Plots for model-based clustering results, such as BIC, classification, uncertainty and density.

```
# S3 method for Mclust
plot(x, what = c("BIC", "classification", "uncertainty", "density"),
dimens = NULL, xlab = NULL, ylab = NULL,
addEllipses = TRUE, main = FALSE, ...)
```

- x
Output from

`Mclust`

.- what
A string specifying the type of graph requested. Available choices are:

`"BIC"`

plot of BIC values used for choosing the number of clusters.

`"classification"`

=a plot showing the clustering. For data in more than two dimensions a pairs plot is produced, followed by a coordinate projection plot using specified

`dimens`

. Ellipses corresponding to covariances of mixture components are also drawn if`addEllipses = TRUE`

.`"uncertainty"`

a plot of classification uncertainty. For data in more than two dimensions a coordinate projection plot is drawn using specified

`dimens`

.`"density"`

a plot of estimated density. For data in more than two dimensions a matrix of contours for coordinate projection plot is drawn using specified

`dimens`

.

If not specified, in interactive sessions a menu of choices is proposed.

A vector of integers specifying the dimensions of the coordinate projections
in case of `"classification"`

, `"uncertainty"`

, or `"density"`

plots.

Optional labels for the x-axis and the y-axis.

A logical indicating whether or not to add ellipses with axes
corresponding to the within-cluster covariances in case of
`"classification"`

or `"uncertainty"`

plots.

A logical or `NULL`

indicating whether or not to add a title
to the plot identifying the type of plot drawn.

Other graphics parameters.

For more flexibility in plotting, use `mclust1Dplot`

,
`mclust2Dplot`

, `surfacePlot`

, `coordProj`

, or
`randProj`

.

`Mclust`

,
`plot.mclustBIC`

,
`plot.mclustICL`

,
`mclust1Dplot`

,
`mclust2Dplot`

,
`surfacePlot`

,
`coordProj`

,
`randProj`

.

```
# \donttest{
precipMclust <- Mclust(precip)
plot(precipMclust)
faithfulMclust <- Mclust(faithful)
plot(faithfulMclust)
irisMclust <- Mclust(iris[,-5])
plot(irisMclust)
# }
```

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