bootPLSDA: Fit predictive models for PLS-DA

In wenbostar/metaX: A flexible and comprehensive software for processing omics data.

Fit predictive models for PLS-DA

Description

Fit predictive models for PLS-DA

Usage

bootPLSDA(x, y, ncomp = 2, sample = NULL, test = NULL, split = 0,
  method = "repeatedcv", repeats = 250, number = 7, ...)

Arguments

x	An object where samples are in rows and features are in columns. This could be a simple matrix, data frame.
y	A numeric or factor vector containing the outcome for each sample.
ncomp	The maximal number of component for PLS-DA
sample	A vector contains the sample used for the model
test	The data set (data.frame) for testing. If the data contains a column with the name "class", this column is the sample class.
split	Whether split the data as train and test set. Default is 0 which indicates not split the data.
method	The resampling method: boot, boot632, cv, repeatedcv, LOOCV, LGOCV (for repeated training/test splits), none (only fits one model to the entire training set), oob (only for random forest, bagged trees, bagged earth, bagged flexible discriminant analysis, or conditional tree forest models), "adaptive_cv", "adaptive_boot" or "adaptive_LGOCV"
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
...	Arguments passed to the classification or regression routine

Value

A list object

wenbostar/metaX documentation built on July 4, 2023, 7:50 p.m.