Description Usage Arguments Details Value References See Also
(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.
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 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: |
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 |
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. |
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.
For details see stabsel
.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.