R: Run Stability Selection

run_stabsel {stabs}

R Documentation

Run Stability Selection

Description

(Internal) function that is used to run stability selection (i.e. to apply the fit-function to the subsamples. This function is not intended to be directly called.

Usage

run_stabsel(fitter, args.fitter, n, p, cutoff, q, PFER, folds, B, assumption,
            sampling.type, papply, verbose, FWER, eval, names,
            mc.preschedule = FALSE, ...)

Arguments

`fitter`	a function to fit the model on subsamples. See argument `fitfun` of `stabsel` for details.
`args.fitter`	a named list containing additional arguments that are passed to `fitter`. See argument `args.fitfun` `stabsel` for details.
`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 per-family 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: `TRUE`) that determines wether `warnings` should be issued.
`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.

Details

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.

Value

An object of class stabsel with the following elements:

`phat`	selection probabilities.
`selected`	elements with maximal selection probability greater `cutoff`.
`max`	maximum of selection probabilities.
`cutoff`	cutoff used.
`q`	average number of selected variables used.
`PFER`	per-family 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.

References

B. Hofner, L. Boccuto and M. Goeker (2015), Controlling false discoveries in high-dimensional situations: Boosting with stability selection. BMC Bioinformatics, 16:144.
doi: 10.1186/s12859-015-0575-3.