c060 (version 0.3-0)

stabsel: function to estimate a stable set of variables

Description

Given a desired type I error rate and a stability path calculated with stability.path the function selects a stable set of variables.

Usage

stabsel(x,error=0.05,type=c("pfer","pcer"),pi_thr=0.6)

Value

a list of four objects

stable

a vector giving the positions of the estimated stable variables

lambda

the penalization parameter used for the stability selection

lpos

the position of the penalization parameter in the regularization path

error

the desired type I error level w.r.t. to the chosen type I error rate

type

the type I error rate

Arguments

x

an object of class "stabpath" as returned by the function stabpath.

error

the desired type I error level w.r.t. to the chosen type I error rate.

type

The type I error rate used for controlling the number falsely selected variables. If type="pfer" the per-family error rate is controlled and error corresponds to the expected number of type I errors. Selecting type="pfer" and error in the range of $0 > error < 1$ will control the family-wise error rate, i.e. the probability that at least one variable in the estimated stable set has been falsely selected. If type="pcer" the per-comparison error rate is controlled and error corresponds to the expected number of type I errors divided by the number variables.

pi_thr

the threshold used for the stability selection, should be in the range of $0.5 > pi_thr < 1$.

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

plot.stabpath,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)
stabsel(res,error=0.05,type="pfer")
}