| stat.lasso_lambdadiff {knockoff} | R Documentation |
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 \lambda 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 |
Details
This function uses glmnet to compute the lasso path
on a fine grid of \lambda'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 \lambda'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)