Description Usage Arguments Value References See Also Examples
View source: R/noupclassifymodel.R
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", ...)
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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 log-likelihood 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 91:1743-1748.
Fraley, C. and Raftery, A.E. (2002). Model-based clustering, discriminant analysis, and density estimation. Journal of the American Statistical Association 97:611-631.
Fraley, C. and Raftery, A.E. (2006). MCLUST Version for R: Normal Mixture Modeling and Model-Based 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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