Summarizing Classification Method

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Description

summary method for class "upclassfit"

Usage

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## S3 method for class 'upclassfit'
summary(object, ...)

Arguments

object

an object of class "upclassfit", the result of a call to upclassify or noupclassify.

...

further arguments passed to or from other methods.

Details

summary.upclassfit gives a fuller output than print.upclassfit. Any of the members of the list can be called using the names listed in the helptext for upclassify or noupclassify.

Value

Model Name

A character string identifying the model (same as the input argument).

Log Likelihood

The log-likelihood for the data in the mixture model.

Dimension

The dimension of the data.

Ntrain

The number of observations in the training data.

Ntest

The number of observations in the test data.

bic

The Bayesian Information Criterion for the best model.

misclass

The number of misclassified observations (displayed only if labels are provided for the test data).

rate

The percentage of misclassified observations(displayed only if labels are provided for the test data).

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 97:611-631.

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.

See Also

upclassify, noupclassify

Examples

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data(wine, package = "gclus")
X <- as.matrix(wine[, -1])
cl <- as.matrix(wine[, 1])
indtrain <- sort(sample(1:178, 120))
indtest <- setdiff(1:178, indtrain)

fitup <- upclassify(X[indtrain,], cl[indtrain], X[indtest,], cl[indtest])
summary(fitup)