plot.stabpath: function to plot a stability path

In c060: Extended Inference for Lasso and Elastic-Net Regularized Cox and Generalized Linear Models

function to plot a stability path

Description

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.

Usage

## S3 method for class '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.
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.
xvar	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.
col.all	the color used for the variables that are not in the estimated stable set
col.sel	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(s)

Martin Sill \ [e-mail]

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. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.18637/jss.v062.i05")}

See Also

stabsel,stabpath

Examples

## Not run: 
#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')

## End(Not run)

c060 documentation built on March 31, 2023, 5:15 p.m.