Obtaining the Best Model for Data Classification Using an Updated Classification Method
Description
This function performs upclassifymodel
over a range of different models and finds the model that best fits the data by comparing the BIC values.
Usage
1 2 3 
Arguments
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 of 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 
modelscope 
A character string indicating the desired models to be tested. With default NULL, all available models are tested. The models available for univariate and multivariate data are described in 
tol 
A nonnegative number, with default 10^5, which is a measure of how strictly convergence is defined. 
iterlim 
A nonnegative integer, with default 1000, which is the desired limit on the maximum number of iterations. 
Aitken 
A logical value with default TRUE which tests for convergence using Aitken acceleration. If value is set to FALSE, convergence is tested by comparing 
... 
Arguments passed to or from other methods 
Value
An object of class "upclassfit" providing a list of output components for each model in modelscope
, with the Best model (according to BIC) first.
The details of the output components are as follows
call 
How to call the function and the order of its arguments. 
Ntrain 
The number of observations in the training set. 
Ntest 
The number of observations in the test set. 
d 
The dimension of the data. 
G 
The number of groups in the training set. 
iter 
The number of iterations taken. 
converged 
Whether or not the algorithm has converged. If 
modelName 
The model considered in this run of the algorithm. 
parameters 
A list of the final model parameters estimated by the algorithm.

train 
A list of information about the training data. This will not have changed from before the run.

test 
A list of information about the test data.

ll 
The loglikelihood of the data. 
bic 
The Bayes information criterion for the specified model. 
Author(s)
Niamh Russell
References
C. Fraley and A.E. Raftery (2002). Model based 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.
Dean, N., Murphy, T.B. and Downey, G (2006). Using unlabelled data to update classification rules with applications in food authenticity studies. Journal of the Royal Statistical Society: Series C 55 (1), 114.
See Also
upclassifymodel
, modelvec
, Aitken
Examples
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15  data(iris)
X < as.matrix(iris[,5])
cl < unclass(iris[,5])
indtrain < sort(sample(1:150,110))
Xtrain < X[indtrain,]
cltrain < cl[indtrain]
indtest < setdiff(1:150, indtrain)
Xtest < X[indtest,]
cltest < cl[indtest]
modelscope < c("EII", "VII", "VEI","EVI")
fitupmodels < upclassify(Xtrain, cltrain, Xtest, cltest, modelscope)
fitupmodels$Best$modelName # What is the best model?
