**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.

For an expanded and executable version of this file please see

`vignette("Using_stabs", package = "stabs")`

- Current version (from CRAN):

`install.packages("stabs")`

- Latest development version from GitHub:

```
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")
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))
## plot results
par(mfrow = c(2, 1))
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"))
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 model
fit <- lars::lars(x, y, max.steps = q, ...)
## which coefficients are non-zero?
selected <- unlist(fit$actions)
## check if variables are removed again from the active set
## and remove these from selected
if (any(selected < 0)) {
idx <- which(selected < 0)
idx <- c(idx, which(selected %in% abs(selected[idx])))
selected <- selected[-idx]
}
ret <- logical(ncol(x))
ret[selected] <- TRUE
names(ret) <- colnames(x)
## compute selection paths
cf <- fit$beta
sequence <- t(cf != 0)
## return both
return(list(selected = ret, path = sequence))
}
```

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")
## 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"))
opar <- par(mai = par("mai") * c(1, 1, 1, 2.7))
plot(sbody, type = "paths")
par(opar)
plot(sbody, type = "maxsel", ymargin = 6)
```

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 (2017). stabs: Stability
Selection with Error Control, R package version R package version
0.6-3, 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 obtain BibTeX references use

`toBibtex(citation("stabs"))`