CRAN/E | RelimpPCR

RelimpPCR

Relative Importance PCA Regression

Installation

About

Performs Principal Components Analysis (also known as PCA) dimensionality reduction in the context of a linear regression. In most cases, PCA dimensionality reduction is performed independent of the response variable for a regression. This captures the majority of the variance of the model's predictors, but may not actually be the optimal dimensionality reduction solution for a regression against the response variable. An alternative method, optimized for a regression against the response variable, is to use both PCA and a relative importance measure. This package applies PCA to a given data frame of predictors, and then calculates the relative importance of each PCA factor against the response variable. It outputs ordered factors that are optimized for model fit. By performing dimensionality reduction with this method, an individual can achieve a the same r-squared value as performing just PCA, but with fewer PCA factors. References: Yuri Balasanov (2017) .

github.com/mhernan88/RelimpPCR
Bug report File report

Key Metrics

Version 0.3.0
R ≥ 3.3.0
Published 2023-06-01 383 days ago
Needs compilation? no
License MIT
License File
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Maintainer

Maintainer

Michael Hernandez

Authors

Michael Hernandez
Yuri Balasanov

Material

README
Reference manual
Package source

macOS

r-release

arm64

r-oldrel

arm64

r-release

x86_64

r-oldrel

x86_64

Windows

r-devel

x86_64

r-release

x86_64

r-oldrel

x86_64

Old Sources

RelimpPCR archive

Depends

R ≥ 3.3.0

Imports

relaimpo
Rmisc
caret
ggplot2
reshape2
logger

Suggests

parallel
testthat