met.PLSDA.CV: PLS-DA classification and feature selection

In NicWir/VisomX: User-Friendly and Comprehensive Analysis and Visualization of Omics Data

PLS-DA classification and feature selection

Description

met.PLSDA.CV performs group classification and feature selection on a PLS model.

Usage

met.PLSDA.CV(
  mSetObj = NA,
  methodName = "LOOCV",
  k = 5,
  compNum = GetDefaultPLSCVComp(mSetObj),
  choice = "Q2",
  data = "all"
)

Arguments

mSetObj	Enter name of the created mSet object after PLS model creation (see met.PLSR.Anal).
methodName	(Character) Enter one of two methods for PLS-DA model (cross) validation: "LOOCV" performs leave-one-out cross validation "CV" performs k-fold cross validation.
k	(Numeric) The number of (randomized) groups that the dataset is to be split into during cross validation if methodName = "CV".
compNum	Number of components used for model validation (detected automatically).
choice	(Character) Choose the criterion used to estimate the predictive ability of the model, "Q2" or "R2".
data	(Character) Enter "all" to train the PLS(-DA) model on your whole (filtered and normalized) dataset or "anova" to use a subset of features defined as significant based on ANOVA analysis.

Value

The input mSet object with the results of PLS-DA at mSetObj$analSet$plsda.

Author(s)

Nicolas T. Wirth [e-mail] Technical University of Denmark License: GNU GPL (>= 2)

References

adapted from PLSDA.CV (https://github.com/xia-lab/MetaboAnalystR).

NicWir/VisomX documentation built on Dec. 8, 2024, 1:27 a.m.