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

or `prcomp`

```
# 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, …)

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`

.

a plot is produced on the current graphics device.

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`

.

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.

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

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