slplot {pcaMethods} | R Documentation |
A common way of representing PCA result for two component
slplot(object, pcs=c(1,2), scoresLoadings=c(TRUE, TRUE), sl="def", ll="def", hotelling=0.95, rug=TRUE, sub=NULL,...)
object |
a pcaRes object |
pcs |
which two pcs to plot |
scoresLoadings |
Which should be shown scores and or loadings |
sl |
labels to plot in the scores plot |
ll |
labels to plot in the loadings plot |
hotelling |
confidence interval for ellipse |
rug |
logical, rug x axis or not |
sub |
Subtitle, defaults to annotate with amount of explained variance. |
... |
Further arguments to plot functions |
Uses layout instead of par to provide side-by-side so it works with Sweave.
None, used for side effect.
Henning Redestig
prcomp
, pca
, princomp
data(iris) pcIr <- pca(iris[,1:4], scale="UV", method="svd") slplot(pcIr, sl=NULL, pch=5, col=as.integer(iris[,5]))