MNM (version 1.0-4)

mvPCA: Principal Component Analysis

Description

Principal component analysis based on different score functions

Usage

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

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.

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.

Author

Klaus Nordhausen

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.

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])