lasso.multiSplit: Multi-split lasso

In lol: Lots Of Lasso

Description

Multi-split lasso as described in Meinshausen 2009

Usage

lasso.multiSplit(y, x=NULL, lambda1=NULL, nSubsampling=200, model='linear', alpha=0.05, gamma.min=0.05, gamma.max=0.95, track=FALSE, ...)

Arguments

y	A vector of gene expression of a probe, or a list object if x is NULL. In the latter case y should a list of two components y and x, y is a vector of expression and x is a matrix containing copy number variables
x	Either a matrix containing CN variables or NULL
nSubsampling	number of splits, default to 200
model	which model to use, one of "cox", "logistic", "linear", or "poisson". Default to 'linear'
alpha	specify significant level to determine the non-zero coefficients in the range of 0 and 1, default to 0.05
gamma.min	the lower bound of gamma
gamma.max	the higher bound of gamma
lambda1	minimum lambda to be used, if known
track	track progress
...	other parameters to be passed to lass.cv

Details

This function performs the multi-split lasso as proposed by Meinshausen et al. 2009. The samples are first randomly split into two disjoint sets, one of which is used to find non-zero coefficients with a regular lasso regression, then these non-zero coefficients are fitted to another sample set with OLS. The resulting p-values after multiple runs can then be aggregated using quantiles.

Value

A list object of class 'lol', consisting of:

beta	coefficients
mat	the Q_gamma matrix as described in the paper
residuals	residuals, here is only the input y
pmat	the adjusted p matrix as described in the paper

Author(s)

Yinyin Yuan

References

Nicolai Meinshausen, Lukas Meier and Peter Buehlmann (2009), P-values for high-dimensional regression. Journal of the American Statistical Association, 104, 1671-1681.

See Also

lasso

Examples

data(chin07)
data <- list(y=chin07$ge[1,], x=t(chin07$cn))
res <- lasso.multiSplit(data, nSubsampling=50)
res

Example output

Loading required package: penalized
Loading required package: survival
Welcome to penalized. For extended examples, see vignette("penalized").
Loading required package: Matrix
 Non-zero coefficients: 1
 from a total of 339 predictors

lol documentation built on Oct. 31, 2019, 2:21 a.m.