PCA {structToolbox}R Documentation

Principal Component Analysis (PCA)

Description

PCA is a multivariate data reduction technique. It summarises the data in a smaller number of Principal Components that maximise variance.

Usage

PCA(number_components = 2, ...)

Arguments

number_components

(numeric, integer) The number of Principal Components calculated. The default is 2.

...

Additional slots and values passed to struct_class.

Value

A PCA object with the following output slots:

scores (DatasetExperiment) A matrix of PCA scores where each column corresponds to a Principal Component.
loadings (data.frame)
eigenvalues (data.frame)
ssx (numeric)
correlation (data.frame)
that (DatasetExperiment)

Examples

M = PCA()

[Package structToolbox version 1.6.0 Index]