

   PPrriinncciippaall CCoommppoonneennttss AAnnaallyyssiiss

        prcomp(x=, scale=FALSE, use="all.obs")

        print.prcomp(prcomp.obj)
        plot.prcomp(prcomp.obj)

   AArrgguummeennttss::

          x: a matrix (or data frame) which provides the data
             for the principal components analysis.

      scale: a logical value indicating whether the variables
             should be scaled to have unit variance before the
             analysis takes place.

        use: the strategy to use for dealing with missing
             observations.  The possible values are
             `"all.obs"', `"complete.obs"', or `"pairwise.com-
             plete.obs"'.  An unambiguous substring can be
             used.

   DDeessccrriippttiioonn::

        This function performs a principal components analysis
        on the given data matrix and returns the results as a
        `prcomp' object.  The print method for the these
        objects prints the results in a nice format and the
        plot method produces a scree plot.

   VVaalluuee::

        `prcomp' returns an list with class `"prcomp"' contain-
        ing the following components:

        var: the variances of the principal components (i.e.
             the eigenvalues)

       load: the matrix of variable loadings (i.e. a matrix
             whose columns contain the eigenvectors).

      scale: the value of the `scale' argument.

   RReeffeerreenncceess::

        Mardia, K. V., J. T. Kent and J. M. Bibby (1979).  Mul-
        tivariate Analysis, London: Academic Press.

   SSeeee AAllssoo::

        `cor', `cov', `eigen'.

   EExxaammpplleess::

        # the variances of the variables in the
        # crimes data vary by orders of magnitude
        data(crimes)
        prcomp(crimes)
        prcomp(crimes,scale=TRUE)

