knitr::opts_chunk$set(echo = TRUE) required <- c("lars", "mboost") if (!all(sapply(required, function(pkg) requireNamespace(pkg, quietly = TRUE)))) knitr::opts_chunk$set(eval = FALSE)
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.
To be able to use the
install_github() command, one needs to install devtools first:
A simple example of how to use stabs with package lars:
library("stabs") library("lars") ## 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)) ## stepwise selection (stab.stepwise <- stabsel(x = bodyfat[, -2], y = bodyfat[,2], fitfun = lars.stepwise, cutoff = 0.75, PFER = 1))
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
These need to take arguments
x (the predictors),
y (the outcome) and
number of selected variables as defined for stability selection. Additional
arguments to the variable selection method can be handled by
.... In the
stabsel() these can then be specified as a named list which is given
fitfun function then needs to return a named list with two elements
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:
To see more examples simply print, e.g.,
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
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") ## the same: stabsel(mod, q = 3, PFER = 1, sampling.type = "MB", eval = FALSE) ## now run stability selection (sbody <- stabsel(mod, q = 3, PFER = 1, sampling.type = "MB"))
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
which will currently give you
To obtain BibTeX references use
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