# stabsel

0th

Percentile

##### Stability Selection

Selection of influential variables or model components with error control.

Keywords
nonparametric
##### Usage
## a method to compute stability selection paths for fitted mboost models
# S3 method for mboost
stabsel(x, cutoff, q, PFER, grid = 0:mstop(x),
folds = subsample(model.weights(x), B = B),
B = ifelse(sampling.type == "MB", 100, 50),
assumption = c("unimodal", "r-concave", "none"),
sampling.type = c("SS", "MB"),
papply = mclapply, verbose = TRUE, FWER, eval = TRUE, ...)## just a wrapper to stabsel(p, ..., eval = FALSE)
# S3 method for mboost
stabsel_parameters(p, ...)
##### Arguments
x, p

an fitted model of class "mboost".

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.

grid

a numeric vector of the form 0:m. See also cvrisk.

folds

a weight matrix with number of rows equal to the number of observations, see cvrisk and subsample. Usually one should not change the default here as subsampling with a fraction of $1/2$ is needed for the error bounds to hold. One usage scenario where specifying the folds by hand might be the case when one has dependent data (e.g. clusters) and thus wants to draw clusters (i.e., multiple rows together) not individuals.

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).

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.

papply

(parallel) apply function, defaults to mclapply. Alternatively, parLapply can be used. In the latter case, usually more setup is needed (see example of cvrisk for some details).

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 (eval = TRUE; default) or if only the parameter combination is returned.

additional arguments to parallel apply methods such as mclapply and to cvrisk.

##### Details

For details see stabsel in package stabs and Hofner et al. (2015).

##### Value

An object of class stabsel with a special print method. The object has 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.

sampling.type

the sampling type used for stability selection.

assumption

the assumptions made on the selection probabilities.

call

the call.

##### References

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

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.

stabsel and stabsel_parameters

##### Aliases
• stabsel
• stabsel.mboost
• stabsel_parameters.mboost
##### Examples
# NOT RUN {
## make data set available
data("bodyfat", package = "TH.data")
## set seed
set.seed(1234)

### low-dimensional example
mod <- glmboost(DEXfat ~ ., data = bodyfat)

## compute cutoff ahead of running stabsel to see if it is a sensible
## parameter choice.
##   p = ncol(bodyfat) - 1 (= Outcome) + 1 ( = Intercept)
stabsel_parameters(q = 3, PFER = 1, p = ncol(bodyfat) - 1 + 1,
sampling.type = "MB")
## the same:
stabsel(mod, q = 3, PFER = 1, sampling.type = "MB", eval = FALSE)

# }
# NOT RUN {
############################################################
## Do not run and check these examples automatically as
## they take some time (~ 10 seconds depending on the system)

## now run stability selection
(sbody <- stabsel(mod, q = 3, PFER = 1, sampling.type = "MB"))
opar <- par(mai = par("mai") * c(1, 1, 1, 2.7))
plot(sbody)
par(opar)

plot(sbody, type = "maxsel", ymargin = 6)

## End(Not run and test)
# }

Documentation reproduced from package mboost, version 2.9-1, License: GPL-2

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