'     toc_depth: 2	
#'     toc_float:	
#'       collapsed: false	
#'       smooth_scroll: true	
#' ---	
#' 	
#' Many biomedical and biosocial studies involve large amounts of complex data, including cases where the number of features ($k$) is large and may exceed the number of cases ($n$). In such situations, parameter estimates are difficult to compute or may be unreliable as the [system is underdetermined](https://en.wikipedia.org/wiki/Underdetermined_system). Regularization provides one approach to improve model reliability, prediction accuracy, and result interpretability. It is based on augmenting the fidelity term of the objective function used in the model fitting process with a regularization term that provides restrictions on the parameter space.	
#' 	
#' Classical techniques for choosing *important* covariates to include in a model of complex multivariate data relied on various types of stepwise variable selection processes, see [Chapter 16](http://www.socr.umich.edu/people/dinov/2017/Spring/DSPA_HS650/notes/16_FeatureSelection.html). These tend to improve prediction accuracy in certain situations, e.g., when a small number of features are strongly predictive, or associated, with the clinical outcome or biosocial trait. However, the prediction error may be large when the model relies purely on a fidelity term. Including a regularization term in the optimization of the cost function improves the prediction accuracy. For example, below we show that by shrinking large regression coefficients, ridge regularization reduces overfitting and improves prediction error. Similarly, the least absolute shrinkage and selection operator (LASSO) employs regularization to perform simultaneous parameter estimation and variable selection. LASSO enhances the prediction accuracy and provides a natural interpretation of the resulting model. *Regularization* refers to forcing certain characteristics of model-based scientific inference, e.g., discouraging complex models or extreme explanations, even if they fit the data, enforcing model generalizability to prospective data, or restricting model overfitting of accidental samples.	
#' 	
#' In this chapter, we extend the mathematical foundation we presented in [Chapter 4](http://www.socr.umich.edu/people/dinov/2017/Spring/DSPA_HS650/notes/04_LinearAlgebraMatrixComputing.html) and (1) discuss computational protocols for handling complex high-dimensional data, (2) illustrate model estimation by controlling the false-positive rate of selection of salient features, and (3) derive effective forecasting models.	
#' 	
#' # Questions	
#' 	
#' * How to deal with extremely high-dimensional data (hundreds or thousands of features)?	
#' * Why mix fidelity (model fit) and regularization (model interpretability) terms in objective function optimization?	
#' * How to reduce the false-positive rate, increase scientific validation, and improve result reproducibility (e.g., Knockoff filtering)?	
#' 	
#' # Matrix notation  	
#' We should review the basics of matrix notation, linear algebra, and matrix computing we covered in [Chapter 4](http://www.socr.umich.edu/people/dinov/2017/Spring/DSPA_HS650/notes/04_LinearAlgebraMatrixComputing.html). At the core of matrix manipulations are scalars, vectors and matrices.	
#' 	
#' - ${y}_i$: output or response variable, $i = 1, ..., n$ (cases/subjects).	
#' - $x_{ij}$: input, predictor, or feature variable,  $1\leq j \leq k,\ 1\leq i \leq n.$	
#' 	
#' $${y} = \begin{pmatrix} y_1 \\ y_2 \\ \vdots \\ y_n \end{pmatrix} \quad ,$$	
#' and 	
#' $$\quad {X} = 	
#'   \begin{pmatrix} 	
#'     x_{1,1} & x_{1,2} & \cdots & x_{1,k} \\ 	
#'     x_{2,1} & x_{2,2} & \cdots & x_{2,k} \\ 	
#'     \vdots & \vdots & \cdots & \vdots \\ 	
#'     x_{n,1} & x_{n,2} & \cdots & x_{n,k} 	
#'   \end{pmatrix} .$$	
#'   	
#' # Regularized Linear Modeling	
#' 	
#' If we assume that the covariates are orthonormal, i.e., we have a special kind of a *design matrix* $X^T X = I$, then:	
#' 	
#' * The ordinary least squares (`OLS`) estimates minimize $$\min_{ \beta \in \mathbb{R}^k } \left\{ \frac{1}{N} \left\| y - X \beta \right\|_2^2\right\}, $$ 	
#' 	
#'  and are defined by 	
#' $$\hat{\beta}^{OLS} = (X^T X)^{-1} X^T y = X^T y, $$	
#' 	
#' * `LASSO` estimates minimize $$\min_{ \beta \in \mathbb{R}^k } \left\{ \frac{1}{N} \left\| y - X \beta \right\|_2^2 + \lambda \| \beta \|_1 \right\}, $$ 	
#' 	
#' and are defined as a soft-threshold function of the OLS estimates: 	
#' $$\hat{\beta}_j = S_{N \lambda}( \hat{\beta}^\text{OLS}_j ) = \hat{\beta}^\text{OLS}_j \max \left( 0, 1 - \frac{ N \lambda }{ |\hat{\beta}^{OLS}_j| } \right), $$	
#' 	
#' where $S_{N \lambda}$ is a soft thresholding operator translating values towards zero, instead of setting smaller values to zero and leaving larger ones untouched as the hard thresholding operator would.	
#' 	
#' * `Ridge` regression, where the objective is to minimize:	
#' $$\min_{ \beta \in \mathbb{R}^k } \left\{ \frac{1}{N} \| y - X \beta \|_2^2 + \lambda \| \beta \|_2^2 \right\}, $$	
#' 	
#' which yields estimates $\hat{\beta}_j = ( 1 + N \lambda )^{-1} \hat{\beta}^{OLS}_j$. Thus, ridge regression shrinks all coefficients by a uniform factor, $(1 + N \lambda)^{-1}$, and does not set any coefficients to zero.	
#' 	
#' * `Best subset` selection regression, orthogonal matching pursuit (OMP), minimizing:	
#' $$\min_{ \beta \in \mathbb{R}^k } \left\{ \frac{1}{N} \left\| y - X \beta \right\|_2^2 + \lambda \| \beta \|_0 \right\}, $$	
#' 	
#' where $\|.\|_0$ is the "$\ell^0$ norm", defined as  $| z | = m$ if exactly $m$ components of $z$ are nonzero. In this case, the estimates are:	
#' $$\hat{\beta}_j = H_{ \sqrt{ N \lambda } } \left( \hat{\beta}^{OLS}_j \right) = \hat{\beta}^{OLS}_j I \left( \left| \hat{\beta}^{OLS}_j \right| \geq \sqrt{ N \lambda } \right), $$	
#' 	
#' where $H_\alpha$ is a `hard-thresholding` function and $I$ is an indicator function (it is 1 if its argument is true, and 0 otherwise).	
#' 	
#' The LASSO estimates share features of the estimates from both `ridge` and `best` subset selection regression since they both shrink the magnitude of all the coefficients, like ridge regression, but also set some of them to zero, as in the best subset selection case.  `Ridge` regression scales all of the coefficients by a constant factor, whereas LASSO translates the coefficients towards zero by a constant value and sets them to zero if they reach it.	
#' 	
#' ## Ridge Regression	
#' 	
#' [Ridge regression](https://en.wikipedia.org/wiki/Tikhonov_regularization) relies on $L^2$ regularization to improve the model prediction accuracy. It improves prediction error by shrinking large regression coefficients and reduce overfitting. By itself, ridge regularization does not perform variable selection and does not really help with model interpretation.	
#' 	
#' Let's look at one example using one of our datasets [01a_data.txt](https://umich.instructure.com/courses/38100/files/folder/data).	
#' 	
#' 	
# Data: https://umich.instructure.com/courses/38100/files/folder/data   (01a_data.txt)	
data <- read.table('https://umich.instructure.com/files/330381/download?download_frd=1', as.is=T, header=T)    	
attach(data); str(data)	
	
# Training Data	
# Full Model: x <- model.matrix(Weight ~ ., data = data[1:900, ])	
# Reduced Model	
x <- model.matrix(Weight ~ Age + Height, data = data[1:900, ])	
# creates a design (or model) matrix, and adds 1 column for outcome according to the formula.	
y <- data[1:900, ]$Weight	
	
# Testing Data	
x.test <- model.matrix(Weight ~ Age + Height, data = data[901:1034, ])	
y.test <- data[901:1034, ]$Weight	
	
# install.packages("glmnet")	
library("glmnet")	
cv.ridge <-  cv.glmnet(x, y, type.measure = "mse", alpha = 0)	
## alpha =1 for lasso only, alpha = 0 for ridge only, and 0<alpha<1 to blend ridge & lasso penalty !!!!	
plot(cv.ridge)	
coef(cv.ridge)	
sqrt(cv.ridge$cvm[cv.ridge$lambda == cv.ridge$lambda.1se])	
	
#plot variable feature coefficients against the shrinkage parameter lambda.	
glmmod <-glmnet(x, y, alpha = 0)	
plot(glmmod, xvar="lambda")	
grid()	
	
# for plot_glmnet with ridge/lasso coefficient path labels	
# install.packages("plotmo")	
library(plotmo) 	
plot_glmnet(glmmod, lwd=4)        #default colors	
# More elaborate plots can be generated using:	
# plot_glmnet(glmmod,label=2,lwd=4) #label the 2 biggest final coefs	
# specify color of each line	
# g <- "blue" 	
# plot_glmnet(glmmod, lwd=4, col=c(2,g))	
	
	
# report the model coefficient estimates	
coef(glmmod)[, 1]	
	
cv.glmmod <- cv.glmnet(x, y, alpha=0)	
	
mod.ridge <-  cv.glmnet(x, y, alpha = 0, thresh = 1e-12)	
lambda.best <-  mod.ridge$lambda.min	
lambda.best	
ridge.pred <-  predict(mod.ridge, newx = x.test, s = lambda.best)	
ridge.RMS <- mean((y.test - ridge.pred)^2); ridge.RMS	
ridge.test.r2 <-  1 - mean((y.test - ridge.pred)^2)/mean((y.test - mean(y.test))^2)	
	
plot(cv.glmmod)	
best_lambda <- cv.glmmod$lambda.min	
best_lambda	
#' 	
#' 	
#' In the plots above, different colors represents the vector of features, and the corresponding coefficients, displayed as a function of the regularization parameter, $\lambda$. The top axis indicates the number of nonzero coefficients at the current value of $\lambda$. For LASSO regularization, this top-axis corresponds to the effective degrees of freedom (df) for the model. 	
#' 	
#' Notice the usefulness of Ridge regularization for model estimation in highly ill-conditioned problems ($n<<k$) where slight feature perturbations may cause disproportionate alterations of the corresponding weight calculations. When $\lambda$ is very large, the regularization effect dominates the optimization of the objective function and the coefficients tend to zero. At the other extreme, as $\lambda\longrightarrow 0$, the resulting model solution tends towards the ordinary least squares (OLS) and the coefficients exhibit large oscillations. Often in practice we need to tune $\lambda$ to balance this tradeoff.	
#' 	
#' Also note that in the `cv.glmnet` call, `alpha = 0` (ridge) and `alpha = 1` (LASSO) correspond to different types of regularization, and $0<alpha<1$ corresponds to *elastic net* blended regularization.	
#' 	
#' ## Least Absolute Shrinkage and Selection Operator (LASSO) Regression	
#' 	
#' Estimating the linear regression coefficients in a linear regression model using LASSO involves minimizing an objective function that includes an $L^1$ regularization term which tends to shrink the number of features. A descriptive representation of the fidelity (left) and regularization (right) terms of the objective function are shown below:	
#' 	
#' $$\underbrace{\sum_{i=1}^n \left [ y_i - \beta_0 - \sum_{j=1}^k \beta_j x_{ij} \right ]^2}_{\text{fidelity term}} + \underbrace{\lambda\sum_{j=1}^{p}|\beta_j|}_{\text{regilarization term}}.$$	
#' LASSO jointly achieves model quality, reliability and variable selection by penalizing the sum of the absolute values of the regression coefficients. This forces the shrinkage of certain coefficients effectively acting as a variable selection process. This is similar to ridge regression's penalty on the sum of the squares of the regression coefficients, although ridge regression only shrinks the magnitude of the coefficients without truncating them to $0$.	
#' 	
#' Let's show how to select the regularization weight parameter $\lambda$ using `training` data and report the error using `testing` data.	
#' 	
#' 	
mod.lasso <-  cv.glmnet(x, y, alpha = 1, thresh = 1e-12)	
## alpha =1 for lasso only, alpha = 0 for ridge only, and 0<alpha<1 for elastic net, a blend ridge & lasso penalty !!!!	
lambda.best <- mod.lasso$lambda.min	
lambda.best	
lasso.pred <- predict(mod.lasso, newx = x.test, s = lambda.best)	
LASSO.RMS <- mean((y.test - lasso.pred)^2); LASSO.RMS	
#' 	
#' 	
#' Let's retrieve the estimates of the model coefficients.	
#' 	
#' 	
mod.lasso <-  glmnet(x, y, alpha = 1)	
predict(mod.lasso, s = lambda.best, type = "coefficients")	
lasso.test.r2 <-  1 - mean((y.test - lasso.pred)^2)/mean((y.test - mean(y.test))^2)	
#' 	
#' 	
#' Perhaps obtain a classical OLS linear model, as well.	
#' 	
#' 	
lm.fit <-  lm(Weight ~ Age + Height, data = data[1:900, ])	
summary(lm.fit)	
#' 	
#' 	
#' The OLS linear (unregularized) model has slightly larger coefficients and greater MSE than LASSO, which attests to the shrinkage of LASSO.	
#' 	
#' 	
lm.pred <-  predict(lm.fit, newx = x.test)	
LM.RMS <- mean((y - lm.pred)^2); LM.RMS	
lm.test.r2 <-  1 - mean((y - lm.pred)^2) / mean((y.test - mean(y.test))^2)	
  	
barplot(c(lm.test.r2, lasso.test.r2, ridge.test.r2), col = "red", names.arg = c("OLS", "LASSO", "Ridge"), main = "Testing Data Derived R-squared")	
#' 	
#' 	
#' Compare the results of the three alternative models (LM, LASSO and Ridge) for these data and contrast the derived RSS results.	
#' 	
#' 	
library(knitr) #  kable function to convert tabular R-results into Rmd tables	
# create table as data frame	
RMS_Table = data.frame(LM=LM.RMS, LASSO=LASSO.RMS, Ridge=ridge.RMS)	
	
# convert to markdown	
kable(RMS_Table, format="pandoc", caption="Test Dataset RSS Results", align=c("c", "c", "c"))	
#' 	
#' 	
#' As both the *inputs* (features or predictors) and the *output* (response) are observed for the testing data, we can build a learner examining the relationship between the two types of features (controlled covariates and observable responses). Most often, we are interested in forecasting s or predicting of responses using prospective (new, testing, or validation) data.	
#' 	
#' # Predictor Standardization	
#'  	
#' Prior to fitting regularized linear modeling and estimating the effects, covariates may be standardized. This can be accomplished by using the classic ["z-score" formula](http://wiki.socr.umich.edu/index.php/AP_Statistics_Curriculum_2007_Normal_Prob#General_Normal_Distribution]). This puts each predictor on the same scale (unitless quantities) - the mean is 0 and the variance is 1. We use $\hat{\beta_0} = \bar{y}$, for the mean intercept parameter, and estimate the coefficients or the remaining predictors. To facilitate interpretation of the model or results in the context of the specific case-study, we can transform the results back to the original scale/units after the model is estimated.	
#' 	
#' # Estimation Goals	
#' 	
#' The basic setting here is: given a set of predictors ${X}$, find a function, $f({X})$, to model or predict the outcome $Y$.	
#' 	
#' Let's denote the objective (loss or cost) function by $L(y, f({X}))$. It determines adequacy of the fit and allows us to estimate the squared error loss:	
#' $$L(y, f({X})) = (y - f({X}))^2 . $$	
#' 	
#' We are looking to find $f$ that minimizes the expected loss:	
#' $$  E[(Y - f({X}))^2] \Rightarrow f = E[Y | {X} = {x}].$$	
#' 	
#' # Linear Regression	
#' 	
#' Let's assume that:	
#' 	
#' * $$Y_i = \beta_0 + x_{i1}\beta_1 + x_{i2}\beta_2 + \dots + x_{ip}\beta_p + \epsilon, $$	
#' * In shorthand matrix notation, that is: $Y = X\beta.$	
#' * And the expectation of the observed outcome given the data, $E[Y | {X} = {x}]$, is a linear function, which in certain situations can be expressed as:	
#' $$\arg\min_{{\beta}} \sum_{i=1}^n \left (y_i - \sum_{j=1}^{p} x_{ij} \beta_j \right )^2 = \arg\min_{{\beta}} \sum_{i=1}^{n} (y_i - x_{i}^T \beta).$$	
#' 	
#' Multiplying both hands sides by $X^T=X'$, the transpose of the design matrix $X$ (on the left, as matrix multiplication is not always commutative), yields:	
#' $$X^T Y = X^T (X\beta) = (X^TX)\beta.$$	
#' 	
#' To solve for the effect-sizes $\beta$, we can multiply both sides of the equation by the inverse of its (right hand side) multiplier:	
#' $$(X^TX)^{-1} (X^T Y) = (X^TX)^{-1} (X^TX)\beta = \beta.$$	
#' 	
#' The *ordinary least squares (OLS)* estimate of ${\beta}$ is given by:	
#' $$\hat{{\beta}} = 	
#'   \arg\min_{{\beta}} \sum_{i=1}^n (y_i - \sum_{j=1}^{p} x_{ij} \beta_j)^2 = \arg\min_{{\beta}} \| {y} - {X}{\beta} \|^2_2 \Rightarrow$$	
#'   $$\hat{{\beta}}^{OLS} = ({X}'{X})^{-1} {X}'{y} \Rightarrow \hat{f}({x}_i) = {x}_i'\hat{{\beta}}.$$	
#' 	
#' ## Drawbacks of Linear Regression	
#' 	
#' Despite its wide use and elegant theory, linear regression has some shortcomings.	
#' 	
#' - Prediction accuracy - Often can be improved upon;	
#' 	
#' - Model interpretability - Linear model does not automatically do variable selection.	
#' 	
#' ## Assessing Prediction Accuracy	
#' 	
#' Given a new input, ${x}_0$, how do we assess our prediction $\hat{f}({x}_0)$?	
#' 	
#' The *Expected Prediction Error (EPE)* is:	
#'  $$ \begin{aligned} EPE({x}_0) &= E[(Y_0 - \hat{f}({x}_0))^2] \\	
#'                    &= \text{Var}(\epsilon) + \text{Var}(\hat{f}({x}_0)) + \text{Bias}(\hat{f}({x}_0))^2 \\	
#'                    &= \text{Var}(\epsilon) + MSE(\hat{f}({x}_0)) \end{aligned} .$$	
#' 	
#' where	
#' 	
#' * $\text{Var}(\epsilon)$: irreducible error variance	
#' * $\text{Var}(\hat{f}({x}_0))$: sample-to-sample variability of $\hat{f}({x}_0)$ , and	
#' * $\text{Bias}(\hat{f}({x}_0))$: average difference of  $\hat{f}({x}_0)$ & $f({x}_0)$.	
#'     	
#' ## Estimating the Prediction Error 	
#' 	
#' Common approach to estimating prediction error include:	
#' 	
#' * Randomly splitting the data into "training" and "testing" sets, where the testing data has $m$ observations that will be used to independently validate the model quality. We estimate/calculate $\hat{f}$ using training data;	
#'  * Estimating prediction error using the *testing set MSE*	
#' $$ \hat{MSE}(\hat{f}) = \frac{1}{m} \sum_{i=1}^{m} (y_i - \hat{f}(x_i))^2.$$	
#' 	
#' Ideally, we want our model/predictions to perform well with new or prospective data.	
#' 	
#' ## Improving the Prediction Accuracy	
#' 	
#' If $f(x) \approx \text{linear}$, $\hat{f}$ will have low bias but possibly high variance, e.g., in high-dimensional setting due to correlated predictors, over  ($k\ \text{features} << n\ \text{cases}$), or underdetermination ($k > n$).  The goal is to minimize total error by trading off bias and precision:	
#' $$MSE(\hat{f}(x)) = \text{Var}(\hat{f}(x)) +\text{Bias}(\hat{f}(x))^2.$$	
#' We can sacrifice bias to reduce variance, which may lead to decrease in $MSE$. So, regularization allows us to tune this tradeoff.	
#' 	
#' We aim to predict the outcome variable, $Y_{n\times1}$, in terms of other features $X_{n,k}$. Assume a first-order relationship relating $Y$ and $X$ is of the form $Y=f(X)+\epsilon$, where the error term $\epsilon \sim N(0,\sigma)$. An estimate model $\hat{f}(X)$ can be computed in many different ways (e.g., using least squares calculations for linear regressions, Newton-Raphson, steepest decent and other methods). Then, we can decompose the expected squared prediction error at $x$ as:	
#' 	
#' $$E(x)=E[(Y-\hat{f}(x))^2] = \underbrace{\left ( E[\hat{f}(x)]-f(x) \right )^2}_{Bias^2} + 	
#' \underbrace{E\left [\left (\hat{f}(x)-E[\hat{f}(x)] \right )^2 \right]}_{\text{precision (variance)}} + \underbrace{\sigma^2}_{\text{irreducible error (noise)}}.$$	
#' 	
#' When the true $Y$ vs. $X$ relation is not known, infinite data may be necessary to calibrate the model $\hat{f}$ and it may be impractical to jointly reduce both the model *bias* and *variance*. In general, minimizing the *bias* at the same time as minimizing the *variance* may not be possible.	
#' 	
#' The figure below illustrates diagrammatically the dichotomy between *bias* and *precision* (variance), additional information is available in the [SOCR SMHS EBook](http://wiki.socr.umich.edu/index.php/SMHS_BiasPrecision).	
#' 	
#' ![](http://wiki.socr.umich.edu/images/d/dd/SMHS_BIAS_Precision_Fig_1_cg_07282014.png)	
#' 	
#' ## Variable Selection	
#' 	
#' Oftentimes, we are only interested in using a subset of the original features as model predictors. Thus, we need to identify the most relevant predictors, which usually capture the big picture of the process. This helps us avoid overly complex models that may be difficult to interpret. Typically, when considering several models achieve similar results, it's natural to select the simplest of them.	
#' 	
#' Linear regression does not directly determine the importance of features to predict a specific outcome. The problem of selecting critical predictors is therefore very important.	
#' 	
#' Automatic feature subset selection methods should directly determine an optimal subset of variables. Forward or backward stepwise variable selection and forward stagewise are examples of classical methods for choosing the best subset by assessing various metrics like $MSE$, $C_p$, AIC, or BIC, see [Chapter 16](http://www.socr.umich.edu/people/dinov/2017/Spring/DSPA_HS650/notes/16_FeatureSelection.html).	
#' 	
#' # Regularization Framework	
#' 	
#' As before, we start with a given ${X}$ and look for a (linear) function, $f({X})$, to model or predict $y$ subject to certain objective cost function, e.g., squared error loss. Adding a second term to the cost function minimization process yields (model parameter) estimates expressed as:	
#' 	
#' $$\hat{{\beta}}(\lambda) = \arg\min_{\beta} 	
#'   \left\{\sum_{i=1}^n (y_i - \sum_{j=1}^{p} x_{ij} \beta_j)^2 	
#'   + \lambda J({\beta})\right\}$$	
#' 	
#' In the above expression, $\lambda \ge 0$ is the regularization (tuning or penalty) parameter, $J({\beta})$ is a `user-defined penalty function` - typically, the intercept is not penalized.	
#'  	
#' ## Role of the *Penalty Term*	
#' 	
#' Consider $\arg\min J({\beta}) = \sum_{j=1}^k \beta_j^2 =\| {\beta} \|^2_2$ (Ridge Regression, *RR*).	
#' 	
#' Then, the formulation of the regularization framework is:	
#' $$\hat{{\beta}}(\lambda)^{RR} = \arg\min_{{\beta}} 	
#'   \left\{\sum_{i=1}^n (y_i - \sum_{j=1}^{p} x_{ij} \beta_j)^2 + 	
#'   \lambda \sum_{j=1}^k \beta_j^2 \right\}.$$	
#' 	
#' Or, alternatively:	
#' 	
#' $$\hat{{\beta}}(t)^{RR} = \arg\min_{{\beta}} 	
#'   \sum_{i=1}^n (y_i - \sum_{j=1}^{p} x_{ij} \beta_j)^2, $$ 	
#' subject to	
#' $$\sum_{j=1}^k \beta_j^2 \le t .$$	
#' 	
#' ## Role of the *Regularization Parameter*	
#' 	
#' The regularization parameter $\lambda\geq 0$ directly controls the bias-variance trade-off:	
#' 	
#' * $\lambda = 0$ corresponds to OLS, and	
#' * $\lambda \rightarrow \infty$ puts more weight on the penalty function and results in more shrinkage of the coefficients, i.e., we introduce bias at the sake of reducing the variance.	
#' 	
#' The choice of $\lambda$ is crucial and will be discussed below as each $\lambda$ results in a different solution $\hat{{\beta}}(\lambda)$.  	
#' 	
#' ## LASSO	
#' The LASSO (Least Absolute Shrinkage and Selection Operator) regularization relies on: 	
#' $$\arg\min J({\beta}) = \sum_{j=1}^k |\beta_j| =  \| {\beta} \|_1,$$	
#' which leads to the following objective function:	
#' $$\hat{{\beta}}(\lambda)^{L} = \arg\min_{\beta} 	
#'   \left\{\sum_{i=1}^n (y_i - \sum_{j=1}^{k} x_{ij} \beta_j)^2 + 	
#'   \lambda \sum_{j=1}^k |\beta_j| \right\}.$$	
#' 	
#' In practice, subtle changes in the penalty terms frequently lead to big differences of the results. Not only does the regularization term shrink coefficients towards zero, but it sets some of them to be exactly zero. Thus, it performs continuous variable selection, hence the name, Least Absolute Shrinkage and Selection Operator (LASSO).	
#' 	
#' [For further details, see the "Tibshirani's LASSO Page"](http://statweb.stanford.edu/~tibs/LASSO.html).	
#' 	
#' ## General Regularization Framework	
#' 	
#' The general regularization framework involves optimization of a more general objective function:	
#' 	
#' $$\min_{f \in {H}} \sum_{i=1}^n \left\{L(y_i, f(x_i)) + \lambda J(f)\right\}, $$	
#' 	
#' where $\mathcal{H}$ is a space of possible functions, $L$ is the *fidelity term*, e.g., squared error, absolute error, zero-one, negative log-likelihood (GLM), hinge loss (support vector machines), and $J$ is the *regularizer*, e.g., [ridge regression, LASSO, adaptive LASSO, group LASSO, fused LASSO, thresholded LASSO, generalized LASSO, constrained LASSO, elastic-net, Dantzig selector, SCAD, MCP, smoothing splines](http://www.stat.cmu.edu/~ryantibs/papers/genlasso.pdf), etc. 	
#' 	
#' This represents a very general and flexible framework that allows us to incorporate prior knowledge (sparsity, structure, etc.) into the model estimation.	
#' 	
#' # Implementation of Regularization	
#' 	
#' ## Example: Neuroimaging-genetics study of Parkinson's Disease Dataset	
#' 	
#' More information about this specific study and the included derived [neuroimaging biomarkers is available here](http://wiki.socr.umich.edu/index.php/SOCR_Data_PD_BiomedBigMetadata). A link to the data and a brief summary of the features are included below:	
#' 	
#' - [05_PPMI_top_UPDRS_Integrated_LongFormat1.csv](https://umich.instructure.com/files/330397/download?download_frd=1)	
#' - Data elements include: FID_IID, L_insular_cortex_ComputeArea, L_insular_cortex_Volume, R_insular_cortex_ComputeArea, R_insular_cortex_Volume, L_cingulate_gyrus_ComputeArea, L_cingulate_gyrus_Volume, R_cingulate_gyrus_ComputeArea, R_cingulate_gyrus_Volume, L_caudate_ComputeArea, L_caudate_Volume, R_caudate_ComputeArea, R_caudate_Volume, L_putamen_ComputeArea, L_putamen_Volume, R_putamen_ComputeArea, R_putamen_Volume, Sex, Weight, ResearchGroup, Age, chr12_rs34637584_GT, chr17_rs11868035_GT, chr17_rs11012_GT, chr17_rs393152_GT, chr17_rs12185268_GT, chr17_rs199533_GT, UPDRS_part_I, UPDRS_part_II, UPDRS_part_III, time_visit	
#' 	
#' Note that the dataset includes missing values and repeated measures.	
#' 	
#' The *goal* of this demonstration is to use `OLS`, `ridge regression`, and the `LASSO` to **find the best predictive model for the clinical outcomes** -- UPRDR score (vector) and Research Group (factor variable), in terms of demographic, genetics, and neuroimaging biomarkers.	
#' 	
#' We can utilize the `glmnet` package in R for most calculations.	
#' 	
#' 	
#### Initial Stuff ####	
# clean up	
rm(list=ls())	
# load required packages	
# install.packages("arm")	
library(glmnet)	
library(arm)	
library(knitr) #  kable function to convert tabular R-results into Rmd tables	
# pick a random seed, but set.seed(seed) only effects next block of code!	
seed = 1234	
	
#### Organize Data ####	
# load dataset	
# Data: https://umich.instructure.com/courses/38100/files/folder/data  	
# (05_PPMI_top_UPDRS_Integrated_LongFormat1.csv)	
data1 <- read.table('https://umich.instructure.com/files/330397/download?download_frd=1', sep=",", header=T)    	
# we will deal with missing values using multiple imputation later. For now, let's just ignore incomplete cases	
data1.completeRowIndexes <- complete.cases(data1); table(data1.completeRowIndexes)	
prop.table(table(data1.completeRowIndexes))	
attach(data1)	
# View(data1[data1.completeRowIndexes, ])	
	
# define response and predictors	
y <- data1$UPDRS_part_I + data1$UPDRS_part_II + data1$UPDRS_part_III	
table(y)   # Show Clinically relevant classification	
y <- y[data1.completeRowIndexes]	
	
# X = scale(data1[,])  # Explicit Scaling is not needed, as glmnet auto standardizes predictors	
# X = as.matrix(data1[, c("R_caudate_Volume", "R_putamen_Volume", "Weight", "Age", "chr17_rs12185268_GT")])  # X needs to be a matrix, not a data frame	
drop_features <- c("FID_IID", "ResearchGroup", "PDRS_part_I", "UPDRS_part_II", "UPDRS_part_III")	
X <- data1[ , !(names(data1) %in% drop_features)]	
X = as.matrix(X)   # remove columns: index, ResearchGroup, and y=(PDRS_part_I + UPDRS_part_II + UPDRS_part_III)	
X <- X[data1.completeRowIndexes, ]	
summary(X)	
	
# randomly split data into training (80%) and test (20%) sets	
set.seed(seed)	
train = sample(1 : nrow(X), round((4/5) * nrow(X)))	
test = -train	
	
# subset training data	
yTrain = y[train]	
XTrain = X[train, ]	
XTrainOLS = cbind(rep(1, nrow(XTrain)), XTrain)	
  	
# subset test data	
yTest = y[test]	
XTest = X[test, ]	
	
#### Model Estimation & Selection ####	
# Estimate models	
fitOLS = lm(yTrain ~ XTrain)  # Ordinary Least Squares	
# glmnet automatically standardizes the predictors	
fitRidge = glmnet(XTrain, yTrain, alpha = 0)  # Ridge Regression	
fitLASSO = glmnet(XTrain, yTrain, alpha = 1)  # The LASSO	
#' 	
#' 	
#' Readers are encouraged to compare the two models, *ridge* and *LASSO.*	
#' 	
#' ## Computational Complexity	
#' 	
#' Recall that the regularized regression estimates depend on the regularization parameter $\lambda$. Fortunately, efficient algorithms for choosing optimal $\lambda$ parameters do exist. Examples of solution path algorithms include:	
#' 	
#' * [LARS Algorithm for the LASSO](http://projecteuclid.org/euclid.aos/1083178935) (Efron et al., 2004)	
#' * [Piecewise linearity](http://www.jstor.org/stable/25463590?seq=1#page_scan_tab_contents) (Rosset & Zhu, 2007)	
#' * [Generic path algorithm](http://dx.doi.org/10.1080/01621459.2013.864166) (Zhou & Wu, 2013)	
#' * [Pathwise coordinate descent](http://projecteuclid.org/euclid.aoas/1196438020) (Friedman et al., 2007)	
#' * [Alternating Direction Method of Multipliers (ADMM)](https://doi.org/10.1561/2200000016) (Boyd et al. 2011)	
#' 	
#' We will show how to visualize the relations between the regularization parameter ($\ln(\lambda)$) and the number and magnitude of the corresponding coefficients for each specific regularized regression method.	
#' 	
#' ## LASSO and Ridge Solution Paths	
#' 	
#' The plot for the *LASSO* results can be obtained via:	
#' 	
#' 	
### Plot Solution Path ###	
# LASSO	
plot(fitLASSO, xvar="lambda", label="TRUE")	
# add label to upper x-axis	
mtext("LASSO regularizer: Number of Nonzero (Active) Coefficients", side=3, line=2.5)	
#' 	
#' 	
#' Similarly, the plot for the *Ridge* regularization can be obtained by:	
#' 	
#' 	
### Plot Solution Path ###	
# Ridge	
plot(fitRidge, xvar="lambda", label="TRUE")	
# add label to upper x-axis	
mtext("Ridge regularizer: Number of Nonzero (Active) Coefficients", side=3, line=2.5)	
#' 	
#' 	
#' ## Regression Solution Paths - Ridge vs. LASSO	
#' 	
#' Let's try to compare the paths of the *LASSO* and *Ridge* regression solutions. Below, you will see that the curves of LASSO are steeper and non-differentiable at some points, which is the result of using the $L_1$ norm. On the other hand, the Ridge path is smoother and asymptotically tends to $0$ as $\lambda$ increases.	
#' 	
#' Let's start by examining the joint objective function (including LASSO and Ridge terms):	
#' 	
#' $$\min_\beta \left (\sum_i (y_i-x_i\beta)^2+\frac{1-\alpha}{2}||\beta||_2^2+\alpha||\beta||_1	
#' \right ),$$	
#' 	
#' where $||\beta||_1 = \sum_{j=1}^{p}|\beta_j|$ and $||\beta||_2 = \sqrt{\sum_{j=1}^{p}||\beta_j||^2}$ are the norms of $\boldsymbol\beta$ corresponding to the $L_1$ and $L_2$ distance measures, respectively. When $\alpha=0$ and $\alpha=1$ correspond to *Ridge* and *LASSO* regularization. The following two natural questions raise:	
#' 	
#' * What if $0 <\alpha<1$?	
#' * How does the regularization penalty term affect the optimal solution?	
#' 	
#' [In Chapter 9](http://www.socr.umich.edu/people/dinov/2017/Spring/DSPA_HS650/notes/09_RegressionForecasting.html), we explored the minimal SSE (Sum of Square Error) for the OLS (without penalty) where the feasible parameter ($\beta$) spans the entire real solution space. In penalized optimization problems, the best solution may actually be unachievable. Therefore, we look for solutions that are "closest", within the feasible region, to the enigmatic best solution.	
#' 	
#' The effect of the penalty term on the objective function is separate from the *fidelity term* (OLS solution). Thus, the effect of $0\leq \alpha \leq 1$ is limited to the **size and shape of the penalty region**. Let's try to visualize the feasible region as:	
#' 	
#' * centrosymmetric, when $\alpha=0$, and	
#' * super diamond, then $\alpha=1$.	
#' 	
#' Here is a hands-on example:	
#' 	
#' 	
require(needs)	
	
# Constructing Quadratic Formula	
result <- function(a,b,c){	
  if(delta(a,b,c) > 0){ # first case D>0	
    x_1 = (-b+sqrt(delta(a,b,c)))/(2*a)	
    x_2 = (-b-sqrt(delta(a,b,c)))/(2*a)	
    result = c(x_1,x_2)	
  }	
  else if(delta(a,b,c) == 0){ # second case D=0	
    x = -b/(2*a)	
  }	
  else {"There are no real roots."} # third case D<0	
}	
# Constructing delta	
delta<-function(a,b,c){	
  b^2-4*a*c	
}	
#' 	
#' 	
#' To make this realistic, we will use the [MLB dataset](https://umich.instructure.com/files/330381/download?download_frd=1) to first fit an OLS model. The dataset contains $1,034$ records of *heights and weights* for some current and recent Major League Baseball (MLB) Players. 	
#' 	
#' * *Height*: Player height in inch,	
#' * *Weight*: Player weight in pounds,	
#' * *Age*: Player age at time of record.	
#' 	
#' Then, we can obtain the SSE for any $||\boldsymbol\beta||$:	
#' 	
#' $$SSE = ||Y-\hat Y||^2 = (Y-\hat Y)^{T}(Y-\hat Y)=Y^TY - 2\beta^TX^TY + \beta^TX^TX\beta.$$	
#' 	
#' Next, we will compute the contours for SSE in several situations.	
#' 	
#' 	
library("ggplot2")	
# load data	
mlb<- read.table('https://umich.instructure.com/files/330381/download?download_frd=1', as.is=T, header=T)	
str(mlb)	
fit<-lm(Height~Weight+Age-1, data = as.data.frame(scale(mlb[,4:6])))	
points = data.frame(x=c(0,fit$coefficients[1]),y=c(0,fit$coefficients[2]),z=c("(0,0)","OLS Coef"))	
	
Y=scale(mlb$Height)	
X = scale(mlb[,c(5,6)])	
beta1=seq(-0.556, 1.556, length.out = 100)	
beta2=seq(-0.661, 0.3386, length.out = 100)	
df <- expand.grid(beta1 = beta1, beta2 = beta2)	
b = as.matrix(df)	
df$sse <- rep(t(Y)%*%Y,100*100) - 2*b%*%t(X)%*%Y + diag(b%*%t(X)%*%X%*%t(b))	
#' 	
#' 	
#' 	
base <- ggplot(df) + 	
  stat_contour(aes(beta1, beta2, z = sse),breaks = round(quantile(df$sse, seq(0, 0.2, 0.03)), 0), 	
               size = 0.5,color="darkorchid2",alpha=0.8)+ 	
  scale_x_continuous(limits = c(-0.4,1))+	
  scale_y_continuous(limits = c(-0.55,0.4))+	
  coord_fixed(ratio=1)+	
  geom_point(data = points,aes(x,y))+	
  geom_text(data = points,aes(x,y,label=z),vjust = 2,size=3.5)+	
  geom_segment(aes(x = -0.4, y = 0, xend = 1, yend = 0),colour = "grey46",	
               arrow = arrow(length=unit(0.30,"cm")),size=0.5,alpha=0.8)+	
  geom_segment(aes(x = 0, y = -0.55, xend = 0, yend = 0.4),colour = "grey46",	
               arrow = arrow(length=unit(0.30,"cm")),size=0.5,alpha=0.8)	
#' 	
#' 	
#' 	
plot_alpha = function(alpha=0,restrict=0.2,beta1_range=0.2,annot=c(0.15,-0.25,0.205,-0.05)){	
  a=alpha; t=restrict; k=beta1_range; pos=data.frame(V1=annot[1:4])	
  tex=paste("(",as.character(annot[3]),",",as.character(annot[4]),")",sep = "")	
  K = seq(0,k,length.out = 50)	
  y = unlist(lapply((1-a)*K^2/2+a*K-t,result,a=(1-a)/2,b=a))[seq(1,99,by=2)]	
  fills = data.frame(x=c(rev(-K),K),y1=c(rev(y),y),y2=c(-rev(y),-y))	
  p<-base+geom_line(data=fills,aes(x = x,y = y1),colour = "salmon1",alpha=0.6,size=0.7)+	
    geom_line(data=fills,aes(x = x,y = y2),colour = "salmon1",alpha=0.6,size=0.7)+	
    geom_polygon(data = fills, aes(x, y1),fill = "red", alpha = 0.2)+	
    geom_polygon(data = fills, aes(x, y2), fill = "red", alpha = 0.2)+	
    geom_segment(data=pos,aes(x = V1[1] , y = V1[2], xend = V1[3], yend = V1[4]),	
                 arrow = arrow(length=unit(0.30,"cm")),alpha=0.8,colour = "magenta")+	
    ggplot2::annotate("text", x = pos$V1[1]-0.01, y = pos$V1[2]-0.11,	
                      label = paste(tex,"\n","Point of Contact \n i.e., Coef of", "alpha=",fractions(a)),size=3)+	
    xlab(expression(beta[1]))+	
    ylab(expression(beta[2]))+	
    ggtitle(paste("alpha =",as.character(fractions(a))))+	
    theme(legend.position="none")	
}	
#' 	
#' 	
#' 	
# $\alpha=0$ - Ridge	
p1 <- plot_alpha(alpha=0,restrict=(0.21^2)/2,beta1_range=0.21,annot=c(0.15,-0.25,0.205,-0.05))	
p1 <- p1 + ggtitle(expression(paste(alpha, "=0 (Ridge)")))	
# $\alpha=1/9$	
p2 <- plot_alpha(alpha=1/9,restrict=0.046,beta1_range=0.22,annot =c(0.15,-0.25,0.212,-0.02))	
p2 <- p2 + ggtitle(expression(paste(alpha, "=1/9")))	
# $\alpha=1/5$	
p3 <- plot_alpha(alpha=1/5,restrict=0.063,beta1_range=0.22,annot=c(0.13,-0.25,0.22,0))	
p3 <- p3 + ggtitle(expression(paste(alpha, "=1/5")))	
# $\alpha=1/2$	
p4 <- plot_alpha(alpha=1/2,restrict=0.123,beta1_range=0.22,annot=c(0.12,-0.25,0.22,0))	
p4 <- p4 + ggtitle(expression(paste(alpha, "=1/2")))	
# $\alpha=3/4$	
p5 <- plot_alpha(alpha=3/4,restrict=0.17,beta1_range=0.22,annot=c(0.12,-0.25,0.22,0))	
p5 <- p5 + ggtitle(expression(paste(alpha, "=3/4")))	
#' 	
#' 	
#' 	
# $\alpha=1$ - LASSO	
t=0.22	
K = seq(0,t,length.out = 50)	
fills = data.frame(x=c(-rev(K),K),y1=c(rev(t-K),c(t-K)),y2=c(-rev(t-K),-c(t-K)))	
p6 <- base + 	
  geom_segment(aes(x = 0, y = t, xend = t, yend = 0),colour = "salmon1",alpha=0.1,size=0.2)+	
  geom_segment(aes(x = 0, y = t, xend = -t, yend = 0),colour = "salmon1",alpha=0.1,size=0.2)+	
  geom_segment(aes(x = 0, y = -t, xend = t, yend = 0),colour = "salmon1",alpha=0.1,size=0.2)+	
  geom_segment(aes(x = 0, y = -t, xend = -t, yend = 0),colour = "salmon1",alpha=0.1,size=0.2)+	
  geom_polygon(data = fills, aes(x, y1),fill = "red", alpha = 0.2)+	
  geom_polygon(data = fills, aes(x, y2), fill = "red", alpha = 0.2)+	
  geom_segment(aes(x = 0.12 , y = -0.25, xend = 0.22, yend = 0),colour = "magenta",	
               arrow = arrow(length=unit(0.30,"cm")),alpha=0.8)+	
  ggplot2::annotate("text", x = 0.11, y = -0.36,	
                    label = "(0.22,0)\n Point of Contact \n i.e Coef of LASSO",size=3)+	
  xlab( expression(beta[1]))+	
  ylab( expression(beta[2]))+	
  theme(legend.position="none")+  	
  ggtitle(expression(paste(alpha, "=1 (LASSO)")))	
#' 	
#' 	
#' Then, let's add the six feasible regions corresponding to $\alpha=0$ (Ridge), $\alpha=\frac{1}{9}$, $\alpha=\frac{1}{5}$, $\alpha=\frac{1}{2}$, $\alpha=\frac{3}{4}$ and $\alpha=1$ (LASSO).	
#' 	
#' This figure provides some intuition into the continuum from Ridge to LASSO regularization. The feasible regions are drawn as ellipse contours of the SSE in *red*. Curves around the corresponding feasible regions represent the *boundary of the constraint function* $\frac{1-\alpha}{2}||\beta||_2^2+\alpha||\beta||_1\leq t$.	
#' 	
#' In this example, $\beta_2$ shrinks to $0$ for $\alpha=\frac{1}{5}$, $\alpha=\frac{1}{2}$, $\alpha=\frac{3}{4}$ and $\alpha=1$.	
#' 	
#' We observe that it is almost impossible for the contours of Ridge regression to touch the circle at any of the coordinate axes. This is also true in higher dimensions ($nD$), where the $L_1$ and $L_2$ metrics are unchanged and the 2D ellipse representations of the feasibility regions become hyper-ellipsoidal shapes.	
#' 	
#' Generally, as $\alpha$ goes from $0$ to $1$. The coefficients of more features tend to shrink towards $0$. This specific property makes LASSO useful for variable selection.	
#' 	
#' Let's compare the feasibility regions corresponding to *Ridge* (top, $p1$) and *LASSO* (bottom, $p6$) regularization.	
#' 	
#' 	
plot(p1)	
#' 	
#' 	
#' 	
plot(p6)	
#' 	
#' 	
#' Then, we can plot the *progression* from Ridge to LASSO.	
#' (This composite *plot is intense* and may take several minutes to render!)	
#' 	
#' 	
library("gridExtra")	
grid.arrange(p1,p2,p3,p4,p5,p6,nrow=3)	
#' 	
#' 	
#' ## Choice of the Regularization Parameter	
#' 	
#' Efficiently obtaining the entire solution path is nice, but we still have to choose a specific $\lambda$ regularization parameter. This is critical as $\lambda$ `controls the bias-variance tradeoff`.	
#' 	
#' Traditional model selection methods rely on various metrics like [Mallows' $C_p$](https://en.wikipedia.org/wiki/Mallows%27s_Cp), [AIC](https://en.wikipedia.org/wiki/Akaike_information_criterion), [BIC](https://en.wikipedia.org/wiki/Bayesian_information_criterion), and adjusted $R^2$.	
#' 	
#' Internal statistical validation (Cross validation) is a popular modern alternative, which offers some of these benefits:	
#' 	
#' * Choice is based on predictive performance,	
#' * Makes fewer model assumptions,	
#' * More widely applicable.	
#' 	
#' ## Cross Validation Motivation	
#' 	
#' Ideally, we would like a separate validation set for choosing $\lambda$ for a given method. Reusing training sets may encourage overfitting and using testing data to pick $\lambda$ may underestimates the true error rate. Often, when we do not have enough data for a separate validation set, cross validation provides an alternative strategy.	
#' 	
#' ## $n$-Fold Cross Validation	
#' 	
#' We have already seen examples of using cross-validation, e.g., [Chapter 13](http://www.socr.umich.edu/people/dinov/2017/Spring/DSPA_HS650/notes/13_ModelEvaluation.html#4_estimating_future_performance_(internal_statistical_validation)) and [Chapter 20](http://www.socr.umich.edu/people/dinov/2017/Spring/DSPA_HS650/notes/20_PredictionCrossValidation.html) provides more details about this internal statistical assessment strategy. 	
#' 	
#' We can use either automated or manual cross-validation. In either case, the protocol involves the following iterative steps:	
#' 	
#' 1. Randomly split the training data into $n$ parts ("folds").	
#' 2. Fit a model using data in $n-1$ folds for multiple $\lambda\text{s}$.	
#' 3. Calculate some prediction quality metrics (e.g., MSE, accuracy) on the last remaining fold, see [Chapter 13](http://www.socr.umich.edu/people/dinov/2017/Spring/DSPA_HS650/notes/13_ModelEvaluation.html).	
#' 4. Repeat the process and average the prediction metrics across iterations.	
#' 	
#' Common choices of $n$ are 5, 10, and $n$ (which corresponds to `leave-one-out` CV). One standard error rule is to choose $\lambda$ corresponding to smallest model with MSE within one standard error of the minimum MSE.	
#' 	
#' ## LASSO 10-Fold Cross Validation	
#' 	
#' Now, let's apply an internal statistical cross-validation to assess the quality of the LASSO and Ridge models, based on our Parkinson's disease case-study. Recall our split of the PD data into training (yTrain, XTrain) and testing (yTest, XTest) sets.	
#' 	
#' 	
#### 10-fold cross validation ####	
# LASSO	
library("glmnet")	
set.seed(seed)  # set seed 	
# (10-fold) cross validation for the LASSO	
cvLASSO = cv.glmnet(XTrain, yTrain, alpha = 1)	
plot(cvLASSO)	
mtext("CV LASSO: Number of Nonzero (Active) Coefficients", side=3, line=2.5)	
	
# Report MSE LASSO	
predLASSO <-  predict(cvLASSO, s = cvLASSO$lambda.1se, newx = XTest)	
testMSE_LASSO <- mean((predLASSO - yTest)^2); testMSE_LASSO	
#' 	
#' 	
#' 	
#### 10-fold cross validation ####	
# Ridge Regression	
set.seed(seed)  # set seed 	
# (10-fold) cross validation for Ridge Regression	
cvRidge = cv.glmnet(XTrain, yTrain, alpha = 0)	
plot(cvRidge)	
mtext("CV Ridge: Number of Nonzero (Active) Coefficients", side=3, line=2.5)	
	
# Report MSE Ridge	
predRidge <-  predict(cvRidge, s = cvRidge$lambda.1se, newx = XTest)	
testMSE_Ridge <- mean((predRidge - yTest)^2); testMSE_Ridge	
#' 	
#' 	
#' Note that the `predict()` method applied to `cv.gmlnet` or `glmnet` forecasting models is effectively a function wrapper to `predict.gmlnet()`. According to what you would like to get as a **prediction output**, you can use `type="..."` to specify one of the following types of prediction outputs:	
#' 	
#' * `type="link"`, reports the linear predictors for "binomial", "multinomial", "poisson" or "cox" models; for "gaussian" models it gives the fitted values. 	
#' * `type="response"`, reports the fitted probabilities for "binomial" or "multinomial", fitted mean for "poisson" and the fitted relative-risk for "cox"; for "gaussian" type "response" is equivalent to type "link". 	
#' * `type="coefficients"`, reports the coefficients at the requested values for `s`. Note that for "binomial" models, results are returned only for the class corresponding to the second level of the factor response. 	
#' * `type="class"`, applies only to "binomial" or "multinomial" models, and produces the class label corresponding to the maximum probability. 	
#' * `type="nonzero"`, returns a list of the indices of the nonzero coefficients for each value of `s`.	
#' 	
#' ## Stepwise OLS (ordinary least squares)	
#' 	
#' For a fair comparison, let's also obtain an OLS stepwise model selection, see [Chapter 16](http://www.socr.umich.edu/people/dinov/2017/Spring/DSPA_HS650/notes/16_FeatureSelection.html).	
#' 	
#' 	
dt = as.data.frame(cbind(yTrain,XTrain))	
ols_step <- lm(yTrain ~., data = dt)	
ols_step <- step(ols_step, direction = 'both', k=2, trace = F)	
summary(ols_step)	
#' 	
#' 	
#' We use `direction=both` for both *forward* and *backward* selection and choose the optimal one. `k=2` specifies AIC and BIC criteria, and you can choose $k\sim \log(n)$.	
#' 	
#' Then, we use the `ols_step` model to predict the outcome $Y$ for some new test data.	
#' 	
#' 	
betaHatOLS_step = ols_step$coefficients	
var_step <- colnames(ols_step$model)[-1]	
XTestOLS_step = cbind(rep(1, nrow(XTest)), XTest[,var_step])	
predOLS_step = XTestOLS_step%*%betaHatOLS_step 	
testMSEOLS_step = mean((predOLS_step - yTest)^2)	
# Report MSE OLS Stepwise feature selection	
testMSEOLS_step	
#' 	
#' 	
#' Alternatively, we can predict the outcomes directly using the `predict()` function, and the results should be identical:	
#' 	
#' 	
pred2 <- predict(ols_step,as.data.frame(XTest))	
any(pred2 == predOLS_step)	
#' 	
#' 	
#' ## Final Models	
#' 	
#' Let's identify the most important (predictive) features, which can then be interpreted in the context of the specific data.	
#' 	
#' 	
# Determine final models	
	
# Extract Coefficients	
# OLS coefficient estimates	
betaHatOLS = fitOLS$coefficients	
# LASSO coefficient estimates 	
betaHatLASSO = as.double(coef(fitLASSO, s = cvLASSO$lambda.1se))  # s is lambda	
# Ridge  coefficient estimates 	
betaHatRidge = as.double(coef(fitRidge, s = cvRidge$lambda.1se))	
	
# Test Set MSE	
# calculate predicted values	
	
XTestOLS = cbind(rep(1, nrow(XTest)), XTest) # add intercept to test data	
predOLS = XTestOLS%*%betaHatOLS 	
predLASSO = predict(fitLASSO, s = cvLASSO$lambda.1se, newx = XTest)	
predRidge = predict(fitRidge, s = cvRidge$lambda.1se, newx = XTest)	
	
# calculate test set MSE	
testMSEOLS = mean((predOLS - yTest)^2)	
testMSELASSO = mean((predLASSO - yTest)^2)	
testMSERidge = mean((predRidge - yTest)^2)	
#' 	
#' 	
#' This plot shows a rank-ordered list of hte key predictors of the clinical outcome variable (total UPDRS, `y <- data1$UPDRS_part_I + data1$UPDRS_part_II + data1$UPDRS_part_III`).	
#' 	
#' 	
# Plot Regression Coefficients	
# create variable names for plotting 	
library("arm")	
par(mar=c(2, 13, 1, 1))   # extra large left margin	
varNames <- colnames(data1[ , !(names(data1) %in% drop_features)]); varNames; length(varNames)	
  	
# Graph 27 regression coefficients (exclude intercept [1], betaHat indices 2:27)	
coefplot(betaHatOLS[2:27], sd = rep(0, 26), cex.pts = 5, main = "Regression Coefficient Estimates", varnames = varNames)	
coefplot(betaHatLASSO[2:27], sd = rep(0, 26), add = TRUE, col.pts = "red", cex.pts = 5)	
coefplot(betaHatRidge[2:27], sd = rep(0, 26), add = TRUE, col.pts = "blue", cex.pts = 5)	
legend("bottomright", c("OLS", "LASSO", "Ridge"), col = c("black", "red", "blue"), pch = c(20, 20 , 20), bty = "o")	
# par()	
#' 	
#' 	
#' ## Model Performance	
#' We next quantify the performance of the model.	
#' 	
#' 	
# Test Set MSE Table 	
# create table as data frame	
MSETable = data.frame(OLS=testMSEOLS, OLS_step=testMSEOLS_step, LASSO=testMSELASSO, Ridge=testMSERidge)	
	
# convert to markdown	
kable(MSETable, format="pandoc", caption="Test Set MSE", align=c("c", "c", "c", "c"))	
#' 	
#' 	
#' ## Compare the selected variables 	
#' 	
#' 	
var_step = names(ols_step$coefficients)[-1]	
var_lasso = colnames(XTrain)[which(coef(fitLASSO, s = cvLASSO$lambda.min)!=0)-1]	
intersect(var_step,var_lasso)	
coef(fitLASSO, s = cvLASSO$lambda.min)	
#' 	
#' 	
#' Stepwise variable selection for OLS selects 12 variables, whereas LASSO selects 9 variables with the best $\lambda$. There are 6 common variables common for both OLS and LASSO.	
#' 	
#' ## Summary	
#' Traditional linear models are useful but also have their shortcomings:	
#' 	
#' * Prediction accuracy may be sub-optimal.	
#' * Model interpretability may be challenging (especially when a large number of features are used as regressors).	
#' * Stepwise model selection may improve the model performance and add some interpretations, but still may not be optimal.	
#' 	
#' Regularization adds a penalty term to the estimation:	
#' 	
#' *  Enables exploitation of the *bias-variance* tradeoff.	
#' *  Provides flexibility on specifying penalties to allow for continuous variable selection.	
#' *  Allows incorporation of prior knowledge.	
#'  	
#' # Knock-off Filtering: Simulated Example	
#' 	
#' Variable selection that controls the false discovery rate (FDR) of *salient features* can be accomplished in different ways. The [knockoff filtering](https://web.stanford.edu/~candes/Knockoffs/) represents one strategy for controlled variable selection.	
#' To show the usage of `knockoff.filter` we start with a synthetic dataset constructed so that the true coefficient vector $\beta$ has only a few nonzero entries.	
#' 	
#' The essence of the knockoff filtering is based on the following three-step process:	
#' 	
#' * Construct the decoy features (knockoff variables), one for each real observed feature. These act as controls for assessing the importance of the real variables.	
#' * For each feature, $X_i$, compute the knockoff statistic, $W_j$, which measures the importance of the variable, relative to its decoy counterpart, $\tilde{X}_i$.	
#' * Determine the overall knockoff threshold. This is computed by rank-ordering the $W_j$ statistics (from large to small), walking down the list of $W_j$'s, selecting variables $X_j$ corresponding to positive $W_j$'s, and terminating this search the last time the ratio of negative to positive $W_j$'s is below the default FDR $q$ value, e.g., $q=0.10$.	
#' 	
#' Mathematically, we consider $X_j$ to be *unimportant* (i.e., peripheral or extraneous) if the conditional distribution of $Y$ given $X_1,...,X_p$ does not depend on $X_j$. Formally, $X_j$ is unimportant if it is conditionally independent of $Y$ given all other features, $X_{-j}$:	
#' 	
#' $$ Y \perp X_j | X_{-j}.$$	
#' We want to generate a Markov Blanket of $Y$, such that the smallest set of features $J$ satisfies this condition. Further, to make sure we do not make too many mistakes, we search for a set $\hat{S}$ controlling the false discovery rate (FDR):	
#' 	
#' $$ FDR(\hat{S}) = \mathrm{E} \left (\frac{\#j\in \hat{S}:\ x_j\ unimportant}{\#j\in \hat{S}} \right) \leq q\ (e.g.\ 10\%).$$	
#' 	
#' Let's look at one simulation example.	
#' 	
#' 	
# Problem parameters	
n = 1000          # number of observations	
p = 300           # number of variables	
k = 30            # number of variables with nonzero coefficients	
amplitude = 3.5   # signal amplitude (for noise level = 1)	
	
# Problem data	
X = matrix(rnorm(n*p), nrow=n, ncol=p)	
nonzero = sample(p, k)	
beta = amplitude * (1:p %in% nonzero)	
y.sample <- function() X %*% beta + rnorm(n)	
#' 	
#' 	
#' To begin with, we will invoke the `knockoff.filter` using the default settings.	
#' 	
#' 	
# install.packages("knockoff")	
library(knockoff)	
y = y.sample()	
result = knockoff.filter(X, y)	
print(result)	
#' 	
#' 	
#' The false discovery proportion (fdp) is:	
#' 	
#' 	
fdp <- function(selected) sum(beta[selected] == 0) / max(1, length(selected))	
fdp(result$selected)	
#' 	
#' 	
#' This yields an approximate FDR of $0.10$.	
#' 	
#' The default settings of the knockoff filter uses a test statistic based on LASSO -- `knockoff.stat.lasso_signed_max`, which computes the $W_j$ statistics that quantify the discrepancy between a real ($X_j$) and a decoy, knockoff ($\tilde{X}_j$), feature:	
#' 	
#' $$W_j=\max(X_j, \tilde{X}_j) \times sgn(X_j - \tilde{X}_j). $$	
#' Effectively, the $W_j$ statistics measures how much more important the variable $X_j$ is relative to its decoy counterpart $\tilde{X}_j$. The strength of the importance of $X_j$ relative to $\tilde{X}_j$ is measured by the magnitude of $W_j$.	
#' 	
#' The `knockoff` package includes several other test statistics, with appropriate names prefixed by *knockoff.stat*. For instance, we can use a statistic based on forward selection ($fs$) and a lower target FDR of $0.10$.	
#' 	
#' 	
result = knockoff.filter(X, y, fdr = 0.10, statistic = knockoff.stat.fs)	
fdp(result$selected)	
#' 	
#' 	
#' One can also define additional test statistics, complementing the ones included in the package already. For instance, if we want to implement the following test-statistics:	
#' 	
#' $$W_j= || X^t_j . y|| - ||\tilde{X^t} . y||.$$	
#' 	
#' We can code it as:	
#' 	
#' 	
new_knockoff_stat <- function(X, X_ko, y) {	
  abs(t(X) %*% y) - abs(t(X_ko) %*% y)	
}	
result = knockoff.filter(X, y, statistic = new_knockoff_stat)	
fdp(result$selected)	
#' 	
#' 	
#' ## Notes	
#' The `knockoff.filter` function is a wrapper around several simpler functions that (1) construct knockoff variables (*knockoff.create*); (2) compute the test statistic $W$ (various functions with prefix *knockoff.stat*); and (3) compute the threshold for variable selection (*knockoff.threshold*).	
#' 	
#' The high-level function *knockoff.filter* will automatically `normalize the columns` of the input matrix (unless this behavior is explicitly disabled). However, all other functions in this *package assume that the columns of the input matrix have unitary Euclidean norm*.	
#' 	
#' ## PD Neuroimaging-genetics Case-Study	
#' 	
#' Let's illustrate controlled variable selection via knockoff filtering using the real PD dataset.	
#' 	
#' The goal is to determine which imaging, genetics and phenotypic covariates are associated with the clinical diagnosis of PD. The [dataset is publicly available online](https://umich.instructure.com/files/330397/download?download_frd=1)	
#' 	
#' ### Preparing the data	
#' 	
#' The data set consists of clinical, genetics, and demographic measurements. To evaluate our results, we will compare diagnostic predictions created by the model for the *UPDRS scores* and the *ResearchGroup* factor variable. 	
#' 	
#' ### Fetching and cleaning the data	
#' 	
#' First, we download the data and read it into data frames.	
#' 	
#' 	
data1 <- read.table('https://umich.instructure.com/files/330397/download?download_frd=1', sep=",", header=T)    	
# we will deal with missing values using multiple imputation later. For now, let's just ignore incomplete cases	
data1.completeRowIndexes <- complete.cases(data1) # table(data1.completeRowIndexes)	
prop.table(table(data1.completeRowIndexes))	
	
# attach(data1)	
# View(data1[data1.completeRowIndexes, ])	
data2 <- data1[data1.completeRowIndexes, ]	
Dx_label  <- data2$ResearchGroup; table(Dx_label)	
#' 	
#' 	
#' ### Preparing the design matrix	
#' 	
#' We now construct the design matrix $X$ and the response vector $Y$. The features (columns of $X$) represent covariates that will be used to explain the response $Y$.	
#' 	
#' 	
# Construct preliminary design matrix.	
# define response and predictors	
Y <- data1$UPDRS_part_I + data1$UPDRS_part_II + data1$UPDRS_part_III	
table(Y)   # Show Clinically relevant classification	
Y <- Y[data1.completeRowIndexes]	
	
# X = scale(ncaaData[, -20])  # Explicit Scaling is not needed, as glmnet auto standardizes predictors	
# X = as.matrix(data1[, c("R_caudate_Volume", "R_putamen_Volume", "Weight", "Age", "chr17_rs12185268_GT")])  # X needs to be a matrix, not a data frame	
drop_features <- c("FID_IID", "ResearchGroup", "UPDRS_part_I", "UPDRS_part_II", "UPDRS_part_III")	
X <- data1[ , !(names(data1) %in% drop_features)]	
X = as.matrix(X)   # remove columns: index, ResearchGroup, and y=(PDRS_part_I + UPDRS_part_II + UPDRS_part_III)	
X <- X[data1.completeRowIndexes, ]; dim(X)	
summary(X)	
mode(X) <- 'numeric'	
	
Dx_label <- Dx_label[data1.completeRowIndexes]; length(Dx_label)	
#' 	
#' 	
#' ### Preparing the response vector	
#' 	
#' The knockoff filter is designed to control the FDR under Gaussian noise. A quick inspection of the response vector shows that it is highly non-Gaussian.	
#' 	
#' 	
hist(Y, breaks='FD')	
#' 	
#' 	
#' A `log-transform` may help to stabilize the clinical response measurements:	
#' 	
#' 	
hist(log(Y), breaks='FD')	
#' 	
#' 	
#' For **binary outcome variables**, or **ordinal categorical variables**, we can employ the `logistic curve` to transform the polytomous outcomes into real values.	
#' 	
#' The Logistic curve is $y=f(x)=  \frac{1}{1+e^{-x}}$, 	
#' where y and x represent probability and quantitative-predictor values, respectively. A slightly more general form is: $y=f(x)=  \frac{K}{1+e^{-x}}$, where the covariate $x \in (-\infty, \infty)$ and the response $y \in [0, K]$. For example, 	
#' 	
#' 	
library("ggplot2")	
k=7	
x <- seq(-10, 10, 1)	
plot(x, k/(1+exp(-x)), xlab="X-axis (Covariate)", ylab="Outcome k/(1+exp(-x)), k=7", type="l")	
#' 	
#' 	
#' The point of this logistic transformation is that:	
#' $$y=  \frac{1}{1+e^{-x}} \Longleftrightarrow x=\ln\frac{y}{1-y},$$	
#' which represents the `log-odds` (when $y$ is the probability of an event of interest)!!!	
#' 	
#' We use the logistic regression equation model to estimate the probability of specific outcomes:	
#' 	
#' (Estimate of)$P(Y=1| x_1, x_2, ., x_l)=  \frac{1}{1+e^{-(a_o+\sum_{k=1}^l{a_k x_k })}}$, 	
#' where the coefficients $a_o$ (intercept) and effects $a_k, k = 1, 2, ..., l$, are estimated using GLM according to a maximum likelihood approach. Using this model allows us to estimate the probability of the dependent (clinical outcome) variable $Y=1$ (CO), i.e., surviving surgery, given the observed values of the predictors $X_k, k = 1, 2, ..., l$.	
#' 	
#' **Probability of surviving a heart transplant based on surgeon's experience**. A group of 20 patients undergo heart transplantation with different surgeons having experience in the range {0(least), 2, ..., 10(most)}, representing 100's of operating/surgery hours. How does the surgeon's experience affect the probability of the patient survival?	
#' 	
#' The data below shows the outcome of the surgery (1=survival) or (0=death) according to the surgeons' experience in 100's of hours of practice.	
#' 	
#' Surgeon's Experience (SE) | 1 | 1.5 | 2 | 2.5 | 3 | 3.5 | 3.5 | 4 | 4.5 | 5 | 5.5 | 6 | 6.5 | 7 | 8 | 8.5 | 9 | 9.5 | 10 | 10	
#' ----------------------|---|-----|---|-----|---|-----|-----|---|-----|---|-----|---|-----|---|---|-----|---|-----|----|---	
#' Clinical Outcome (CO) | 0 | 0   | 0 | 0   | 0 | 0   | 1   | 0 | 1   | 0 | 1   | 0 | 1   | 0 | 1 | 1   | 1 | 1   | 1  | 1	
#' 	
#' 	
mydata <- read.csv("https://umich.instructure.com/files/405273/download?download_frd=1")  # 01_HeartSurgerySurvivalData.csv	
# estimates a logistic regression model for the clinical outcome (CO), survival, using the glm 	
# (generalized linear model) function. 	
# convert Surgeon's Experience (SE) to a factor to indicate it should be treated as a categorical variable.	
# mydata$rank <- factor(mydata$SE)	
mylogit <- glm(CO ~ SE, data = mydata, family = "binomial")	
	
# library(ggplot2)	
ggplot(mydata, aes(x=SE, y=CO)) + geom_point() + 	
  	stat_smooth(method="glm", method.args=list(family = "binomial"), se=FALSE)	
#' 	
#' 	
#' Graph of a logistic regression curve showing probability of surviving the surgery versus surgeon's experience.	
#' 	
#' The graph shows the probability of the clinical outcome, survival, (Y-axis) versus the surgeon's experience (X-axis), with the logistic regression curve fitted to the data.	
#' 	
#' 	
mylogit <- glm(CO ~ SE, data = mydata, family = "binomial")	
summary(mylogit)	
#' 	
#' 	
#' The output indicates that surgeon's experience (SE) is significantly associated with the probability of surviving the surgery (0.0157, Wald test). The output also provides the coefficients for:	
#' 	
#' * Intercept = -4.1030 and  SE = 0.7583.	
#' 	
#' These coefficients can then be used in the logistic regression equation model to estimate the probability of surviving the heart surgery:	
#' 	
#' Probability of surviving heart surgery $CO =1/(1+exp(-(-4.1030+0.7583\times SE)))$	
#' 	
#' For example, for a patient who is operated by a surgeon with 200 hours of operating experience (SE=2), we plug in the value 2 in the equation to get an estimated probability of survival, $p=0.07$:	
#' 	
#' 	
SE=2	
CO =1/(1+exp(-(-4.1030+0.7583*SE)))	
CO	
#' 	
#' 	
#' [1] 0.07001884	
#' 	
#' Similarly, a patient undergoing heart surgery with a doctor that has 400 operating hours experience (SE=4), the estimated probability of survival is p=0.26:	
#' 	
#' 	
SE=4; CO =1/(1+exp(-(-4.1030+0.7583*SE))); CO	
CO	
	
for (SE in c(1:5)) {	
  CO <- 1/(1+exp(-(-4.1030+0.7583*SE))); 	
  print(c(SE, CO))	
}	
#' 	
#' 	
#' [1] 0.2554411	
#' 	
#' The table below shows the probability of surviving surgery for several values of surgeons' experience.	
#' 	
#' Surgeon's Experience (SE) | Probability of patient survival (Clinical Outcome)	
#' ----------------------|---------------------------------------------------	
#' 1                     | 0.034	
#' 2                     | 0.07	
#' 3                     | 0.14	
#' 4                     | 0.26	
#' 5                     | 0.423	
#' 	
#' The output from the logistic regression analysis gives a p-value of $p=0.0157$, which is based on the Wald z-score. In addition to the Wald method, we can calculate the p-value for logistic regression using the Likelihood Ratio Test (LRT), which for these data yields $0.0006476922$.	
#' 	
#' 	
mylogit <- glm(CO ~ SE, data = mydata, family = "binomial")	
summary(mylogit)	
#' 	
#' 	
#' .  | Estimate  | Std. Error  | z value | $Pr(\gt|z|)$ Wald 	
#' ---|-----------|-------------|---------|--------------	
#' SE | 0.7583    | 0.3139      | 2.416   | 0.0157 *	
#' 	
#' The *logit* of a number $0\leq p\leq 1$ is given by the formula: $logit(p)=log\frac{p}{1-p}$, and represents the log-odds ratio (of survival in this case).	
#' 	
#' 	
		confint(mylogit) 	
#' 	
#' 	
#' So, why `exponentiating the coefficients`? Because, 	
#' 	
#' $$logit(p)=log\frac{p}{1-p} \longrightarrow e^{logit(p)} =e^{log\frac{p}{1-p}}\longrightarrow RHS=\frac{p}{1-p}, \ 	
#' \text{(odds-ratio, OR)}.$$	
#' 	
#' 	
exp(coef(mylogit)) 	  # exponentiated logit model coefficients	
#' 	
#' 	
#' * (Intercept)          	SE 	
#' * 0.01652254  		2.13474149    == exp(0.7583456)	
#' * coef(mylogit)    	# raw logit model coefficients 	
#' * (Intercept)          	SE 	
#' * -4.1030298   		0.7583456	
#' 	
#' 	
exp(cbind(OR = coef(mylogit), confint(mylogit)))	
#' 	
#' 	
#'   .         | OR          | 2.5%          | 97.5%      	
#' ------------|-------------|---------------|---------	
#' (Intercept) | 0.01652254  | 0.0001825743  | 0.277290	
#' SE          | 2.13474149  | 1.3083794719  | 4.839986	
#' 	
#' We can compute the LRT and report its p-values by using the *with()* function:	
#' 	
#' `with(mylogit, df.null - df.residual)`	
#' 	
#' 	
with(mylogit, pchisq(null.deviance - deviance, df.null - df.residual, lower.tail = FALSE))	
#' 	
#' 	
#' [1] 0.0006476922	
#' 	
#' LRT p-value < 0.001 tells us that our model as a whole fits significantly better than an empty model. The deviance residual is `-2*log likelihood`, and we can report the model's log likelihood by:	
#' 	
#' 	
logLik(mylogit)	
#' 	
#' 	
#' $$\text{log Lik.}= -8.046117\ (df=2).$$	
#' 	
#' ## Note	
#' The LRT compares the data fit of two models. For instance, removing predictor variables from a model may reduce the model quality (i.e., a model will have a lower log likelihood). To statistically assess whether the observed difference in model fit is significant, the LRT compares the difference of the log likelihoods of the two models. When this difference is statistically significant, the full model (the one with more variables) represents a better fit to the data, compared to the reduced model. LRT is computed using the log likelihoods ($ll$) of the two models:	
#' 	
#' $$LRT = -2 \ln\left (\frac{L(m_1)}{L(m_2)}\right )  = 2(ll(m_2)-ll(m_1)), $$	
#' where:	
#' 	
#' 	
#' * $m_1$ and $m_2$ are the reduced and the full models, respectively,	
#' * $L(m_1)$ and $L(m_2)$ denote the likelihoods of the 2 models, and	
#' * $ll(m_1)$ and $ll(m_2)$ represent the *log likelihood* (natural log of the model likelihood function).	
#' 	
#' As $n\longrightarrow \infty$, the distribution of the LRT is asymptotically chi-squared with degrees of freedom equal to the number of parameters that are reduced (i.e., the number of variables removed from the model). In our case, $LRT \sim \chi_{df=2}^2$, as we have an intercept and one predictor (SE), and the null model is empty (no parameters).	
#' 	
#' ### False Discovery Rate (FDR)	
#' A measure of performance of a test is the FDR rate:	
#' 	
#' $$\underbrace{FDR}_{\text{False Discovery Rate}} =\underbrace{E}_{\text{expectation}} \underbrace{\left( \frac{\# False Positives}{\text{total number of selected features}}\right )}_{\text{False Discovery Proportion}}.$$	
#' 	
#' The Benjamini-Hochberg (BH) FDR procedure involves ordering the p-values, specifying a target FDR, calculating and applying the threshold. Below we show how this is accomplished in R.	
#' 	
#' 	
#p-values entered from smallest to largest	
pvals <- c(0.9, 0.35, 0.01, 0.013, 0.014, 0.19, 0.35, 0.5, 0.63, 0.67, 0.75, 0.81, 0.01, 0.051)	
length(pvals)	
#enter the target FDR	
alpha.star <- 0.05	
	
# order the p-values small to large	
pvals <- sort(pvals); pvals	
	
#calculate the threshold for each p-value	
threshold<-alpha.star*(1:length(pvals))/length(pvals)	
	
#compare the p-value against its threshold and display results	
cbind(pvals, threshold, pvals<=threshold)	
#' 	
#' 	
#' Start with the smallest p-value and move up we find that the largest $k$ for which the p-value is less than its threshold, $\alpha^*$, which is $\hat{k}=4$.	
#' 	
#' Next, the algorithm rejects the null hypotheses for the tests that correspond to the p-values $p_{(1)}, p_{(2)}, p_{(3)}, p_{(4)}$.	
#' 	
#' Note: that since we controlled FDR at $\alpha^*=0.05$, we are guaranteed that on average only 5% of the tests that we rejected are spurious. Since $\alpha^*=0.05$ of 4 is quite small and less than 1, we are confident that none of our rejections are spurious ones.	
#' 	
#' The Bonferroni corrected $\alpha$ for these data is $\frac{0.05}{14} = 0.0036$. If we had used this family-wise error rate in our individual hypothesis tests, then we would have concluded that none of our $14$ results were significant!	
#' 	
#' ### Graphical Interpretation of the Benjamini-Hochberg (BH) Method	
#' 	
#' There's a graphical interpretation of the BH calculations. 	
#' 	
#' * Sort the p-values from largest to smallest.	
#' * Plot the ordered p-values $p_{(k)}$ on the y-axis versus their indexes on the x-axis.	
#' * Superimpose on this plot a line that passes through the origin and has slope $\alpha^*$.	
#' 	
#' Any p-value that falls on or below this line corresponds to a significant result.	
#' 	
#' 	
#generate the values to be plotted on x-axis	
x.values<-(1:length(pvals))/length(pvals)	
	
#widen right margin to make room for labels	
par(mar=c(4.1, 4.1, 1.1, 4.1))	
	
#plot points	
plot(x.values, pvals, xlab=expression(k/m), ylab="p-value") #, ylim=c(0.0, 0.4))	
    	
#add FDR line	
abline(0, .05, col=2, lwd=2)	
    	
#add naive threshold line	
abline(h=.05, col=4, lty=2)	
    	
#add Bonferroni-corrected threshold line	
abline(h=.05/length(pvals), col=4, lty=2)	
    	
#label lines	
mtext(c('naive', 'Bonferroni'), side=4, at=c(.05, .05/length(pvals)), las=1, line=0.2)	
    	
#select observations that are less than threshold	
for.test <- cbind(1:length(pvals), pvals)	
pass.test <- for.test[pvals <= 0.05*x.values, ]	
pass.test	
	
#use largest k to color points that meet Benjamini-Hochberg FDR test	
last<-ifelse(is.vector(pass.test), pass.test[1], pass.test[nrow(pass.test), 1])	
points(x.values[1:last], pvals[1:last], pch=19, cex=1.5)	
#' 	
#' 	
#' ### FDR Adjusting the p-values	
#' 	
#' R can automatically performs the Benjamini-Hochberg procedure. The adjusted p-values are obtained by	
#' 	
#' 	
pvals.adjusted <- p.adjust(pvals, "BH")	
#' 	
#' 	
#' The adjusted p-values indicate the corresponding null hypothesis we need to reject to preserve the initial $\alpha^*$ false-positive rate. We can also compute the adjusted p-values as follows:	
#' 	
#' 	
#calculate the term that appears in the innermost minimum function	
test.p <- length(pvals)/(1:length(pvals))*pvals	
test.p	
	
#use a loop to run through each p-value and carry out the adjustment	
adj.p <- numeric(14)	
for(i in 1:14) {	
    adj.p[i]<-min(test.p[i:length(test.p)])	
    ifelse(adj.p[i]>1, 1, adj.p[i])	
}	
adj.p	
#' 	
#' 	
#' Note that the manually computed (`adj.p`) and the automatically computed (`pvals.adjusted`) adjusted-p-values are the same.	
#' 	
#' ## Running the knockoff filter	
#' 	
#' We now run the knockoff filter on each drug separately. We also run the Benjamini-Hochberg (BH) procedure for later comparison. More [details about the Knock-off filtering methods are available here](https://www.stat.uchicago.edu/~rina/knockoff/knockoff_slides.pdf).	
#' 	
#' Before running either selection procedure, remove rows with missing values, reduce the design matrix by removing predictor columns that do not appear frequently (e.g., at least three times in the sample), and remove any columns that are duplicates.	
#' 	
#' 	
library(knockoff)	
	
# Direct call to knockoff filtering looks like this:	
fdr <- 0.1	
result = knockoff.filter(X, Y, fdr = fdr, knockoffs = 'equicorrelated'); names(result$selected)	
knockoff_selected <- names(result$selected)	
  	
# Run BH (Benjamini-Hochberg)	
k = ncol(X)	
lm.fit = lm(Y ~ X - 1) # no intercept	
# Alternatively: dat = as.data.frame(cbind(Y,X))	
# lm.fit = lm(Y ~ . -1,data=dat ) # no intercept	
	
p.values = coef(summary(lm.fit))[, 4]	
cutoff = max(c(0, which(sort(p.values) <= fdr * (1:k) / k)))	
BH_selected = names(which(p.values <= fdr * cutoff / k))	
	
knockoff_selected; BH_selected	
	
# Housekeeping: remove the "X" prefixes in the BH_selected list of features	
for(i in 1:length(BH_selected)){	
  BH_selected[i] <- substring(BH_selected[i], 2)	
}	
	
intersect(BH_selected,knockoff_selected)	
#' 	
#' 	
#' We see that there are some features that are selected by both methods suggesting they may be indeed salient.	
#' 	
#' <!---	
#' # See an HIV Example: https://cran.r-project.org/web/packages/knockoff/vignettes/hiv.html	
#' # Indirect call to knockoff looks like this:	
#' # knockoff_and_BH <- function (X, y, fdr) {	
#' #   # Log-transform the drug resistance measurements.	
#' #   y = log(y)	
#' #   dim(X)	
#' #   length(y)	
#' #   	
#' #   # Remove patients with missing measurements.	
#' #   #missing = is.na(y)	
#' #   #y = y[!missing]	
#' #   #X = X[!missing, ]	
#' #     	
#' #   # Remove predictors that appear less than 3 times.	
#' #   #X = X[, colSums(X) >= 3]	
#' #   	
#' #   # Remove duplicate predictors.	
#' #   #X = X[, colSums(abs(cor(X)-1) < 1e-4) == 1]	
#' #   	
#' #   # Run the knockoff filter.	
#' #   result = knockoff.filter(X, y, fdr = fdr, knockoffs = 'equicorrelated')	
#' #   knockoff_selected = names(result$selected)	
#' #   	
#' #   # Run BH.	
#' #   #k = ncol(X)	
#' #   #lm.fit = lm(y ~ X - 1) # no intercept	
#' #   #p.values = coef(summary(lm.fit))[, 4]	
#' #   #cutoff = max(c(0, which(sort(p.values) <= fdr * (1:k) / k)))	
#' #   #bhq_selected = names(which(p.values <= fdr * cutoff / k))	
#' #   	
#' #   #list(Knockoff = knockoff_selected, BHq = bhq_selected)	
#' # }	
#' # 	
#' # fdr <- 0.20	
#' # results = lapply(Y, function(y) knockoff_and_BH(X, y, fdr))	
#' 	
#' ## Evaluating the results	
#' 	
#' In this case, we have a "ground truth" (`ResearchGroup` or table(Dx_label)), which can be used to assess the results from the knockoff and BH procedures.	
#' 	
#' 	
# positions = unique(get_position(selected)) # remove possible duplicates	
# discoveries = length(positions)	
# 	
# false_discoveries = length(setdiff(positions, tsm_df$Position))	
# list(true_discoveries = discoveries - false_discoveries, 	
#          false_discoveries = false_discoveries, 	
#          fdp = false_discoveries / max(1, discoveries))	
#         print(comparisons[1])	
#' 	
#' 	
#' ## Visualization	
#' Visually compare the results  by plotting them.	
#' 	
#' 	
# for (drug in names(comparisons)) {	
#   plot_data = do.call(cbind, comparisons[[drug]])	
#   plot_data = plot_data[c('true_discoveries', 'false_discoveries'), ]	
#   barplot(as.matrix(plot_data), main = paste('Resistance to', drug), 	
#           col = c('navy', 'orange'), ylim = c(0, 40))	
# }	
#' 	
#' 	
#' ## One more example.	
#' 	
#' See an [HIV Example](https://cran.r-project.org/web/packages/knockoff/vignettes/hiv.html), including knockoff filter, BH method, evaluation and visualization.	
#' 	
#' ### Bootstrap knockoff and LASSO	
#' Further, we can apply Bootstrap to select variables. The major advantage of Bootstrap is to get variable importance table and various statistics. Notice that when we use Bootstrap, we assume that the data follows the same structure.	
#' 	
#' First, let's try to use Bootstrap LASSO. In each iteration, we randomly select half of data as sample and tune on these data to get the best $\lambda$. Then, record those non-zero variables. Finally, we output the top 10 important variables and their frequency. 	
#' 	
#' 	
set.seed(15)	
mat = matrix(0,ncol = 27)	
N = 400	
sca.x = as.matrix(X)	
for (i in 1:N){	
  #if (i%%50==0){	
  #print(i)	
  #}	
  tp = sample(1:nrow(X),0.5*nrow(X))	
  x.tp = sca.x[tp,]	
  y.tp = Y[tp]	
  cvtp = cv.glmnet(x.tp, y.tp, alpha = 1)	
  tt <- as.matrix(coef(cvtp, s = "lambda.min"))	
  mat[which(tt!=0)]=mat[which(tt!=0)]+1	
}	
	
df <- as.data.frame(t(mat))	
df$id <- 1:27	
varname = colnames(X)[1:26]	
df$names <- c("intercept",varname)	
ddf <- df[order(df$V1,decreasing = T),]	
ddf$fre = ddf$V1/N	
ddf[2:11,c(3,4)] # 1 for intercept	
#' 	
#' 	
#' Similarly, we can perform to knockoff. As we use a somehow strict `fdr`, we need more iterations.	
#' 	
#' 	
set.seed(15)	
N=1000	
num = matrix(0,ncol = 27)	
for (i in 1:N){	
  #if (i%%50==0){	
  # print(i)	
  #}	
  tp = sample(1:nrow(X),0.5*nrow(X))	
  x.tp = sca.x[tp,]	
  y.tp = Y[tp]	
  tt <- knockoff.filter(X = x.tp,fdr=0.2,y = y.tp,randomize = FALSE)	
  tt <- as.matrix(tt$selected)	
  num[tt]=num[tt]+1	
}	
df2 <- as.data.frame(t(num))	
df2$id <- 1:27	
df2$names <- c("intercept",varname)	
ddf2 <- df2[order(df2$V1,decreasing = T),]	
ddf2$fre = ddf2$V1/N	
ddf2[1:10,c(3,4)]	
#' 	
#' 	
#' Now, we can calculate any statistics based on the sampling result.	
#' 	
#' Finally, you can compare the sampling result of LASSO and knockoff, and compare the result on sampling and on whole data. Do these results make sense?	
#' --->	
#' 	
#' Try to apply some of these techniques to [other data from the list of our Case-Studies](https://umich.instructure.com/courses/38100/files/).