PLSDA {structToolbox} | R Documentation

## 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 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 with the following `output` slots:

`scores` | (data.frame)
`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)

### 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.

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')

[Package structToolbox version 1.6.1 Index]