PLSDA: Partial least squares discriminant analysis

In computational-metabolomics/structtoolbox: Data processing & analysis tools for Metabolomics and other omics

Partial least squares discriminant analysis

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. For >2 groups a 1-vs-all approach is used. Group membership can be predicted for test samples based on a probability estimate of group membership, or the estimated y-value.

Usage

PLSDA(number_components = 2, factor_name, pred_method = "max_prob", ...)

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.
pred_method	(character) Prediction method. Allowed values are limited to the following: "max_yhat": The predicted group is selected based on the largest value of y_hat. "max_prob": The predicted group is selected based on the largest probability of group membership. The default is "max_prob".
...	Additional slots and values passed to struct_class.

Details

This object makes use of functionality from the following packages:

• pls

Value

A PLSDA object with the following output slots:

scores	(DatasetExperiment)
loadings	(data.frame)
yhat	(data.frame)
design_matrix	(data.frame)
y	(data.frame)
reg_coeff	(data.frame)
probability	(data.frame)
vip	(data.frame)
pls_model	(list)
pred	(data.frame)
threshold	(numeric)
sr	(data.frame) Selectivity ratio for a variable represents a measure of a variable's importance in the PLS model. The output data.frame contains a column of selectivity ratios, a column of p-values based on an F-distribution and a column indicating significance at p < 0.05.
sr_pvalue	(data.frame) A p-value computed from the Selectivity Ratio based on an F-distribution.

Inheritance

A PLSDA object inherits the following struct classes:

⁠[PLSDA]⁠ >> ⁠[PLSR]⁠ >> ⁠[model]⁠ >> ⁠[struct_class]⁠

References

Liland K, Mevik B, Wehrens R (2023). pls: Partial Least Squares and Principal Component Regression. R package version 2.8-3, 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 = "V1",
      pred_method = "max_prob")

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

computational-metabolomics/structtoolbox documentation built on July 2, 2024, 10:46 p.m.