lasso.stability: Stability and randomised lasso
In lol: Lots Of Lasso

Description Usage Arguments Details Value Author(s) References See Also Examples

point-wise controled lasso stability selection

1	lasso.stability(y, x=NULL, alpha=.5, subsampling=.5, nSubsampling=200, model='linear', pi_th=.6, alpha.fwer=1, lambda1=NULL, steps=10, track=FALSE, standardize=FALSE, ...)

`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
`alpha`	weakness parameter: control the shrinkage of regulators, if alpha = 1 then no randomisation, if NULL then a randomly generated vector is used
`subsampling`	fraction of samples to use in the sampling process, default to 0.5
`nSubsampling`	The number of subsampling to do, default to 200
`model`	which model to use, one of "cox", "logistic", "linear", or "poisson". Default to 'linear'
`pi_th`	The threshold of the stability probablity for selecting a regulator. It is to determine whether a coefficient is non-zero based on the frequency it is subsampled to be non-zero, default to 0.6
`alpha.fwer`	Parameter to control for the FWER, choosing alpha.fwer and alpha control the E(V), V being the number of noise variables, eg. when alpha=0.9, alpha.fwer = 1 control the E(V)<=1
`lambda1`	minimum lambda to use
`steps`	parameter to be passed on to penalized
`track`	track the progress, 0 none tracking, 1 minimum amount of information and 2 full information
`standardize`	standardize the data or not?
`...`

The function first selects lambda that approximately give maximum sqrt(.8*p) predictors, while p is the number of total predictors. Then it runs lasso a number of times keeping lambda fixed. These runs are randomised with scaled predictors and subsamples. At the end, the non-zero coefficients are determined by their frequencies of selections.

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

`beta`	coefficients
`beta.bin`	binary beta vector as thresholded by pi_th
`mat`	the sampling matrix, each column is the result of one sampling
`residuals`	residuals of regression model

Yinyin Yuan

N. Meinshausen and P. Buehlmann (2010), Stability Selection (with discussion), Journal of the Royal Statistical Society, Series B, 72, 417-473.

lasso

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

Loading required package: penalized
Loading required package: survival
Welcome to penalized. For extended examples, see vignette("penalized").
Loading required package: Matrix
 Non-zero coefficients: 0
 from a total of 339 predictors