stabsel.boostrq {boostrq}

Stability Selection for boosting regression quantiles

Description

Stability Selection for boosting regression quantiles

Usage

## S3 method for class 'boostrq'
stabsel(
  x,
  cutoff,
  q,
  PFER,
  grid = 0:mstop(x),
  folds = stabs::subsample(x$weights, B = B),
  B = ifelse(sampling.type == "MB", 100, 50),
  assumption = "unimodal",
  sampling.type = "SS",
  papply = parallel::mclapply,
  verbose = TRUE,
  ...
)

Arguments

x

a fitted model of class "boostrq"

cutoff

cutoff between 0.5 and 1. Preferably a value between 0.6 and 0.9 should be used

q

number of (unique) selected componenents (base-learners) that are selected in each subsample.

PFER

upper bound for the per-family error rate. This specifies the amount of falsely selected base-learners, which is tolerated.

grid

a numeric vector of the form 0:m.

folds

a weight matrix with number of rows equal to the number of observations. Usually one should not change the default here as subsampling with a fraction of 1/2 is needed for the error bounds to hold.

B

umber 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.

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).

papply

(parallel) apply function, defaults to mclapply. To run sequentially (i.e. not in parallel), one can use lapply.

verbose

logical (default: TRUE) that determines wether warnings should be issued.

...

additional arguments passed to callies

Value

An object of class stabsel.

Examples

boosted.rq <-
boostrq(
 formula = mpg ~ brq(cyl) + brq(hp) + brq(am) + brq(wt) + brq(drat),
 data = mtcars,
 mstop = 600,
 nu = 0.1,
 tau = 0.5
)

stabsel_parameters(
 q = 3,
 PFER = 1,
 p = 5,
 sampling.type = "SS",
 assumption = "unimodal"
)


set.seed(100)
brq.stabs <-
stabsel(
 x = boosted.rq,
 q = 3,
 PFER = 1,
 sampling.type = "SS",
 assumption = "unimodal"
)

brq.stabs