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Partial least squares discriminant analysis

Source: R/PLSDA_class.R
PLSDA.Rd

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.

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.

Details

This object makes use of functionality from the following packages:

  • pls

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')