Plot two-dimensional data given parameters of an MVN mixture model for the data.

```
mclust2Dplot(data, parameters = NULL, z = NULL,
classification = NULL, truth = NULL, uncertainty = NULL,
what = c("classification", "uncertainty", "error"),
addEllipses = TRUE, fillEllipses = mclust.options("fillEllipses"),
symbols = NULL, colors = NULL,
xlim = NULL, ylim = NULL, xlab = NULL, ylab = NULL,
scale = FALSE, cex = 1, PCH = ".",
main = FALSE, swapAxes = FALSE, ...)
```

A plot showing the data, together with the location of the mixture components, classification, uncertainty, and/or classification errors.

- data
A numeric matrix or data frame of observations. Categorical variables are not allowed. If a matrix or data frame, rows correspond to observations and columns correspond to variables. In this case the data are two dimensional, so there are two columns.

- parameters
A named list giving the parameters of an

*MCLUST*model, used to produce superimposing ellipses on the plot. The relevant components are as follows:`pro`

Mixing proportions for the components of the mixture. There should one more mixing proportion than the number of Gaussian components if the mixture model includes a Poisson noise term.

`mean`

The mean for each component. If there is more than one component, this is a matrix whose kth column is the mean of the

*k*th component of the mixture model.`variance`

A list of variance parameters for the model. The components of this list depend on the model specification. See the help file for

`mclustVariance`

for details.

A matrix in which the `[i,k]`

th entry gives the
probability of observation *i* belonging to the *k*th class.
Used to compute `classification`

and
`uncertainty`

if those arguments aren't available.

A numeric or character vector representing a classification of
observations (rows) of `data`

. If present argument `z`

will be ignored.

A numeric or character vector giving a known
classification of each data point.
If `classification`

or `z`

is also present,
this is used for displaying classification errors.

A numeric vector of values in *(0,1)* giving the
uncertainty of each data point. If present argument `z`

will be ignored.

Choose from one of the following three options: `"classification"`

(default), `"error"`

, `"uncertainty"`

.

A logical indicating whether or not to add ellipses with axes corresponding to the within-cluster covariances.

A logical specifying whether or not to fill ellipses with transparent
colors when `addEllipses = TRUE`

.

Either an integer or character vector assigning a plotting symbol to each
unique class in `classification`

. Elements in `colors`

correspond to classes in order of appearance in the sequence of
observations (the order used by the function `unique`

).
The default is given by `mclust.options("classPlotSymbols")`

.

Either an integer or character vector assigning a color to each
unique class in `classification`

. Elements in `colors`

correspond to classes in order of appearance in the sequence of
observations (the order used by the function `unique`

).
The default is given is `mclust.options("classPlotColors")`

.

Optional argument specifying bounds for the ordinate, abscissa of the plot. This may be useful for when comparing plots.

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

A logical variable indicating whether or not the two chosen
dimensions should be plotted on the same scale, and
thus preserve the shape of the distribution.
Default: `scale=FALSE`

An argument specifying the size of the plotting symbols. The default value is 1.

An argument specifying the symbol to be used when a classificatiion has not been specified for the data. The default value is a small dot ".".

A logical variable or `NULL`

indicating whether or not to add a title
to the plot identifying the dimensions used.

A logical variable indicating whether or not the axes should be swapped for the plot.

Other graphics parameters.

`surfacePlot`

,
`clPairs`

,
`coordProj`

,
`mclust.options`

```
# \donttest{
faithfulModel <- Mclust(faithful)
mclust2Dplot(faithful, parameters=faithfulModel$parameters,
z=faithfulModel$z, what = "classification", main = TRUE)
mclust2Dplot(faithful, parameters=faithfulModel$parameters,
z=faithfulModel$z, what = "uncertainty", main = TRUE)
# }
```

Run the code above in your browser using DataLab