pcaDiagplot: Diagnostics plot for PCA

In chemometrics: Multivariate Statistical Analysis in Chemometrics

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 <[e-mail]>

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)

chemometrics documentation built on Aug. 25, 2023, 5:18 p.m.