princomprmult: Principal component analysis for real data

In compositions: Compositional Data Analysis

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))

compositions documentation built on June 22, 2024, 12:15 p.m.