stabsel  R Documentation 
Selection of influential variables or model components with error control.
## a method to compute stability selection paths for fitted mboost models ## S3 method for class '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", "rconcave", "none"), sampling.type = c("SS", "MB"), papply = mclapply, verbose = TRUE, FWER, eval = TRUE, ...) ## just a wrapper to stabsel(p, ..., eval = FALSE) ## S3 method for class 'mboost' stabsel_parameters(p, ...)
x, p 
an fitted model of class 
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. 
grid 
a numeric vector of the form 
folds 
a weight matrix with number of rows equal to the number
of observations, see 
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 ( 
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

verbose 
logical (default: 
FWER 
deprecated. Only for compatibility with older versions, use PFER instead. 
eval 
logical. Determines whether stability selection is
evaluated ( 
... 
additional arguments to parallel apply methods such as

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

max 
maximum of selection probabilities. 
cutoff 
cutoff used. 
q 
average number of selected variables used. 
PFER 
perfamily error rate. 
sampling.type 
the sampling type used for stability selection. 
assumption 
the assumptions made on the selection probabilities. 
call 
the call. 
B. Hofner, L. Boccuto and M. Goeker (2015), Controlling false discoveries in highdimensional 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
## make data set available data("bodyfat", package = "TH.data") ## set seed set.seed(1234) ### lowdimensional 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) ## End(Not run)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.