Performs a principal component analysis for datasets of type rmult.
# 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"))
a rmult-dataset
Further arguments to call princomp.default
logical: shall the computation be based on correlations rather than covariances?
logical: shall scores be computed?
provides the covariance matrix to be used for the principle component analysis
provides the be used for the computation of scores
A rowindex to x giving the columns that should be used to estimate the variance.
Gives the robustness type for the calculation of the
covariance matrix. See var.rmult
for details.
An object of type princomp
with the following fields
the standard deviation of the principal components.
the matrix of variable loadings (i.e., a matrix whose
columns contain the eigenvectors). This is of class
"loadings"
.
the mean that was substracted from the data set
the scaling applied to each variable
number of observations
if scores = TRUE
, the scores of the supplied data
on the principal components. Scores are coordinates in a basis given by the
principal components.
the matched call
Not clearly understood
The function just does princomp(unclass(x),…,scale=scale)
and is only here for convenience.
# NOT RUN {
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))
# }
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