stat.lasso_lambdasmax_bin: Penalized logistic regression statistics for knockoff

View source: R/stats_lasso_bin.R

stat.lasso_lambdasmax_binR Documentation

Penalized logistic regression statistics for knockoff


Computes the signed maximum statistic

W_j = \max(Z_j, \tilde{Z}_j) \cdot \mathrm{sgn}(Z_j - \tilde{Z}_j),

where Z_j and \tilde{Z}_j are the maximum values of λ at which the jth variable and its knockoff, respectively, enter the penalized logistic regression model.


stat.lasso_lambdasmax_bin(X, X_k, y, ...)



n-by-p matrix of original variables.


n-by-p matrix of knockoff variables.


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.


additional arguments specific to glmnet or lars (see Details).


This function uses glmnet to compute the regularization path on a fine grid of λ's.

The additional nlambda parameter can be used to control the granularity of the grid of λ values. The default value of nlambda is 500.

This function is a wrapper around the more general stat.glmnet_lambdadiff.

For a complete list of the available additional arguments, see glmnet.


A vector of statistics W of length p.


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,

# 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)

knockoff documentation built on Aug. 15, 2022, 9:06 a.m.