`stabpath.Rd`

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

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

y | response variable. Like for the glment function: Quantitative for |
---|---|

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 |

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 |

... | further arguments that are passed to the |

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

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

a matrix which represents the stability path.

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

Martin Sill m.sill@dkfz.de

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.*
https://www.jstatsoft.org/v062/i05/

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) }