Plot a overlaid scores and loadings plot
Visualize two-components simultaneously
## S3 method for class 'pcaRes' biplot(x, choices = 1:2, scale = 1, pc.biplot = FALSE, ...) ## S4 method for signature 'pcaRes' biplot(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
|
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 the inner products between variables approximate covariances and distances between observations approximate Mahalanobis distance. |
... |
optional arguments to be passed to
|
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)
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