multisplit: Variable Selection on Random Sample Splits.

In hierGWAS: Asessing statistical significance in predictive GWA studies

Description

Performs repeated variable selection via the lasso on random sample splits.

Usage

multisplit(x, y, covar = NULL, B = 50)

Arguments

x	The SNP data matrix, of size nobs x nvar. Each row represents a subject, each column a SNP.
y	The response vector. It can be continuous or discrete.
covar	NULL or the matrix of covariates one wishes to control for, of size nobs x ncovar.
B	The number of random splits. Default value is 50.

Details

The samples are divided into two random splits of approximately equal size. The first subsample is used for variable selection, which is implemented using glmnet. The first [nobs/6] variables which enter the lasso path are selected. The procedure is repeated B times.

If one or more covariates are specified, these will be added unpenalized to the regression.

Value

A data frame with 2 components. A matrix of size B x [nobs/2] containing the second subsample of each split, and a matrix of size B x [nobs/6] containing the selected variables in each split.

References

Meinshausen, N., Meier, L. and Buhlmann, P. (2009), P-values for high-dimensional regression, Journal of the American Statistical Association 104, 1671-1681.

Examples

library(MASS)
x <- mvrnorm(60,mu = rep(0,200), Sigma = diag(200))
beta <- rep(1,200)
beta[c(5,9,3)] <- 3
y <- x %*% beta + rnorm(60)
res.multisplit <- multisplit(x, y)

hierGWAS documentation built on Nov. 8, 2020, 8:05 p.m.