bootPLSDA: Fit predictive models for PLS-DA

Description Usage Arguments Value

Description

Fit predictive models for PLS-DA

Usage

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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


metaX documentation built on Oct. 5, 2016, 4:41 a.m.

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