# R/stats_lasso_bin.R In knockoff: The Knockoff Filter for Controlled Variable Selection

#' Importance statistics based on regularized logistic regression
#'
#' Fit the lasso path and computes the difference statistic
#'   \deqn{W_j = Z_j - \tilde{Z}_j}
#' where \eqn{Z_j} and \eqn{\tilde{Z}_j} are the maximum values of the
#' regularization parameter \eqn{\lambda} at which the jth variable
#' and its knockoff enter the penalized logistic regression model, respectively.
#'
#' @param X n-by-p matrix of original variables.
#' @param X_k n-by-p matrix of knockoff variables.
#' @param y vector of length n, containing the response variables. It should be either a factor with two levels,
#' or a two-column matrix of counts or proportions
#' (the second column is treated as the target class; for a factor, the last level
#' in alphabetical order is the target class). If y is presented as a vector,
#' it will be coerced into a factor.
#' @param ... additional arguments specific to \code{glmnet} (see Details).
#' @return A vector of statistics \eqn{W} of length p.
#'
#' @details This function uses \code{glmnet} to compute the lasso path
#' on a fine grid of \eqn{\lambda}'s.
#'
#' The \code{nlambda} parameter can be used to control the granularity of the
#' grid of \eqn{\lambda}'s. The default value of \code{nlambda} is \code{500}.
#'
#'
#' For a complete list of the available additional arguments, see \code{\link[glmnet]{glmnet}}
#'
#' @family statistics
#'
#' @examples
#' p=200; n=100; k=15
#' mu = rep(0,p); Sigma = diag(p)
#' X = matrix(rnorm(n*p),n)
#' nonzero = sample(p, k)
#' beta = 3.5 * (1:p %in% nonzero)
#' pr = 1/(1+exp(-X %*% beta))
#' y = rbinom(n,1,pr)
#' knockoffs = function(X) create.gaussian(X, mu, Sigma)
#'
#' # Basic usage with default arguments
#' result = knockoff.filter(X, y, knockoffs=knockoffs,
#' print(result$selected) #' #' # Advanced usage with custom arguments #' foo = stat.lasso_lambdadiff_bin #' k_stat = function(X, X_k, y) foo(X, X_k, y, nlambda=200) #' result = knockoff.filter(X, y, knockoffs=knockoffs, statistic=k_stat) #' print(result$selected)
#'
#' @export
stat.lasso_lambdadiff_bin <- function(X, X_k, y, ...) {
}

#' Penalized logistic regression statistics for knockoff
#'
#' Computes the signed maximum statistic
#'   \deqn{W_j = \max(Z_j, \tilde{Z}_j) \cdot \mathrm{sgn}(Z_j - \tilde{Z}_j),}
#' where \eqn{Z_j} and \eqn{\tilde{Z}_j} are the maximum values of
#' \eqn{\lambda} at which the jth variable and its knockoff, respectively,
#' enter the penalized logistic regression model.
#'
#' @param X n-by-p matrix of original variables.
#' @param X_k n-by-p matrix of knockoff variables.
#' @param y vector of length n, containing the response variables. It should be either a factor with two levels,
#' or a two-column matrix of counts or proportions
#' (the second column is treated as the target class; for a factor, the last level
#' in alphabetical order is the target class). If y is presented as a vector,
#' it will be coerced into a factor.
#' @param ... additional arguments specific to \code{glmnet} or \code{lars} (see Details).
#' @return A vector of statistics \eqn{W} of length p.
#'
#' @details This function uses \code{glmnet} to compute the regularization path
#' on a fine grid of \eqn{\lambda}'s.
#'
#' parameter can be used to control the granularity of the grid of \eqn{\lambda} values.
#' The default value of \code{nlambda} is \code{500}.
#'
#' This function is a wrapper around the more general
#'
#' For a complete list of the available additional arguments, see \code{\link[glmnet]{glmnet}}.
#'
#' @examples
#' p=200; n=100; k=15
#' mu = rep(0,p); Sigma = diag(p)
#' X = matrix(rnorm(n*p),n)
#' nonzero = sample(p, k)
#' beta = 3.5 * (1:p %in% nonzero)
#' pr = 1/(1+exp(-X %*% beta))
#' y = rbinom(n,1,pr)
#' knockoffs = function(X) create.gaussian(X, mu, Sigma)
#'
#' # Basic usage with default arguments
#' result = knockoff.filter(X, y, knockoff=knockoffs,
#'                            statistic=stat.lasso_lambdasmax_bin)
#' print(result$selected) #' #' # Advanced usage with custom arguments #' foo = stat.lasso_lambdasmax_bin #' k_stat = function(X, X_k, y) foo(X, X_k, y, nlambda=200) #' result = knockoff.filter(X, y, knockoffs=knockoffs, statistic=k_stat) #' print(result$selected)
#'
#' @rdname stat.lasso_lambdasmax_bin
#' @export
stat.lasso_lambdasmax_bin <- function(X, X_k, y, ...) {
stat.glmnet_lambdasmax(X, X_k, y, family='binomial', ...)
}


## Try the knockoff package in your browser

Any scripts or data that you put into this service are public.

knockoff documentation built on July 2, 2020, 12:02 a.m.