Compute the missing parameter from the two given parameters in order to assess suitability of the parameter constellation

`stabsel_parameters(p, ...)`# S3 method for default
stabsel_parameters(p, cutoff, q, PFER,
B = ifelse(sampling.type == "MB", 100, 50),
assumption = c("unimodal", "r-concave", "none"),
sampling.type = c("SS", "MB"),
verbose = FALSE, FWER, ...)

# S3 method for stabsel_parameters
print(x, heading = TRUE, ...)

p

number of possible predictors (including intercept if applicable).

cutoff

cutoff between 0.5 and 1. Preferably a value between 0.6 and 0.9 should be used.

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. This specifies the amount of falsely selected base-learners, which is tolerated. See details.

B

number of subsampling replicates. Per default, we use 50 complementary pairs for the error bounds of Shah & Samworth (2013) and 100 for the error bound derived in Meinshausen & Buehlmann (2010). As we use \(B\) complementray pairs in the former case this leads to \(2B\) subsamples.

assumption

Defines the type of assumptions on the
distributions of the selection probabilities and simultaneous
selection probabilities. Only applicable for
`sampling.type = "SS"`

. For `sampling.type = "MB"`

we
always use code"none".

sampling.type

use sampling scheme of of Shah & Samworth
(2013), i.e., with complementarty pairs (`sampling.type = "SS"`

),
or the original sampling scheme of Meinshausen & Buehlmann (2010).

verbose

logical (default: `TRUE`

) that determines wether
`warnings`

should be issued.

FWER

deprecated. Only for compatibility with older versions, use PFER instead.

x

an object of class `"stabsel_parameters"`

.

heading

logical. Specifies if a heading line should be printed.

…

additional arguments to be passed to next function.

An object of class `stabsel_parameters`

with a special `print`

method.
The object has the following elements:

cutoff used.

average number of selected variables used.

(realized) upper bound for the per-family error rate.

specified upper bound for the per-family error rate.

the number of effects subject to selection.

the number of subsamples.

the sampling type used for stability selection.

the assumptions made on the selection probabilities.

This function implements the error bounds for stability selection by Meinshausen and Buehlmann (2010) and the improved error bounds by Shah and Samworth (2013). For details see also Hofner et al. (2014).

Two of the three arguments `cutoff`

, `q`

and `PFER`

*must* be specified. The per-family error rate (PFER), i.e., the
expected number of false positives \(E(V)\), where \(V\) is the
number of false positives, is bounded by the argument `PFER`

.

For more details see also `stabsel`

.

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

N. Meinshausen and P. Buehlmann (2010), Stability selection.
*Journal of the Royal Statistical Society, Series B*,
**72**, 417--473.

R.D. Shah and R.J. Samworth (2013), Variable selection with error
control: another look at stability selection. *Journal of the Royal
Statistical Society, Series B*, **75**, 55--80.

For more details see also `stabsel`

.