function to estimate a stable set of variables

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)

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 $0.5 > pi_thr < 1$.

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

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. https://doi.org/10.18637/jss.v062.i05.

Author

Martin Sill \ m.sill@dkfz.de

Examples


if (FALSE) { # \dontrun{
#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")
} # }