Given a desired family-wise error rate (FWER) and a stability path calculated with stability.path the function selects an stable set of features and plots the stability path and the corresponding regularization path.

# S3 method for stabpath
plot(x, error=0.05, type=c("pfer","pcer"), pi_thr=0.6, xvar=c("lambda", "norm", "dev"),
col.all="black", col.sel="red", ...)

## Arguments

x an object of class "stabpath" as returned by the function stabpath. the desired type I error level w.r.t. to the chosen type I error rate. 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. the threshold used for the stability selection, should be in the range of 0.5 > pi_thr < 1. the variable used for the xaxis, e.g. for "lambda" the selection probabilities are plotted along the log of the penalization parameters, for "norm" along the L1-norm and for "dev" along the fraction of explained deviance. the color used for the variables that are not in the estimated stable set the color used for the variables in the estimated stable set further arguments that are passed to matplot

## 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

## Author

Martin Sill \ m.sill@dkfz.de

## References

Meinshausen N. and Buehlmann 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/

stabsel,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,error=.5,type='pfer')
}