| plot.mclustDAtrain {mclust} | R Documentation |
Plots representation of the models produced by
mclustDAtrain. For multidimensional data,
the plot is a coordinate projection and the ellipses shown correspond to
the covariance matrices.
plot.mclustDAtrain(x, data, dimens=c(1,2), symbols=NULL, colors=NULL,
scale = FALSE, xlim=NULL, ylim=NULL, CEX = 1, ...)
x |
An object produced by a call to mclustDAtrain.
|
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. |
dimens |
A vector of length 2 giving the integer dimensions of the
desired coordinate projections. The default is
c(1,2), in which the first
dimension is plotted against the second.
|
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 |
Arguments 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. |
... |
Other graphics parameters. |
A plot showing a two-dimensional coordinate projection of 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.
coordProj,
mclust1Dplot,
mclust2Dplot,
mclustOptions
odd <- seq(from = 1, to = nrow(iris), by = 2) irisTrain <- mclustDAtrain(data = iris[odd,-5], labels = iris[odd,5]) ## Not run: plot(irisTrain, iris[odd,-5]) ## End(Not run)