mvPCA {MNM} | R Documentation |
Principal component analysis based on different score functions
mvPCA(X, score = "identity", estimate = "outer", na.action = na.fail, ...)
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. |
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.
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. |
Klaus Nordhausen, klaus.nordhausen@uta.fi
Oja, H. (2010), Multivariate Nonparametric Methods with R, Springer.
data(iris) IRIS <- iris[,1:4] iris.pca <- mvPCA(IRIS, "sign", "i") iris.pca summary(iris.pca) pairs(iris.pca$scores, col=iris[,5])