lol (version 1.20.0)

lasso.cv: Cross validation optimizer for 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

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