

   BBiipplloott ffoorr PPrriinncciippaall CCoommppoonneennttss

        biplot.princomp(x, choices=1:2, scale=1, pc.biplot=FALSE, ...)

   AArrgguummeennttss::

          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 vari-
             ables scaled down by sqrt(n).  Then inner products
             between variables approximate covariances and dis-
             tances between observations approximate Maha-
             lanobis distance.

        ...: optional arguments to be passed to
             `biplot.default'.

   DDeessccrriippttiioonn::

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

   DDeettaaiillss::

        This is a method for the generic function `biplot'.
        There is considerable confusion over the precise defi-
        nitions: 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'.

   SSiiddee EEffffeeccttss::

        a plot is produced on the current graphics device.

   RReeffeerreenncceess::

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

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

   SSeeee AAllssoo::

        `biplot', `princomp'.

   EExxaammpplleess::

        data(crimes)
        biplot(princomp(crimes))

