Description Usage Arguments Details Value See Also Examples
Method to change the parameters cutoff
, PFER
and
assumption
of stability selection that can be altered without
the need to rerun the subsampling process.
1 2 
x 
an object that results from a call to 
cutoff 
cutoff between 0.5 and 1. Preferably a value between 0.6 and 0.9 should be used. 
PFER 
upper bound for the perfamily error rate. This specifies the amount of falsely selected baselearners, which is tolerated. See details. 
assumption 
Defines the type of assumptions on the
distributions of the selection probabilities and simultaneous
selection probabilities. Only applicable for

... 
additional arguments that are currently ignored. 
This function allows to alter the parameters cutoff
,
PFER
and assumption
of a fitted stability selection
result. All other parameters are reused from the original stability
selection results. The missing paramter is computed and the selected
variables are updated accordingly.
An object of class stabsel
. For details see there.
stabsel
for the generic function,
stabsel_parameters
for the computation of error bounds,
fitfun
for available fitting functions and
plot.stabsel
for available plot functions
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30  if (require("TH.data")) {
## make data set available
data("bodyfat", package = "TH.data")
} else {
## simulate some data if TH.data not available.
## Note that results are nonsense with this data.
bodyfat < matrix(rnorm(720), nrow = 72, ncol = 10)
}
## set seed
set.seed(1234)
####################################################################
### using stability selection with Lasso methods:
if (require("lars")) {
(stab.lasso < stabsel(x = bodyfat[, 2], y = bodyfat[,2],
fitfun = lars.lasso, cutoff = 0.75,
PFER = 1))
par(mfrow = c(2, 1))
plot(stab.lasso)
## now change the PFER and the assumption:
(stab.lasso_cf0.93_rconc < stabsel(stab.lasso, cutoff = 0.93,
assumption = "rconcave"))
plot(stab.lasso_cf0.93_rconc)
## the cutoff did change and hence the PFER and the selected
## variables
}

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