PCA: Principal Component Analysis (PCA)

In computational-metabolomics/structToolbox: Data processing & analysis tools for Metabolomics and other omics

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.