recursive_feature_elimination: Perform recursive feature elimination
In specmine: Metabolomics and Spectral Data Analysis and Mining

Description Usage Arguments Value Examples

Perform recursive feature elimination on the dataset using caret's package.

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recursive_feature_elimination(datamat, samples.class, 
functions = caret::rfFuncs, method = "cv", repeats = 5, 
number = 10, subsets = 2^(2:4))

`datamat`	data matrix from dataset.
`samples.class`	string or index indicating what metadata to use.
`functions`	a list of functions for model fitting, prediction and variable importance.
`method`	the external resampling method: boot, cv, LOOCV or LGOCV (for repeated training/test splits.
`repeats`	for repeated k-fold cross-validation only: the number of complete sets of folds to compute.
`number`	either the number of folds or number of resampling iterations.
`subsets`	a numeric vector of integers corresponding to the number of features that should be retained.

A caret's rfe object with the result of recursive feature selection.

  ## Example of recursive feature elimination
  library(specmine.datasets)
  data(cachexia)
  library(caret)
  rfe.result = recursive_feature_elimination(cachexia$data, 
	       cachexia$metadata$Muscle.loss, functions = caret::rfFuncs, 
	       method = "cv", number = 3, subsets = 2^(1:6))