R: Compare two Covariance Matrices in Plots

plotcov {pcaPP} | R Documentation

## Compare two Covariance Matrices in Plots

### Description

allows a direct comparison of two estimations of the covariance matrix (e.g. resulting from covPC) in a plot.

### Usage

    plotcov(cov1, cov2, method1, labels1, method2, labels2, ndigits, ...)

### Arguments

`cov1` | a covariance matrix (from cov, covMcd, covPC, covPCAgrid, covPCAproj, etc.
`cov2` | a covariance matrix (from cov, covMcd, covPC, covPCAgrid, covPCAproj, etc.
`method1` | legend for ellipses of estimation method1
`method2` | legend for ellipses of estimation method2
`labels1` | legend for numbers of estimation method1
`labels2` | legend for numbers of estimation method2
`ndigits` | number of digits to use for printing covariances, by default ndigits=4
`...` | additional arguments for text or plot

### Details

Since (robust) PCA can be used to re-compute the (robust) covariance matrix, one might be interested to compare two different methods of covariance estimation visually. This routine takes as input objects for the covariances to compare the output of `cov`, but also the return objects from `covPCAgrid`, `covPCAproj`, `covPC`, and `covMcd`. The comparison of the two covariance matrices is done by numbers (the covariances) and by ellipses.

### Value

only the plot is generated

### Author(s)

Heinrich Fritz, Peter Filzmoser <P.Filzmoser@tuwien.ac.at>

### References

C. Croux, P. Filzmoser, M. Oliveira, (2007). Algorithms for Projection-Pursuit Robust Principal Component Analysis, Chemometrics and Intelligent Laboratory Systems, Vol. 87, pp. 218-225.

### See Also

`PCAgrid`, `PCAproj`, `princomp`

### Examples

      # multivariate data with outliers
      library(mvtnorm)
      x <- rbind(rmvnorm(200, rep(0, 6), diag(c(5, rep(1,5)))),
                 rmvnorm( 15, c(0, rep(20, 5)), diag(rep(1, 6))))
      plotcov(covPCAproj(x),covPCAgrid(x))

[Package pcaPP version 2.0-1 Index]