R: Robust Covariance Matrix Estimation

covPCA {pcaPP} | R Documentation

## Robust Covariance Matrix Estimation

### Description

computes the robust covariance matrix using the `PCAgrid` and `PCAproj` functions.

### Usage

    covPCAproj(x, control)
    covPCAgrid(x, control)

### Arguments

`x` | a numeric matrix or data frame which provides the data.
`control` | a list whose elements must be the same as (or a subset of) the parameters of the appropriate PCA function (`PCAgrid` or `PCAproj`).

### Details

The functions `covPCAproj` and `covPCAgrid` use the functions `PCAproj` and `PCAgrid` respectively to estimate the covariance matrix of the data matrix `x`.

### Value

`cov` | the actual covariance matrix estimated from `x`
`center` | the center of the data `x` that was substracted from them before the PCA algorithms were run.
`method` | a string describing the method that was used to calculate the covariance matrix estimation

### 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`, `ScaleAdv`, `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))))
      covPCAproj(x)
      # compare with classical covariance matrix:
      cov(x)

[Package pcaPP version 2.0-1 Index]