# diagPlot

##### Diagnostic plot for PCA

Make diagnostic plot using the output from `robpca`

or `rospca`

.

##### 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.

##### 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)*