c060 (version 0.3-0)

stabpath: Stability path for glmnet models

Description

The function calculates the stability path for glmnet models, e.g. the selection probabilities of the features along the range of regularization parameters.

Usage

stabpath(y,x,size=0.632,steps=100,weakness=1,mc.cores=getOption("mc.cores", 2L),...)

Value

an object of class "stabpath", which is a list of three objects

fit

the fit object of class "glmnet" as returned from the glmnet function when applied to the complete data set.

stabpath

a matrix which represents the stability path.

qs

a vector holding the values of the average number of non-zero coefficients w.r.t to the lambdas in the regularization path.

Arguments

y

response variable. Like for the glment function: Quantitative for family="gaussian" or family="poisson" (non-negative counts). For family="binomial" should be either a factor with two levels, or a two-column matrix of counts or proportions. For family="multinomial", can be a nc>=2 level factor, or a matrix with nc columns of counts or proportions. For family="cox", y should be a two-column matrix with columns named 'time' and 'status'. The latter is a binary variable, with '1' indicating death, and '0' indicating right censored. The function Surv() in package survival produces such a matrix

x

input matrix. Like for the glmnet function: of dimension nobs x nvars; each row is an observation vector. Can be in sparse matrix format (inherit from class "sparseMatrix" as in package Matrix; not yet available for family="cox")

size

proportion of samples drawn in every subsample used for the stability selection.

steps

number of subsamples used for the stability selection.

weakness

weakness parameter used for the randomised lasso as described in Meinshausen and B\"uhlmann (2010). For each subsample the features are reweighted by a random weight uniformly sampled in [weakness,1]. This additional randomisation leads to a more consistent estimation of the stable set of features.

mc.cores

number of cores used for the parallelization. For unix like system the parallelization is done by forking using the function mclapply. For windows systems socket cluster are used.

...

further arguments that are passed to the glmnet function.

Author

Martin Sill

References

Meinshausen N. and B\"uhlmann P. (2010), Stability Selection, Journal of the Royal Statistical Society: Series B (Statistical Methodology) Volume 72, Issue 4, pages 417--473.

Sill M., Hielscher T., Becker N. and Zucknick M. (2014), c060: Extended Inference with Lasso and Elastic-Net Regularized Cox and Generalized Linear Models, Journal of Statistical Software, Volume 62(5), pages 1--22. tools:::Rd_expr_doi("10.18637/jss.v062.i05")

See Also

glmnet,stabsel,plot.stabpath

Examples

if (FALSE) {
#gaussian
set.seed(1234)
x <- matrix(rnorm(100*1000,0,1),100,1000)
y <- x[1:100,1:1000]%*% c(rep(2,5),rep(-2,5),rep(.1,990))
res <- stabpath(y,x,weakness=1,mc.cores=2)
plot(res)

#binomial
y=sample(1:2,100,replace=TRUE)
res <- stabpath(y,x,weakness=1,mc.cores=2,family="binomial")
plot(res)
    
#multinomial
y=sample(1:4,100,replace=TRUE)
res <- stabpath(y,x,weakness=1,mc.cores=2,family="multinomial")
plot(res)
    
#poisson
N=100; p=1000
nzc=5
x=matrix(rnorm(N*p),N,p)
beta=rnorm(nzc)
f = x[,seq(nzc)]%*%beta
mu=exp(f)
y=rpois(N,mu)
res <- stabpath(y,x,weakness=1,mc.cores=2,family="poisson")
plot(res)

#Cox
library(survival)
set.seed(10101)
N=100;p=1000
nzc=p/3
x=matrix(rnorm(N*p),N,p)
beta=rnorm(nzc)
fx=x[,seq(nzc)]%*%beta/3
hx=exp(fx)
ty=rexp(N,hx)
tcens=rbinom(n=N,prob=.3,size=1)
y=cbind(time=ty,status=1-tcens)
res <- stabpath(y,x,weakness=1,mc.cores=2,family="cox")
plot(res)
}