dt_pca: PCA

In daltoolbox: Leveraging Experiment Lines to Data Analytics

PCA

Description

PCA (Principal Component Analysis) is an unsupervised dimensionality reduction technique used in data analysis and machine learning. It transforms a dataset of possibly correlated variables into a new set of uncorrelated variables called principal components.

Usage

dt_pca(attribute = NULL, components = NULL)

Arguments

attribute	target attribute to model building
components	number of components for PCA

Value

returns an object of class dt_pca

Examples

mypca <- dt_pca("Species")
# Automatically fitting number of components
mypca <- fit(mypca, iris)
iris.pca <- transform(mypca, iris)
head(iris.pca)
head(mypca$pca.transf)
# Manual establishment of number of components
mypca <- dt_pca("Species", 3)
mypca <- fit(mypca, datasets::iris)
iris.pca <- transform(mypca, iris)
head(iris.pca)
head(mypca$pca.transf)

daltoolbox documentation built on Nov. 3, 2024, 9:06 a.m.