# stat.lasso_lambdasmax: Penalized linear regression statistics for knockoff In knockoff: The Knockoff Filter for Controlled Variable Selection

 stat.lasso_lambdasmax R Documentation

## Penalized linear regression statistics for knockoff

### Description

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 linear regression model.

### Usage

stat.lasso_lambdasmax(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 or lars (see Details).

### 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.

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

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

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

### Value

A vector of statistics W of length p.

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

# Basic usage with default arguments
result = knockoff.filter(X, y, knockoff=knockoffs,
statistic=stat.lasso_lambdasmax)
print(result$selected) # Advanced usage with custom arguments foo = stat.lasso_lambdasmax k_stat = function(X, X_k, y) foo(X, X_k, y, nlambda=200) result = knockoff.filter(X, y, knockoffs=knockoffs, statistic=k_stat) print(result$selected)



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