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 rmultdataset 
... 
Further arguments to call 
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 
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

center 
the mean that was substracted from the data set 
scale 
the scaling applied to each variable 
n.obs 
number of observations 
scores 
if 
call 
the matched call 
na.action 
Not clearly understood 
Author(s)
K.Gerald v.d. Boogaart http://www.stat.boogaart.de
See Also
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))