noupclassifymodel
implements the EM algorithm to classify unlabeled data using parameter estimates derived from labeled data only. It is a background function not designed to be used directly.
1  noupclassifymodel(Xtrain, cltrain, Xtest, cltest = NULL, modelName = "EEE", ...)

Xtrain 
A numeric matrix of observations where rows correspond to observations and columns correspond to variables. The group membership of each observation is known  labeled data. 
cltrain 
A numeric vector with distinct entries representing a classification for the corresponding observations in 
Xtest 
A numeric matrix of observations where rows correspond to observations and columns correspond to variables. The group membership of each observation may not be known  unlabeled data. 
cltest 
A numeric vector with distinct entries representing a classification of the corresponding observations in 
modelName 
A character string indicating the model, with default 
... 
Arguments passed to or from other methods. 
The return value is a list with the following components:
call 
The function call from 
Ntrain 
The number of observations in the training data. 
Ntest 
The number of observations in the test data. 
d 
The dimension of the data. 
G 
The number of groups in the data. 
modelName 
A character string identifying the model (same as the input argument) 
parameters 

train/test 

ll 
The loglikelihood for the data in the mixture model. 
bic 
The Bayesian Information Criterion for the data. 
Bensmail, H. and Celeux, G. (1996). Regularized Gaussian discriminant analysis through eigenvalue decomposition. Journal of the American Statistical Association 91:17431748.
Fraley, C. and Raftery, A.E. (2002). Modelbased clustering, discriminant analysis, and density estimation. Journal of the American Statistical Association 97:611631.
Fraley, C. and Raftery, A.E. (2006). MCLUST Version for R: Normal Mixture Modeling and ModelBased Clustering, Technical Report no. 504, Department of Statistics, University of Washington.
1 2 3 4 5 6 7 8 9 10  # This function is not designed to be used on its own,
# but to be called by \code{noupclassify}
data(wine, package = "gclus")
X < as.matrix(wine[, 1])
cl < unclass(wine[, 1])
indtrain < sort(sample(1:178, 120))
indtest < setdiff(1:178, indtrain)
fitnoup < noupclassifymodel(X[indtrain,],
cl[indtrain], X[indtest,], cl[indtest])

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