pcaDiagplot {chemometrics}R Documentation

Diagnostics plot for PCA

Description

Score distances and of the orthogonal distances are computed and plotted.

Usage

pcaDiagplot(X, X.pca, a = 2, quantile = 0.975, 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
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. To appear.

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 0.4 Index]