mclustDA {mclust} | R Documentation |
MclustDA training and testing.
mclustDA(train, test, pro=NULL, G=NULL, modelNames=NULL, prior=NULL, control=emControl(), initialization=NULL, warn=FALSE, verbose=FALSE, ...)
train |
A list with two named components: |
test |
A list with two named components: |
pro |
Optional prior probabilities for each class in the training data. |
G |
An integer vector specifying the numbers of mixture components
(clusters) for which the BIC is to be calculated.
The default is |
modelNames |
A vector of character strings indicating the models to be fitted
in the EM phase of clustering. The help file for
|
prior |
The default assumes no prior, but this argument allows specification of a
conjugate prior on the means and variances through the function
|
control |
A list of control parameters for EM. The defaults are set by the call
|
initialization |
A list containing zero or more of the following components:
|
warn |
A logical value indicating whether or not certain warnings (usually related to singularity) should be issued when estimation fails. The default is to suppress these warnings. |
verbose |
A logical variable telling whether or not to print an indication that the function is in the training phase, which may take some time to complete. |
... |
Catches unused arguments in indirect or list calls via |
mclustDA
combines functions mclustDAtrain
and
mclustDAtest
and their summaries. This is suitable when
all test data are available in advance, so that the training
model is only used once.
A list with the following components:
test |
A list with the following components:
|
training |
A list with the following components:
|
summary |
A data frame summarizing the |
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.
plot.mclustDA
,
mclustDAtrain
,
mclustDAtest
,
classError
n <- 250 ## create artificial data set.seed(1) triModal <- c(rnorm(n,-5), rnorm(n,0), rnorm(n,5)) triClass <- c(rep(1,n), rep(2,n), rep(3,n)) odd <- seq(from = 1, to = length(triModal), by = 2) even <- odd + 1 triMclustDA <- mclustDA(train=list(data=triModal[odd],labels=triClass[odd]), test= list(data=triModal[even],labels=triClass[even]), verbose = TRUE) names(triMclustDA) ## Not run: plot(triMclustDA, trainData = triModal[odd], testData = triModal[even]) ## End(Not run) odd <- seq(from = 1, to = nrow(cross), by = 2) even <- odd + 1 crossMclustDA <- mclustDA( train=list(data=cross[odd,-1], labels=cross[odd,1]), test= list(data=cross[even,-1],labels=cross[even,1]), verbose = TRUE) ## Not run: plot(crossMclustDA, trainData = cross[odd,-1], testData = cross[even,-1]) ## End(Not run) odd <- seq(from = 1, to = nrow(iris), by = 2) even <- odd + 1 irisMclustDA <- mclustDA(train=list(data=iris[odd,-5],labels=iris[odd,5]), test= list(data=iris[even,-5],labels=iris[even,5]), verbose = TRUE) ## Not run: plot(irisMclustDA, trainData = iris[odd,-5], testData = iris[even,-5]) ## End(Not run)