Description Usage Arguments Value Author(s) References See Also Examples
This function performs upclassifymodel
over a range of different models and finds the model that best fits the data by comparing the BIC values.
1 2 3 |
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 non-negative number, with default 10^-5, which is a measure of how strictly convergence is defined. |
iterlim |
A non-negative 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 |
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 log-likelihood of the data. |
bic |
The Bayes information criterion for the specified model. |
Niamh Russell
C. Fraley and A.E. Raftery (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.
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), 1-14.
upclassifymodel
, modelvec
, Aitken
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?
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