boot.glmnet: Calculate confidence intervals for lasso using bootstrap

In pbreheny/hdrm: High-Dimensional Regression Modeling

Calculate confidence intervals for lasso using bootstrap

Description

Calculate confidence intervals for lasso using bootstrap

Usage

boot.glmnet(X, y, B = 500, lambda, seed, alpha = 0.05, bar = TRUE)

Arguments

X	Design matrix
y	Response vector
B	Number of bootstrap resamples (default 500)
lambda	Regularization parameter at which solutions are to be bootstrapped (by default, uses cross-validation to find lambda)
seed	Seed (optional)
alpha	Error rate; 0.05 (default) corresponds to 95% confidence interval
bar	Print a progress bar?

Examples

Data <- genDataABN(n=100, p=20, a=2, b=3)
CI <- boot.glmnet(Data$X, Data$y)
covered <- Data$beta >= CI[,1] & Data$beta <= CI[,2]
table(covered)
CI <- boot.glmnet(Data$X, Data$y, lambda=0.65)
covered <- Data$beta >= CI[,1] & Data$beta <= CI[,2]
table(covered)

pbreheny/hdrm documentation built on May 24, 2024, 9:30 p.m.