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compositions (version 1.01-1)

biplot3D: Three-dimensional biplots, based on package rgl

Description

Plots variables and cases in the same plot, based on a principal component analysis.

Usage

biplot3D(x,...)
## S3 method for class 'default':
biplot3D(x,y,var.axes=TRUE,col=c("green","red"),cex=c(2,2),
            xlabs = NULL, ylabs = NULL, expand = 1,arrow.len = 0.1,
            ...,add=FALSE)
 ## S3 method for class 'princomp':
biplot3D(x,choices=1:3,scale=1,...,
            comp.col=par("fg"),comp.labs=paste("Comp.",1:3),
            scale.scores=lambda[choices]^(1-scale),
            scale.var=scale.comp, scale.comp=sqrt(lambda[choices]), 
            scale.disp=1/scale.comp)

Arguments

x
princomp object or matrix of point locations to be drawn (typically, cases)
choices
Which principal components should be used?
scale
a scaling parameter like in biplot
scale.scores
a vector giving the scaling applied to the scores
scale.var
a vector giving the scaling applied to the variables
scale.comp
a vector giving the scaling applied to the unit length of each component
scale.disp
a vector giving the scaling of the display in the directions of the components
comp.col
color to draw the axes of the components
comp.labs
labels for the components
...
further plotting parameters as defined in rgl.material
y
matrix of second point/arrow-head locations (typically, variables)
var.axes
logical, TRUE draws arrows and FALSE points for y
col
vector/list of two elements the first giving the color/colors for the first data set and the second giving color/colors for the second data set.
cex
vector/list of two elements the first giving the size for the first data set and the second giving size for the second data set.
xlabs
labels to be plotted at x-locations
ylabs
labels to be plotted at y-locations
expand
the relative expansion of the y data set with respect to x
arrow.len
The length of the arrows as defined in arrows3D
add
logical, adding to existing plot or making a new one?

Value

  • nothing

Details

This "biplot" is a triplot, relating data, variables and principal components. The relative scaling of the components is still experimental, meant to mimic the behavior of classical biplots.

See Also

gsi

Examples

Run this code
data(SimulatedAmounts)
pc <- princomp(acomp(sa.lognormals5))
pc
summary(pc)
plot(pc)           #plot(pc,type="screeplot")
biplot3D(pc)

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