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 1.6.1 Index]