pca.calc: Principal Component Analysis

In MorphoTools2: Multivariate Morphometric Analysis

Principal Component Analysis

Description

This function performs principal component analysis.

Usage

pca.calc(object)

Arguments

object

an object of class morphodata.

Details

The pca.calc function performs an R type principal component analysis using the R base princomp function. Principal component analysis is a variable reduction procedure. It reduces original variables into a smaller number of principal components (artificial variables) that will account for most of the variance in the observed variables.

Value

an object of class pcadata with the following elements:

objects

	ID	IDs of each row of scores object.
	Population	population membership of each row of scores object.
	Taxon	taxon membership of each row of scores object.
	scores	ordination scores of cases (objects, OTUs).

eigenVectors	matrix of eigenvectors (i.e., a matrix of characters loadings).
eigenValues	eigenvalues of principal components, i.e., proportion of variation of the original dataset expressed by individual axes.
eigenvaluesAsPercent	eigenvalues as percent, percentage of their total sum.
cumulativePercentageOfEigenvalues	cumulative percentage of eigenvalues.
groupMeans	data.frame containing the means for the taxa.
rank	number of principal components.
center, scale	the centring and scaling of the input data.

Examples

data(centaurea)
centaurea = naMeanSubst(centaurea)
centaurea = removePopulation(centaurea, populationName = c("LIP", "PREL"))

pcaRes = pca.calc(centaurea)

summary(pcaRes)

plotPoints(pcaRes, axes = c(1,2), col = c("red", "green", "blue", "black"),
  pch = c(20,17,8,21), pt.bg = "orange", legend = TRUE, legend.pos = "bottomright")

MorphoTools2 documentation built on March 7, 2023, 6:18 p.m.