R: Stability Selection

stabsel {mboost}

R Documentation

Stability Selection

Description

Selection of influential variables or model components with error control.

Usage

stabsel(object, FWER = 0.05, cutoff, q,
        folds = cv(model.weights(object), type = "subsampling", B = 100),
        papply = mclapply, verbose = TRUE, ...)

Arguments

`object`	an `mboost` object.
`FWER`	family-wise error rate to be controlled by the selection procedure.
`cutoff`	cutoff between 0.5 and 1.
`q`	average number of selected base-learners.
`folds`	a weight matrix with number of rows equal to the number of observations, see `cvrisk`.
`papply`	(parallel) apply function, defaults to `mclapply`. Alternatively, `parLapply` can be used. In the latter case, usually more setup is needed (see example for some details).
`verbose`	logical (default: `TRUE`) that determines wether `warnings` should be issued.
`...`	additional arguments to `cvrisk`.

Details

This function implements the "stability selection" procedure by Meinshausen and Buehlmann (2010).

Either cutoff or q must be specified. The probability of selecting at least one non-influential variable (or model component) is less than FWER.

Value

An object of class stabsel with 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.
`FWER`	family-wise error rate.

References

N. Meinshausen and P. Buehlmann (2010), Stability selection. Journal of the Royal Statistical Society, Series B, 72(4).

Examples


  data(bodyfat)

  ### (too) low-dimensional example
  mod <- glmboost(DEXfat ~ ., data = bodyfat)
  (sbody <- stabsel(mod, q = 3,
                    folds = cv(model.weights(mod), type = "subsampling", B = 25)))
  opar <- par(mai = par("mai") * c(1, 1, 1, 2.7))
  plot(sbody)
  par(opar)

[Package mboost version 2.2-3 Index]