R: Covariance Matrix Estimation from princomp Object

covPC {pcaPP} | R Documentation

## Covariance Matrix Estimation from princomp Object

### Description

computes the covariance matrix from a princomp object. The number of components k can be given as input.

### Usage

    covPC(x, k, method)

### Arguments

`x` | an object of class princomp.
`k` | number of PCs to use for covariance estimation (optional).
`method` | method how the PCs have been estimated (optional).

### Details

There are several possibilities to estimate the principal components (PCs) from an input data matrix, including the functions `PCAproj` and `PCAgrid`. This function uses the estimated PCs to reconstruct the covariance matrix. Not all PCs have to be used, the number k of PCs (first k PCs) can be given as input to the function.

### Value

`cov` | the estimated covariance matrix
`center` | the center of the data, as provided from the princomp object.
`method` | a string describing the method that was used to calculate the PCs.

### 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))))
      pc <- princomp(x)
      covPC(pc, k=2)

[Package pcaPP version 2.0-1 Index]