lasso.cv: Cross validation optimizer for lasso

In lol: Lots Of Lasso

Description

Cross validation lasso. This function optimizes the lasso solution for correlated regulators by an algorithm. this algorithm chooses the minimum lambda since the penalized package by default use 0 for the minimum, which sometimes take a long time to compute

Usage

lasso.cv(y, x=NULL, lambda1=NULL, model='linear', steps=15, minsteps=5, log=TRUE, track=FALSE, standardize= FALSE, unpenalized=~0, nFold=10, nMaxiter = Inf, ...)

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
lambda1	minimum lambda to use
model	which model to use, one of "cox", "logistic", "linear", or "poisson". Default to 'linear'
steps	parameter to be passed to penalized
minsteps	parameter to be passed to penalized
log	parameter to be passed to penalized
track	parameter to be passed to penalized
standardize	parameter to be passed to penalized
unpenalized	parameter to be passed to penalized
nFold	parameter to be passed to penalized
nMaxiter	parameter to be passed to penalized
...	other parameter to be passed to penalized

Value

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

fit	The final sparse regression fit
beta	the coefficients, non-zero ones are significant
lambda	the penalty parameter lambda used
residuals	regression residuals
conv	logical value indicating whether the optimization has converged

Author(s)

Yinyin Yuan

References

Goeman, J. J. (2009), L1 penalized estimation in the cox proportional hazards model, Biometrical Journal.

See Also

lasso

Examples

data(chin07)
data <- list(y=chin07$ge[1,], x=t(chin07$cn), nFold=5)
res <- lasso.cv(data)
res

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