`mclust2Dplot.Rd`

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, ...)
```

- 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.

- z
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.- classification
A numeric or character vector representing a classification of observations (rows) of

`data`

. If present argument`z`

will be ignored.- truth
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.- uncertainty
A numeric vector of values in

*(0,1)*giving the uncertainty of each data point. If present argument`z`

will be ignored.- what
Choose from one of the following three options:

`"classification"`

(default),`"error"`

,`"uncertainty"`

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

- fillEllipses
A logical specifying whether or not to fill ellipses with transparent colors when

`addEllipses = TRUE`

.- symbols
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")`

.- colors
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")`

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

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

- scale
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`

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

- PCH
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 ".".

- main
A logical variable or

`NULL`

indicating whether or not to add a title to the plot identifying the dimensions used.- swapAxes
A logical variable indicating whether or not the axes should be swapped for the plot.

- ...
Other graphics parameters.

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

```
# \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)
# }
```