## Principal Components Analysis

## Usage

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

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

## Arguments

`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.complete.obs"`. An unambiguous substring can be used.

## Description

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.

## Value

`prcomp` returns an list with class `"prcomp"` containing 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.

## References

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

## See Also

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

## Examples

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

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