caretSBF

0th

Percentile

Selection By Filtering (SBF) Helper Functions

Ancillary functions for univariate feature selection

Keywords
models
Usage
anovaFilter(x, y, cut = 0.05)
gamFilter(x, y, cut = 0.05) 

caretSBF lmSBF rfSBF treebagSBF ldaSBF nbSBF

Arguments
x
a matrix or data frame of numeric predictors
y
a numeric or factor vector of outcomes
cut
a p-value cut-off
Details

This page documents the functions that are used in selection by filtering (SBF). The functions described here are passed to the algorithm via the functions argument of sbfControl.

See sbfControl for details on how these functions should be defined.

anovaFilter and gamFilter are two examples of univariate filtering functions. anovaFilter fits a simple linear model between a single feature and the outcome, then the p-value for the whole model F-test is generated. If the p-values is greater than 0.05, the feature is retained for the model. gamFilter fits a generalized additive model between a single predictor and the outcome using a smoothing spline basis function. A p-value is generated using the whole model test from summary.gam and p-values greater than 0.05 indicate that a predictor will be excluded.

If a particular model fails for lm or gam, the predictor is not used in the model.

See Also

sbfControl, sbf, summary.gam

Aliases
  • caretSBF
  • lmSBF
  • rfSBF
  • treebagSBF
  • ldaSBF
  • nbSBF
  • gamFilter
  • anovaFilter
Documentation reproduced from package caret, version 4.39, License: GPL-2

Community examples

Looks like there are no examples yet.