# biplot.princomp

0th

Percentile

##### Biplot for Principal Components

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

Keywords
multivariate, hplot
##### 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.

##### 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.

##### Side Effects

a plot is produced on the current graphics device.

##### References

Gabriel, K. R. (1971). The biplot graphical display of matrices with applications to principal component analysis. Biometrika, 58, 453--467. 10.2307/2334381.

Gabriel, K. R. and Odoroff, C. L. (1990). Biplots in biomedical research. Statistics in Medicine, 9, 469--485. 10.1002/sim.4780090502.

biplot, princomp.
library(stats) # NOT RUN { require(graphics) biplot(princomp(USArrests)) # }