pcaeval: Principal Component Analysis with Diagnostic Plots

In johnlpearce/sommix: Tools to Apply Self-Organizing Maps

Description

Performs principal component analysis on the data and returns the results as an object of class prcomp. Scree, variance, and loading plots are provided.

Usage

pcaeval(x, labsize = 1, colormod = NULL, varlabs = NULL, ...)

Arguments

x	a data
labsize	sets label size
colormod	can be used to customize plot color schemes. The default uses terrain palletes from grDevices
varlabs	specifies x-axis variable labels
...	arguments passed to or from other methods

Details

Assessing patterns of intercorrelations between x-variables is an important aspect in multivariate analysis. This assessment tool can assist with understanding the complexitiy of the underlying data by identifiying the important primary modes of variance that exist. In most The evaluation tool is based upon application of prcomp for stats. The

Value

a list of pca results with class "prcomp"

Examples

#NIEHS Mixtures Workshop dataset1
data(dataset1)
pcaeval(scale(dataset1[,2:9]))

johnlpearce/sommix documentation built on Jan. 7, 2021, 11:38 p.m.