princomp.rmult {compositions}R Documentation

Principal component analysis for real data

Description

Performs a principal component analysis for datasets of type rmult.

Usage

## S3 method for class '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"))

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.

Details

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

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

Author(s)

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

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))

[Package compositions version 1.01-1 Index]