Plot Methods for RIMLE Objects
Description
Plot robust modelbased clustering results: scatter plot with clustering information and cluster fit.
Usage
1 2 3 
Arguments
x 
Output from 
what 
The type of graph. It can be one of the following:

data 
The data vector, matrix or data.frame (or some
transformation of them), used for obtaining the

margins 
A vector of integers denoting the variables (numbers of columns of

cluster 
An integer denoting the cluster for which the fit
plot is returned. This is only relevant if 
... 
further arguments passed to or from other methods. 
Value
 If
what="fit"

The P

P plot (probability
probability plot) of the weighted empirical distribution function of the Mahalanobis distances of observations from clusters' centers against the target distribution. The target distribution is the Chisquare distribution with degrees of freedom equal toncol(data)
. The weights are given by the improper posterior probabilities. Ifcluster=NULL
P
P plots are produced for all clusters, otherwisecluster
selects a single P
P plot at times.  If
what="clustering"

A pairwise scatterplot with cluster memberships. Points assigned to the noise/outliers component are denoted by
'+'
.
References
Coretto, P. and C. Hennig (2015). Robust improper maximum likelihood: tuning, computation, and a comparison with other methods for robust Gaussian clustering. To appear on the Journal of the American Statistical Association. arXiv preprint at arXiv:1406.0808 with (supplement).
Coretto, P. and C. Hennig (2016). Consistency, breakdown robustness, and algorithms for robust improper maximum likelihood clustering. arXiv preprint at arXiv:1309.6895.
See Also
otrimle
Examples
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25  ## Load Swiss banknotes data
data(banknote)
x < banknote[,1]
## Perform rimle clustering with default arguments
set.seed(1)
a < rimle(data=x, G=2)
print(a)
## Plot clustering
plot(a, data=x, what="clustering")
## Plot clustering on selected margins
plot(a, data=x, what="clustering", margins=4:6)
## Plot clustering on the first two principal components
z < scale(x) %*% eigen(cor(x), symmetric=TRUE)$vectors
colnames(z) < paste("PC", 1:ncol(z), sep="")
plot(a, data=z, what="clustering", margins=1:2)
## Fit plot for all clusters
plot(a, what="fit")
## Fit plot for cluster 1
plot(a, what="fit", cluster=1)
