compositions (version 2.0-8)

princomp.rmult: Principal component analysis for real data

Description

Performs a principal component analysis for datasets of type rmult.

Usage

# S3 method for rmult
princomp(x,cor=FALSE,scores=TRUE,
                   covmat=var(rmult(x[subset,]),robust=robust,giveCenter=TRUE),
                   center=attr(covmat,"center"),  subset = rep(TRUE, nrow(x)),
                   ..., robust=getOption("robust"))

Value

An object of type princomp with the following fields

sdev

the standard deviation of the principal components.

loadings

the matrix of variable loadings (i.e., a matrix whose columns contain the eigenvectors). This is of class "loadings".

center

the mean that was substracted from the data set

scale

the scaling applied to each variable

n.obs

number of observations

scores

if scores = TRUE, the scores of the supplied data on the principal components. Scores are coordinates in a basis given by the principal components.

call

the matched call

na.action

Not clearly understood

Arguments

x

a rmult-dataset

...

Further arguments to call princomp.default

cor

logical: shall the computation be based on correlations rather than covariances?

scores

logical: shall scores be computed?

covmat

provides the covariance matrix to be used for the principle component analysis

center

provides the be used for the computation of scores

subset

A rowindex to x giving the columns that should be used to estimate the variance.

robust

Gives the robustness type for the calculation of the covariance matrix. See var.rmult for details.

Author

K.Gerald v.d. Boogaart http://www.stat.boogaart.de

Details

The function just does princomp(unclass(x),...,scale=scale) and is only here for convenience.

See Also

princomp.rplus

Examples

data(SimulatedAmounts)
pc <- princomp(rmult(sa.lognormals5))
pc
summary(pc)
plot(pc) 
screeplot(pc)
screeplot(pc,type="l")
biplot(pc)
biplot(pc,choice=c(1,3))
loadings(pc)
plot(loadings(pc))
pc$sdev^2
cov(predict(pc,sa.lognormals5))