# biplot.princomp {stats}

### Usage

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

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

Documentation reproduced from R 3.0.2. License: GPL-2.