R: Principal Component Analysis cross-validation error

PCAcv {MatrixCorrelation} | R Documentation

## Principal Component Analysis cross-validation error

### Description

PRESS values for PCA as implemented by Eigenvector and described by Bro et al. (2008).

### Usage

    PCAcv(X, ncomp)

### Arguments

`X` | `matrix` object to perform PCA on.
`ncomp` | `integer` number of components.

### Details

For each number of components predicted residual sum of squares are calculated based on leave-one-out cross-validation. The implementation ensures no over-fitting or information bleeding.

### Value

A vector of PRESS-values.

### Author(s)

Kristian Hovde Liland

### References

R. Bro, K. Kjeldahl, A.K. Smilde, H.A.L. Kiers, Cross-validation of component models: A critical look at current methods. Anal Bioanal Chem (2008) 390: 1241-1251.

### See Also

`plot.SMI` (print.SMI/summary.SMI), `RV` (RV2/RVadj), `r1` (r2/r3/r4/GCD), `allCorrelations` (matrix correlation comparison).

### Examples

    X1  <- scale( matrix( rnorm(100*300), 100,300), scale = FALSE)
    PCAcv(X1,10)

[Package MatrixCorrelation version 0.9.5 Index]