mclust2Dplot {mclust} | R Documentation |
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","errors"), quantiles = c(0.75,0.95), symbols=NULL, colors=NULL, scale=FALSE, xlim=NULL, ylim=NULL, CEX = 1, PCH = ".", identify = 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:
|
z |
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.
|
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), "errors" , "uncertainty" .
|
quantiles |
A vector of length 2 giving quantiles used in plotting uncertainty. The smallest symbols correspond to the smallest quantile (lowest uncertainty), medium-sized (open) symbols to points falling between the given quantiles, and large (filled) symbols to those in the largest quantile (highest uncertainty). The default is (0.75,0.95). |
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 is .Mclust\$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\$classPlotColors .
|
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
|
xlim, ylim |
An argument specifying bounds for the ordinate, abscissa of the plot. This may be useful for when comparing plots. |
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 ".". |
identify |
A logical variable 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.
C. Fraley and A. E. Raftery (2002). Model-based clustering, discriminant analysis, and density estimation. Journal of the American Statistical Association 97:611-631.
C. Fraley and A. E. Raftery (2006). MCLUST Version 3 for R: Normal Mixture Modeling and Model-Based Clustering, Technical Report no. 504, Department of Statistics, University of Washington.
surfacePlot
,
clPairs
,
coordProj
,
mclustOptions
faithfulModel <- mclustModel(faithful,mclustBIC(faithful)) mclust2Dplot(faithful, parameters=faithfulModel$parameters, z=faithfulModel$z, what = "classification", identify = TRUE) mclust2Dplot(faithful, parameters=faithfulModel$parameters, z=faithfulModel$z, what = "uncertainty", identify = TRUE)