bestglm: pcreg – R documentation

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pcreg

Principal Component and Partial Least Squares Regression

Description

Regression using the principal components or latent variables as inputs. The best model is selected using components 1, 2, ..., r, where r, the number of components to use is determined by the AIC or BIC.

Usage

pcreg(Xy, scale = TRUE, method = c("PC", "LV"), ic = c("BIC", "AIC"))

Arguments

`Xy`	dataframe with variable names in columns
`scale`	Whether or not to scale. Default is TRUE.
`method`	either principal components, "PC", or partial least squares latent variables, "LV"
`ic`	"BIC" or "AIC"

Value

An S3 class list "pcreg" with components

`lmfit`	lm model
`PLSFit`	column sd
`Z`	matrix of principal components or latent vector
`method`	'pcr' or 'pls'

Author(s)

A. I. McLeod

Examples

pcreg(mcdonald, scale=TRUE, method="PC")
pcreg(mcdonald, scale=TRUE, method="LV")

bestglm

Best Subset GLM and Regression Utilities

v0.37.3

GPL (>= 2)

Authors

A.I. McLeod, Changjiang Xu and Yuanhao Lai

Initial release

2020-03-13