CrossValidation: Cross-validation for classification models
In mattdneal/FAIMSToolkit: Tools to aid in the analysis of FAIMS data

Cross-validation for classification models

CrossValidation(data.train, targetValues, models = c("rf", "glmnet",
  "svmRadial", "svmLinear", "gbm", "nnet", "glm"), nFolds = 10,
  stratified = TRUE, threshold = NULL, nKeep = NULL, SGoF = NULL,
  verbose = FALSE, heatmap = FALSE, PCA = FALSE, extraData = NULL,
  tuneKFolds = 2, tuneRepeats = 5, tuneLength = 5, folds = NULL,
  precomputedScores = NULL)

`data.train`	Training data to be divided into folds (must be a data frame)
`targetValues`	Target responses
`models`	list of caret::train models to train
`nFolds`	number of folds
`stratified`	TRUE for stratified folds
`threshold`	list of threshold p-values for selecting features to keep
`nKeep`	list of number of features to keep
`SGoF`	alpha for Sequential Goodness of Fit selection of features
`verbose`	TRUE for verbose output
`heatmap`	TRUE to plot a heatmap of selected features for each fold
`PCA`	TRUE to apply PCA to selected features in each fold
`extraData`	any additional data to add to training data after feature selection
`tuneKFolds`	number of folds for tuning models within each fold
`tuneRepeats`	number of repeats for tuning models within each fold
`tuneLength`	number of parameters to test when tuning models
`folds`	optionally pre-specify which samples go in which fold. Should be NULL to select folds randomly, or a vector of length `nrow(data.train)` containing values in `seq(nFolds)`
`precomputedScores`	precomputed scores of features for each fold. A sample of 100 will be tested for each fold and an error thrown in the event of discrepancies.