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:

- `pls`

### 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 1.6.1 Index]