diagPlot

0th

Percentile

Diagnostic plot for PCA

Make diagnostic plot using the output from robpca or rospca.

Keywords
plot, robust
Usage
diagPlot(res, title = "Robust PCA", col = "black", pch = 16, labelOut = TRUE, id = 3)
Arguments
res

A list containing the orthogonal distances (od), the score distances (sd) and their respective cut-offs (cutoff.od and cutoff.sd). Output from robpca or rospca can for example be used.

title

Title of the plot, default is "Robust PCA".

col

Colour of the points in the plot, this can be a single colour for all points or a vector specifying the colour for each point. The default is "black".

pch

Plotting characters or symbol used in the plot, see points for more details. The default is 16 which corresponds to filled circles.

labelOut

Logical indicating if outliers should be labelled on the plot, default is TRUE.

id

Number of OD outliers and number of SD outliers to label on the plot, default is 3.

Details

The diagnostic plot contains the score distances on the x-axis and the orthogonal distances on the y-axis. To detect outliers, cut-offs for both distances are added, see Hubert et al. (2005).

References

Hubert, M., Rousseeuw, P. J., and Vanden Branden, K. (2005), ``ROBPCA: A New Approach to Robust Principal Component Analysis,'' Technometrics, 47, 64--79.

Aliases
  • diagPlot
Examples
# NOT RUN {
X <- dataGen(m=1, n=100, p=10, eps=0.2, bLength=4)$data[[1]]

resR <- robpca(X, k=2, skew=FALSE)
diagPlot(resR)
# }
Documentation reproduced from package rospca, version 1.0.4, License: GPL (>= 2)

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