a numeric matrix or data frame which provides the data for the
principal components analysis.
PCobj
a PCA object resulting from PCAproj or PCAgrid
crit
quantile(s) used for the critical value(s) for OD and SD
ksel
range for the number of PCs to be used in the plot; if NULL all PCs provided are used
plot
if TRUE a plot is generated, otherwise only the values are returned
plotbw
if TRUE the plot uses gray, otherwise color representation
raw
if FALSE, the distribution of the SD will be transformed to approach chisquare
distribution, otherwise the raw values are reported and used for plotting
colgrid
the color used for the grid lines in the plot
...
additional graphics parameters as used in par
Details
Based on (robust) principal components, a diagnostics plot is made using Orthogonal Distance (OD)
and Score Distance (SD). This plot can provide important information about the multivariate
data structure.
Value
ODist
matrix with OD for each observation (rows) and each selected PC (cols)
SDist
matrix with SD for each observation (rows) and each selected PC (cols)
critOD
matrix with critical values for OD for each selected PC (rows) and each
critical value (cols)
critSD
matrix with critical values for SD for each selected PC (rows) and each
critical value (cols)
P. Filzmoser and H. Fritz (2007).
Exploring high-dimensional data with robust principal components.
In S. Aivazian, P. Filzmoser, and Yu. Kharin, editors, Proceedings of the Eighth
International Conference on Computer Data Analysis and Modeling, volume 1, pp. 43-50,
Belarusian State University, Minsk.
M. Hubert, P.J. Rousseeuwm, K. Vanden Branden (2005).
ROBCA: a new approach to robust principal component analysis
Technometrics 47, pp. 64-79.
See Also
PCAproj, PCAgrid
Examples
# multivariate data with outliers
library(mvtnorm)
x <- rbind(rmvnorm(85, rep(0, 6), diag(c(5, rep(1,5)))),
rmvnorm( 15, c(0, rep(20, 5)), diag(rep(1, 6))))
# Here we calculate the principal components with PCAgrid
pcrob <- PCAgrid(x, k=6)
resrob <- PCdiagplot(x,pcrob,plotbw=FALSE)
# compare with classical method:
pcclass <- PCAgrid(x, k=6, method="sd")
resclass <- PCdiagplot(x,pcclass,plotbw=FALSE)