mvPCA: Principal Component Analysis

In MNM: Multivariate Nonparametric Methods. An Approach Based on Spatial Signs and Ranks

Principal Component Analysis

Description

Principal component analysis based on different score functions

Usage

mvPCA(X, score = "identity", estimate = "outer", 
      na.action = na.fail, ...)

Arguments

X	a numeric data frame or matrix with p columns.
score	score to be used. Can be either "identity", "sign", "symmsign" or "rank".
estimate	can be "outer" or "inner".
na.action	a function which indicates what should happen when the data contain 'NA's. Default is to fail.
...	further arguments passed to or from other methods.

Details

PCA as descriped in chapter 9 of the MNM book. Note that here ALL scatter matrices used are standardized to have trace(p). This function differs from most other PCA functions in R in that it does not center the data. The 'mvPCA' class has a print, summary, plot and predict method.

Value

A list with class 'mvloc' containing the following components:

EigenV	the standardized eigenvalues.
loadings	matrix with the corresponding loadings.
scores	matrix with the principal components.
dname	name of X.
method	Which shape matrix was used for the computation.
n.obs	number of observations used.
p	number of variables.

Author(s)

Klaus Nordhausen

References

Oja, H. (2010), Multivariate Nonparametric Methods with R, Springer.

See Also

princomp, prcomp

Examples

data(iris)
IRIS <- iris[,1:4]
iris.pca <- mvPCA(IRIS, "sign", "i")
iris.pca
summary(iris.pca)
pairs(iris.pca$scores, col=iris[,5])

MNM documentation built on May 29, 2024, 8:49 a.m.