PCA: Principal Components Analysis

In Rkabacoff/factorAnalysis: Principal Components, Factor Analysis, and Clustering Tools

Principal Components Analysis

Description

PCA performs a principal components analysis

Usage

PCA(data, nfactors = NULL, rotate = "none", digits = 2, ...)

Arguments

data	a data frame or correlation matrix.
nfactors	nuber of factors to extract.
rotate	factor rotation to perform.
digits	number of digits to retain.
...	parameters passed to the psych::principal function.

Details

The PCA function is a wrapper for the psych::principal function. Component rotations include none, varimax, and promax.

Value

returns a list with 5 components:

call	the call
loadings	structure matrix
variance	variance accounted for
phi	component intercorrelations for oblique rotations
scores	component scores if factors are extracted from a data frame

Examples

fit.pca <- PCA(Harman74.cor$cov, nfactors=4, rotate="varimax")

Rkabacoff/factorAnalysis documentation built on March 21, 2022, 3:15 p.m.