Description Usage Arguments Details Value References See Also
(Internal) function that is used to run stability selection (i.e. to apply the fitfunction to the subsamples. This function is not intended to be directly called.
1 2 3 
fitter 
a function to fit the model on subsamples. See argument

args.fitter 
a named list containing additional arguments that are
passed to 
n 
the number of observations; needed for internal checks. 
p 
number of possible predictors (including intercept if applicable). 
cutoff 
cutoff between 0.5 and 1. 
q 
number of (unique) selected variables (or groups of variables depending on the model) that are selected on each subsample. 
PFER 
upper bound for the perfamily error rate. 
folds 
a weight matrix that represents the subsamples. 
B 
number of subsampling replicates. 
assumption 
distributional assumption. 
sampling.type 
sampling type to be used. 
papply 
(parallel) apply function. 
verbose 
logical (default: 
FWER 
deprecated. Only for compatibility with older versions, use PFER instead. 
eval 
logical. Determines whether stability selection is evaluated. 
names 
variable names that are used to label the results. 
mc.preschedule 
preschedule tasks? 
... 
additional arguments to be passed to next function. 
This is an internal function that fits the actual models to the
subsamples, i.e., this is the work horse that runs stability
selection. Usually, one should use stabsel
, which
internally calls run_stabsel
.
run_stabsel
can be used by expert users to implement stability
selection methods for new model types.
For details (e.g. on arguments) see stabsel
.
An object of class stabsel
with the following elements:
phat 
selection probabilities. 
selected 
elements with maximal selection probability greater

max 
maximum of selection probabilities. 
cutoff 
cutoff used. 
q 
average number of selected variables used. 
PFER 
perfamily error rate. 
p 
the number of effects subject to selection. 
sampling.type 
the sampling type used for stability selection. 
assumption 
the assumptions made on the selection probabilities. 
B. Hofner, L. Boccuto and M. Goeker (2015), Controlling false
discoveries in highdimensional situations: Boosting with stability
selection. BMC Bioinformatics, 16:144.
doi: 10.1186/s1285901505753.
For details see stabsel
.
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