PLSDA: Partial least squares discriminant analysis

In structToolbox: Data processing & analysis tools for Metabolomics and other omics

Description

PLS is a multivariate regression technique that extracts latent variables maximising covariance between the input data and the response. The Discriminant Analysis variant uses group labels in the response variable and applies a threshold to the predicted values in order to predict group membership for new samples.

Usage

PLSDA(number_components = 2, factor_name, ...)

Arguments

number_components	(numeric, integer) The number of PLS components. The default is 2.
factor_name	(character) The name of a sample-meta column to use.
...	Additional slots and values passed to struct_class.

Details

This object makes use of functionality from the following packages:

• pls

Value

A PLSDA object.

References

Mevik B, Wehrens R, Liland K (2020). pls: Partial Least Squares and Principal Component Regression. R package version 2.7-3, https://CRAN.R-project.org/package=pls.

Perez NF, Ferre J, Boque R (2009). “Calculation of the reliability of classification in discriminant partial least-squares binary classification.” Chemometrics and Intelligent Laboratory Systems, 95(2), 122-128.

Barker M, Rayens W (2003). “Partial least squares for discrimination.” Journal of Chemometrics, 17(3), 166-173.

Examples

M = PLSDA('number_components'=2,factor_name='Species')

structToolbox documentation built on Nov. 8, 2020, 6:54 p.m.