dt_pca: PCA

In daltoolbox: Leveraging Experiment Lines to Data Analytics

PCA

Description

Principal Component Analysis (PCA) for unsupervised dimensionality reduction. Transforms correlated variables into orthogonal principal components ordered by explained variance.

Usage

dt_pca(attribute = NULL, components = NULL)

Arguments

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

Details

Fits PCA on (optionally) the numeric predictors only (excluding attribute when provided), removes constant columns, and selects the number of components by an elbow rule (minimum curvature) unless components is set explicitly.

Value

returns an object of class dt_pca

References

Pearson, K. (1901). On lines and planes of closest fit to systems of points in space. Hotelling, H. (1933). Analysis of a complex of statistical variables into principal components.

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. 5, 2025, 7:09 p.m.