plot.stabpath {c060} | R Documentation |
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 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", ...)
x |
an object of class "stabpath" as returned by the function |
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 |
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 |
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 |
Martin Sill \ m.sill@dkfz.de
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.
doi: 10.18637/jss.v062.i05
## 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)