stat.lasso_lambdadiff: Importance statistics based on the lasso

In knockoff: The Knockoff Filter for Controlled Variable Selection

Importance statistics based on the lasso

Description

Fit the lasso path and computes the difference statistic

W_j = Z_j - \tilde{Z}_j

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

Usage

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

Arguments

X	n-by-p matrix of original variables.
X_k	n-by-p matrix of knockoff variables.
y	vector of length n, containing the response variables. It should be numeric.
...	additional arguments specific to glmnet (see Details).

Details

This function uses glmnet to compute the lasso path on a fine grid of λ's and is a wrapper around the more general stat.glmnet_lambdadiff.

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

Unless a lambda sequence is provided by the user, this function generates it on a log-linear scale before calling glmnet (default 'nlambda': 500).

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

Value

A vector of statistics W of length p.

See Also

Other statistics: stat.forward_selection(), stat.glmnet_coefdiff(), stat.glmnet_lambdadiff(), stat.lasso_coefdiff_bin(), stat.lasso_coefdiff(), stat.lasso_lambdadiff_bin(), stat.random_forest(), stat.sqrt_lasso(), stat.stability_selection()

Examples

set.seed(2022)
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)
y = X %*% beta + rnorm(n)
knockoffs = function(X) create.gaussian(X, mu, Sigma)

# Basic usage with default arguments
result = knockoff.filter(X, y, knockoffs=knockoffs, 
                           statistic=stat.lasso_lambdadiff)
print(result$selected)

# Advanced usage with custom arguments
foo = stat.lasso_lambdadiff
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)

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