Compute the missing parameter from the two given parameters in order to assess suitability of the parameter constellation
1 2 3 4 5 6 7 8 9 10 11  stabsel_parameters(p, ...)
## Default S3 method:
stabsel_parameters(p, cutoff, q, PFER,
B = ifelse(sampling.type == "MB", 100, 50),
assumption = c("unimodal", "rconcave", "none"),
sampling.type = c("SS", "MB"),
verbose = FALSE, FWER, ...)
## S3 method for class '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 perfamily error rate. This specifies the amount of falsely selected baselearners, 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 
use sampling scheme of of Shah & Samworth
(2013), i.e., with complementarty pairs ( 
verbose 
logical (default: 
FWER 
deprecated. Only for compatibility with older versions, use PFER instead. 
x 
an object of class 
heading 
logical. Specifies if a heading line should be printed. 
... 
additional arguments to be passed to next function. 
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 perfamily 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
.
An object of class stabsel_parameters
with a special print
method.
The object has the following elements:
cutoff 
cutoff used. 
q 
average number of selected variables used. 
PFER 
(realized) upper bound for the perfamily error rate. 
specifiedPFER 
specified upper bound for the perfamily error rate. 
p 
the number of effects subject to selection. 
B 
the number of subsamples. 
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.
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
.
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