| mclust.options {mclust} | R Documentation |
Set or retrieve default values for use with MCLUST package.
mclust.options(...)
... |
one or more arguments provided in the |
mclust.options is provided for assigning values to the .mclust
variable list, which is used to supply default values to various functions
in MCLUST.
Available options are:
emModelNamesA vector of 3-character strings that are associated with multivariate
models for which EM estimation is available in MCLUST.
The current default is all of the multivariate mixture models
supported in MCLUST.
The help file for mclustModelNames describes the
available models.
hcModelNamesA vector of character strings associated with multivariate models for
which model-based hierarchical clustering is available in MCLUST.
The available models are the following:
"EII" = spherical, equal volume
"EEE" = ellipsoidal, equal volume, shape, and orientation
"VII" = spherical, unequal volume
"VVV" = ellipsoidal, varying volume, shape, and orientation.
The last model in this list is used as default for initialization of
EM-algorithm.
hcUseA string specifying the type of input variables to be used for
model-based hierarchical clustering to start the EM-algorithm.
Possible values are:
"VARS" = original variables (default);
"STD" = standardized variables;
"SPH" = sphered variables (centered, scaled, uncorrelated)
computed using SVD;
"PCS" = principal components computed using SVD on centered
variables (i.e. using the covariance matrix);
"PCR" = principal components computed using SVD on standardized
(center and scaled) variables (i.e. using the correlation matrix);
"SVD" = scaled SVD transformation.
bicPlotSymbolsA vector whose entries correspond to graphics symbols for plotting the
BIC values output from Mclust and mclustBIC.
These are displayed in the legend which appears at the lower right
of the BIC plots.
bicPlotColorsA vector whose entries correspond to colors for plotting the
BIC curves from output from Mclust and
mclustBIC.
These are displayed in the legend which appears at the lower right
of the BIC plots.
classPlotSymbolsA vector whose entries are either integers corresponding to graphics symbols or single characters for indicating classifications when plotting data. Classes are assigned symbols in the given order.
classPlotColorsA vector whose entries correspond to colors for indicating classifications when plotting data. Classes are assigned colors in the given order.
warnA logical value indicating whether or not to issue certain warnings.
Most of these warnings have to do with situations in which
singularities are encountered.
The default is warn = FALSE.
The parameter values set via a call to this function will remain in effect for the rest of the session, affecting the subsequent behaviour of the functions for which the given parameters are relevant.
If the argument list is empty the function returns the current list of values. If the argument list is not empty, the returned list is invisible.
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, A. E. Raftery, T. B. Murphy and L. Scrucca (2012). mclust Version 4 for R: Normal Mixture Modeling for Model-Based Clustering, Classification, and Density Estimation. Technical Report No. 597, Department of Statistics, University of Washington.
Mclust,
MclustDA,
densityMclust,
emControl
opt <- mclust.options() # save default values
irisBIC <- mclustBIC(iris[,-5])
summary(irisBIC, iris[,-5])
mclust.options(emModelNames = c("EII", "EEI", "EEE"))
irisBIC <- mclustBIC(iris[,-5])
summary(irisBIC, iris[,-5])
mclust.options(opt) # restore default values
mclust.options()
oldpar <- par(mfrow = c(2,1), no.readonly = TRUE)
n <- with(mclust.options(),
max(sapply(list(bicPlotSymbols, bicPlotColors),length)))
plot(seq(n), rep(1,n), ylab = "", xlab = "", yaxt = "n",
pch = mclust.options("bicPlotSymbols"),
col = mclust.options("bicPlotColors"))
title("mclust.options(\"bicPlotSymbols\") \n mclust.options(\"bicPlotColors\")")
n <- with(mclust.options(),
max(sapply(list(classPlotSymbols, classPlotColors),length)))
plot(seq(n), rep(1,n), ylab = "", xlab = "", yaxt = "n",
pch = mclust.options("classPlotSymbols"),
col = mclust.options("classPlotColors"))
title("mclust.options(\"classPlotSymbols\") \n mclust.options(\"classPlotColors\")")
par(oldpar)