highdim.p {HCTR}

p-values in high-dimensional linear model

Description

Calculates p-values in high-dimentional linear models using multi-split method

Usage

highdim.p(Y, X, W = NULL, type, B = 100, fold.num)

Arguments

Y

A numeric response vector, containing nobs variables.

X

An input matrix, of dimension nobs x nvars.

W

A covariate matrix, of dimension nobs x ncors, default is NULL.

type

Penalized regression type, valid parameters include "Lasso", "AdaLasso", "SCAD", and "MCP".

B

Multi-split times, default is 100.

fold.num

The number of cross validation folds.

Value

A list of objects containing: (1) harmonic mean p-values; (2) original p-values; (3) index of selected samples; (4) index of selected variables

Examples

set.seed(10)
X <- matrix(rnorm(20000), nrow = 100)
beta <- rep(0, 200)
beta[1:100] <- 5
Y <- MASS::mvrnorm(n = 1, mu = X%*%beta, Sigma = diag(100))
result <- highdim.p(Y=Y, X=X, type = "Lasso", B = 2, fold.num = 10)