getCV: Perform cross-validation on training partition.

In novasmedley/gbmSpm: Find predictive sequential patterns for clinical glioblastoma patients

Description

Perform cross-validation on training partition.

Usage

getCV(data, formula, labelName, lasso = FALSE, llength = NULL,
  lmax = NULL, predictors = NULL, needToRemove = NULL,
  createModelMatrix = FALSE, metric = c("prAUC", "rocAUC"), seed, folds,
  verbose = TRUE)

Arguments

data	dataframe, rows are samples, cols are features plus some metadata not meant for modeling and will be removed
formula	char or formula object
labelName	char, column name of binary label
lasso	logical, whether to use lasso regularization
llength	num, number of lambdas to consider up to lmax
lmax	num, maximum lambda to consider, cannot be NULL if lambda is NULL
predictors	char, names of columns in data that should be in logit fit data
needToRemove	char, names of columns in data that should not be in logit fit data
createModelMatrix	logical, call model.matrix
metric	char, see aucs
seed	int, seed for split
folds	number of folds
verbose	logical, True for print, False for silence

Details

Removes rows for which a column is NA.

Value

folds a list of arrays indicating row position integers corresponding to fold split

See Also

groupKFold, getPatIDs, getClassLabels, getData

novasmedley/gbmSpm documentation built on May 17, 2019, 10:39 a.m.