function to plot a stability path

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

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://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)
plot(res,error=.5,type='pfer')
} # }