stabs implements resampling procedures to assess the stability of selected variables with additional finite sample error control for high-dimensional variable selection procedures such as Lasso or boosting. Both, standard stability selection (Meinshausen & Bühlmann, 2010, doi:10.1111/j.1467-9868.2010.00740.x) and complementarty pairs stability selection with improved error bounds (Shah & Samworth, 2013, doi:10.1111/j.1467-9868.2011.01034.x) are implemented. The package can be combined with arbitrary user specified variable selection approaches.
install.packages("stabs")
library("devtools")
install_github("hofnerb/stabs")
To be able to use the install_github() command, one needs to install devtools first:
install.packages("devtools")
A simple example of how to use stabs with package lars:
library("stabs")
## Loading required package: parallel
library("lars")
## Loaded lars 1.2
## make data set available
data("bodyfat", package = "TH.data")
## set seed
set.seed(1234)
## lasso
(stab.lasso <- stabsel(x = bodyfat[, -2], y = bodyfat[,2],
fitfun = lars.lasso, cutoff = 0.75,
PFER = 1))
## Stability Selection with unimodality assumption
##
## Selected variables:
## waistcirc hipcirc
## 2 3
##
## Selection probabilities:
## age elbowbreadth kneebreadth anthro3b anthro4 anthro3c
## 0.00 0.00 0.00 0.02 0.04 0.05
## anthro3a waistcirc hipcirc
## 0.08 0.86 0.95
##
## ---
## Cutoff: 0.75; q: 2; PFER (*): 0.454
## (*) or expected number of low selection probability variables
## PFER (specified upper bound): 1
## PFER corresponds to signif. level 0.0504 (without multiplicity adjustment)
## stepwise selection
(stab.stepwise <- stabsel(x = bodyfat[, -2], y = bodyfat[,2],
fitfun = lars.stepwise, cutoff = 0.75,
PFER = 1))
## Stability Selection with unimodality assumption
##
## No variables selected
##
## Selection probabilities:
## elbowbreadth age kneebreadth anthro3c anthro4 anthro3a
## 0.00 0.02 0.02 0.06 0.17 0.20
## waistcirc anthro3b hipcirc
## 0.44 0.52 0.57
##
## ---
## Cutoff: 0.75; q: 2; PFER (*): 0.454
## (*) or expected number of low selection probability variables
## PFER (specified upper bound): 1
## PFER corresponds to signif. level 0.0504 (without multiplicity adjustment)
Now plot the results
## plot results
par(mfrow = c(1, 2))
plot(stab.lasso, main = "Lasso")
plot(stab.stepwise, main = "Stepwise Selection")
We can see that stepwise selection seems to be quite unstable, even in this low dimensional example!
To use stabs with user specified functions, one can specify an own fitfun.
These need to take arguments x (the predictors), y (the outcome) and q the
number of selected variables as defined for stability selection. Additional
arguments to the variable selection method can be handled by .... In the
function stabsel() these can then be specified as a named list which is given
to args.fitfun.
The fitfun function then needs to return a named list with two elements
selected and path:
selected is a vector that indicates which variable was selected.path is a matrix that indicates which variable was selected in which step.
Each row represents one variable, the columns represent the steps.
The latter is optional and only needed to draw the complete selection paths.The following example shows how lars.lasso is implemented:
lars.lasso
## function (x, y, q, ...)
## {
## if (!requireNamespace("lars", quietly = TRUE))
## stop("Package ", sQuote("lars"), " needed but not available")
## if (is.data.frame(x)) {
## message("Note: ", sQuote("x"), " is coerced to a model matrix without intercept")
## x <- model.matrix(~. - 1, x)
## }
## fit <- lars::lars(x, y, max.steps = q, ...)
## selected <- unlist(fit$actions)
## if (any(selected < 0)) {
## idx <- which(selected < 0)
## idx <- c(idx, which(selected %in% abs(selected[idx])))
## selected <- selected[-idx]
## }
## ret <- logical(ncol(x))
## ret[selected] <- TRUE
## names(ret) <- colnames(x)
## cf <- fit$beta
## sequence <- t(cf != 0)
## return(list(selected = ret, path = sequence))
## }
## <bytecode: 0x0000000024b2bef8>
## <environment: namespace:stabs>
To see more examples simply print, e.g., lars.stepwise, glmnet.lasso, or
glmnet.lasso_maxCoef. Please contact me if you need help to integrate your
method of choice.
Instead of specifying a fitting function, one can also use stabsel directly on
computed boosting models from
mboost.
library("stabs")
library("mboost")
### 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")
## Stability selection without further assumptions
##
## Cutoff: 0.95; q: 3; PFER: 1
## PFER (specified upper bound): 1
## PFER corresponds to signif. level 0.1 (without multiplicity adjustment)
## the same:
stabsel(mod, q = 3, PFER = 1, sampling.type = "MB", eval = FALSE)
## Stability selection without further assumptions
##
## Cutoff: 0.95; q: 3; PFER: 1
## PFER (specified upper bound): 1
## PFER corresponds to signif. level 0.1 (without multiplicity adjustment)
## now run stability selection
(sbody <- stabsel(mod, q = 3, PFER = 1, sampling.type = "MB"))
## Stability Selection without further assumptions
##
## Selected variables:
## hipcirc
## 4
##
## Selection probabilities:
## (Intercept) age elbowbreadth kneebreadth anthro4 anthro3b
## 0.00 0.00 0.00 0.04 0.08 0.15
## anthro3c anthro3a waistcirc hipcirc
## 0.18 0.62 0.93 1.00
##
## ---
## Cutoff: 0.95; q: 3; PFER: 1
## PFER corresponds to signif. level 0.1 (without multiplicity adjustment)
Now let us plot the results, as paths and as maximum selection frequencies:
opar <- par(mai = par("mai") * c(1, 1, 1, 2.7), mfrow = c(1, 2))
plot(sbody, type = "paths")
plot(sbody, type = "maxsel", ymargin = 6)
par(opar)
To cite the package in publications please use
citation("stabs")
which will currently give you
##
## To cite package 'stabs' in publications use:
##
## Benjamin Hofner and Torsten Hothorn (2021). stabs: Stability
## Selection with Error Control, R package version 0.6-4,
## https://CRAN.R-project.org/package=stabs.
##
## Benjamin Hofner, Luigi Boccuto and Markus Goeker (2015). Controlling
## false discoveries in high-dimensional situations: Boosting with
## stability selection. BMC Bioinformatics, 16:144.
## doi:10.1186/s12859-015-0575-3
##
## To cite the stability selection for 'gamboostLSS' models use:
##
## Thomas, J., Mayr, A., Bischl, B., Schmid, M., Smith, A., and Hofner,
## B. (2017). Gradient boosting for distributional regression - faster
## tuning and improved variable selection via noncyclical updates.
## Statistics and Computing. Online First. DOI 10.1007/s11222-017-9754-6
##
## Use 'toBibtex(citation("stabs"))' to extract BibTeX references.
## To see these entries in BibTeX format, use 'print(<citation>,
## bibtex=TRUE)', 'toBibtex(.)', or set
## 'options(citation.bibtex.max=999)'.
To obtain BibTeX references use
toBibtex(citation("stabs"))