PLSR {structToolbox}R Documentation

Partial least squares regression

Description

PLS is a multivariate regression technique that extracts latent variables maximising covariance between the input data and the response. For regression the response is a continuous variable.

Usage

PLSR(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:

Value

A PLSR object with the following output slots:

scores (data.frame)
loadings (data.frame)
y (data.frame)
yhat (data.frame)
reg_coeff (data.frame)
vip (data.frame)
pls_model (list)
pred (data.frame)

References

Liland K, Mevik B, Wehrens R (2021). pls: Partial Least Squares and Principal Component Regression. R package version 2.8-0, https://CRAN.R-project.org/package=pls.

Examples

M = PLSR(factor_name='run_order')

[Package structToolbox version 1.6.0 Index]