lasso.rand: Randomized Lasso

In cbg-ethz/gespeR: Gene-Specific Phenotype EstimatoR

Description

Based on Meinshausen and Buehlmann (2009)

Usage

lasso.rand(x, y, weakness = 1, subsample = 1:nrow(x), dfmax = (ncol(x) +
  1), lambda = NULL, standardize = FALSE, intercept = FALSE, ...)

Arguments

x	The design matrix
y	The response vector
weakness	The weakness parameter
subsample	The data subsample (default: none)
dfmax	The maxiumum number of degrees of freedom
lambda	The regularisation parameter
standardize	Indicator, wheter to standardize the design matrix
intercept	Indicator, whether to fit an intercept
...	Additional arguments to glmnet

Value

A glmnet object

Author(s)

Fabian Schmich

Examples

y <- rnorm(50)
 x <- matrix(runif(50 * 20), ncol = 20)
 lasso.rand(x = x, y = y)

cbg-ethz/gespeR documentation built on May 13, 2019, 2:02 p.m.