biplot.pcaRes {pcaMethods} | R Documentation |
Visualize two-components simultaneously
biplot.pcaRes(x, choices=1:2, scale=1, pc.biplot=FALSE, ...)
x |
a pcaRes object |
choices |
which two pcs to plot |
scale |
The variables are scaled by
lambda^scale and the observations are scaled
by lambda ^ (1-scale) where lambda are
the singular values as computed by princomp . Normally
0 <= scale <= 1, and a warning will be
issued if the specified 'scale' is outside this range. |
pc.biplot |
If true, use what Gabriel (1971) refers to as a "principal component biplot", with lambda = 1 and observations scaled up by sqrt(n) and variables scaled down by sqrt(n). Then inner products between variables approximate covariances and distances between observations approximate Mahalanobis distance. |
... |
optional arguments to be passed to biplot.default . |
This is a method for the generic function 'biplot'. There is
considerable confusion over the precise definitions: those of the
original paper, Gabriel (1971), are followed here. Gabriel and
Odoroff (1990) use the same definitions, but their plots actually
correspond to pc.biplot = TRUE
.
a plot is produced on the current graphics device.
Kevin Wright, Adapted from biplot.prcomp
prcomp
, pca
, princomp
data(iris) pcIr <- pca(iris[,1:4]) biplot(pcIr)