Principal component analysis for real data
Performs a principal component analysis for datasets of type rmult.
## 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"))x |
a rmult-dataset |
... |
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 |
The function just does princomp(unclass(x),...,scale=scale)
and is only here for convenience.
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 |
K.Gerald v.d. Boogaart http://www.stat.boogaart.de
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))
Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.