R: Diagnostics plot for PCA

pcaDiagplot {chemometrics} | R Documentation

## Diagnostics plot for PCA

### Description

Score distances and orthogonal distances are computed and plotted.

### Usage

    pcaDiagplot(X, X.pca, a = 2, quantile = 0.975, scale = TRUE, plot = TRUE, ...)

### Arguments

`X` | numeric data frame or matrix
`X.pca` | PCA object resulting e.g. from `princomp`
`a` | number of principal components
`quantile` | quantile for the critical cut-off values
`scale` | if TRUE then X will be scaled - and X.pca should be from scaled data too
`plot` | if TRUE a plot is generated
`...` | additional graphics parameters, see `par`

### Details

The score distance measures the outlyingness of the onjects within the PCA space using Mahalanobis distances. The orthogonal distance measures the distance of the objects orthogonal to the PCA space. Cut-off values for both distance measures help to distinguish between outliers and regular observations.

### Value

`SDist` | Score distances
`ODist` | Orthogonal distances
`critSD` | critical cut-off value for the score distances
`critOD` | critical cut-off value for the orthogonal distances

### Author(s)

Peter Filzmoser <P.Filzmoser@tuwien.ac.at>

### References

K. Varmuza and P. Filzmoser: Introduction to Multivariate Statistical Analysis in Chemometrics. CRC Press, Boca Raton, FL, 2009.

### See Also

`princomp`

### Examples

    data(glass)
    require(robustbase)
    glass.mcd <- covMcd(glass)
    rpca <- princomp(glass,covmat=glass.mcd)
    res <- pcaDiagplot(glass,rpca,a=2)

[Package chemometrics version 1.4.2 Index]