bootPLSDA

An object where samples are in rows and features are in columns. 
This could be a simple matrix, data frame.

A numeric or factor vector containing the outcome for each sample.

The maximal number of component for PLS-DA

ncomp

A vector contains the sample used for the model

sample

The data set (data.frame) for testing. If the data contains a 
column with the name "class", this column is the sample class.

test

Whether split the data as train and test set. Default is 0 
which indicates not split the data.

split

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"

method

For repeated k-fold cross-validation only: the number of 
complete sets of folds to compute

repeats

Either the number of folds or number of resampling iterations

number

Arguments passed to the classification or regression routine

The package provides a integrated pipeline for mass spectrometry-
based metabolomic data analysis. It includes the stages peak detection, data
preprocessing, normalization, missing value imputation, univariate statistical
analysis, multivariate statistical analysis such as PCA and PLS-DA, metabolite
identification, pathway analysis, power analysis, feature selection and
modeling, data quality assessment.

bootPLSDA: Fit predictive models for PLS-DA

Description

Usage

Arguments

Value