performanceResample: Uncertainty About Performance of a Classifier (EXPERIMENTAL)
In BuyseTest: Generalized Pairwise Comparisons

performanceResample

R Documentation

Uncertainty About Performance of a Classifier (EXPERIMENTAL)

Description

Use resampling to quantify uncertainties about the performance of one or several binary classifiers evaluated via cross-validation.

Usage

performanceResample(
  object,
  data = NULL,
  name.response = NULL,
  type.resampling = "permutation",
  n.resampling = 1000,
  fold.repetition = 0,
  conf.level = 0.95,
  cpus = 1,
  seed = NULL,
  trace = TRUE,
  filename = NULL,
  ...
)

Arguments

`object`	a `glm` or `range` object, or a list of such object.
`data`	[data.frame] the training data.
`name.response`	[character] The name of the response variable (i.e. the one containing the categories).
`type.resampling`	[character] Should non-parametric bootstrap (`"bootstrap"`) or permutation of the outcome (`"permutation"`) be used.
`n.resampling`	[integer,>0] Nnumber of bootstrap samples or permutations.
`fold.repetition`	[integer,>0] Nnumber of folds used in the cross-validation. Should be strictly positive.
`conf.level`	[numeric, 0-1] confidence level for the confidence intervals.
`cpus`	[integer, >0] the number of CPU to use. If strictly greater than 1, resampling is perform in parallel.
`seed`	[integer, >0] seed used to ensure reproducibility.
`trace`	[logical] Should the execution of the function be traced.
`filename`	[character] Prefix for the files containing each result.
`...`	arguments passed to `performance`.