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 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.
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 kth 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 kth 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.
A plot showing the data, together with the location of the mixture components, classification, uncertainty, and/or classification errors.
# NOT RUN {
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)
# }
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