stabsel {mboost} | R Documentation

## Stability Selection

### Description

Selection of influential variables or model components with error control.

### Usage

    ## a method to compute stability selection paths for fitted mboost models
    ## S3 method for class 'mboost'
    stabsel(x, cutoff, q, PFER,
            folds = subsample(model.weights(x), B = B),
            B = ifelse(sampling.type == "MB", 100, 50),
            assumption = c("unimodal", "r-concave", "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, ...)

### Arguments

`x, p` | an fitted model of class `"mboost"`.
`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 per-family error rate. This specifies the amount of falsely selected base-learners, which is tolerated. See details.

`folds`

a weight matrix with number of rows equal to the number of observations, see `cvrisk` and `subsample`. Usually one should not change the default here as subsampling with a fraction of 1/2 is needed for the error bounds to hold. One usage scenario where specifying the folds by hand might be the case when one has dependent data (e.g. clusters) and thus wants to draw clusters (i.e., multiple rows together) not individuals.

`assumption` | Defines the type of assumptions on the distributions of the selection probabilities and simultaneous selection probabilities. Only applicable for `sampling.type = "SS"`. For `sampling.type = "MB"` we always use code"none".
`sampling.type` | use sampling scheme of of Shah & Samworth (2013), i.e., with complementarty pairs (`sampling.type = "SS"`), or the original sampling scheme of Meinshausen & Buehlmann (2010).
`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 `mclapply`. Alternatively, `parLapply` can be used. In the latter case, usually more setup is needed (see example of `cvrisk` for some details).
`verbose` | logical (default: `TRUE`) that determines wether `warnings` should be issued.
`FWER` | deprecated. Only for compatibility with older versions, use PFER instead.
`eval` | logical. Determines whether stability selection is evaluated (`eval = TRUE`; default) or if only the parameter combination is returned.
`...` | additional arguments to parallel apply methods such as `mclapply` and to `cvrisk`.

### Details

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

### Value

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 `cutoff`.
`max` | maximum of selection probabilities.
`cutoff` | cutoff used.
`q` | average number of selected variables used.
`PFER` | per-family error rate.
`sampling.type` | the sampling type used for stability selection.
`assumption` | the assumptions made on the selection probabilities.
`call` | the call.

### References

B. Hofner, L. Boccuto and M. Goeker (2015), Controlling false discoveries in high-dimensional 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.

### See Also

`stabsel` and `stabsel_parameters`

### Examples

      ## make data set available
      data("bodyfat", package = "TH.data")
      ## set seed
      set.seed(1234)

      ### 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)

    ############################################################
    ## 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)

[Package mboost version 2.8-1 Index]