stats (version 3.4.3)

biplot.princomp: Biplot for Principal Components

Description

Produces a biplot (in the strict sense) from the output of princomp or prcomp

Usage

# S3 method for prcomp
biplot(x, choices = 1:2, scale = 1, pc.biplot = FALSE, …)
# S3 method for princomp
biplot(x, choices = 1:2, scale = 1, pc.biplot = FALSE, …)

Arguments

x

an object of class "princomp".

choices

length 2 vector specifying the components to plot. Only the default is a biplot in the strict sense.

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.

Side Effects

a plot is produced on the current graphics device.

Details

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.

References

Gabriel, K. R. (1971). The biplot graphical display of matrices with applications to principal component analysis. Biometrika, 58, 453--467.

Gabriel, K. R. and Odoroff, C. L. (1990). Biplots in biomedical research. Statistics in Medicine, 9, 469--485.

See Also

biplot, princomp.

Examples

# NOT RUN {
require(graphics)
biplot(princomp(USArrests))
# }