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, ...)
number of possible predictors (including intercept if applicable).
cutoff between 0.5 and 1. Preferably a value between 0.6 and 0.9 should be used.
number of (unique) selected variables (or groups of variables depending on the model) that are selected on each subsample.
upper bound for the per-family error rate. This specifies the amount of falsely selected base-learners, which is tolerated. See details.
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.
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".
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).
logical (default: TRUE) that determines wether
warnings should be issued.
deprecated. Only for compatibility with older versions, use PFER instead.
an object of class "stabsel_parameters".
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.