PCA: Principal Component Analysis (PCA)

View source: R/PCA_class.R

PCAR 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)

Inheritance

A PCA object inherits the following struct classes:

⁠[PCA]⁠ >> ⁠[model]⁠ >> ⁠[struct_class]⁠

Examples

M = PCA(
      number_components = 2)


computational-metabolomics/structToolbox documentation built on July 5, 2024, 12:18 p.m.