Classify multivariate observations on a dimension reduced subspace by Gaussian finite mixture modeling

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Description

Classify multivariate observations on a dimension reduced subspace estimated from a Gaussian finite mixture model.

Usage

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  ## S3 method for class 'MclustDR'
predict(object, dim = 1:object$numdir, newdata, eval.points, ...)

Arguments

object

an object of class 'MclustDR' resulting from a call to MclustDR.

dim

the dimensions of the reduced subspace used for prediction.

newdata

a data frame or matrix giving the data. If missing the data obtained from the call to MclustDR are used.

eval.points

a data frame or matrix giving the data projected on the reduced subspace. If provided newdata is not used.

...

further arguments passed to or from other methods.

Value

Returns a list of with the following components:

dir

a matrix containing the data projected onto the dim dimensions of the reduced subspace.

density

densities from mixture model for each data point.

z

a matrix whose [i,k]th entry is the probability that observation i in newdata belongs to the kth class.

uncertainty

The uncertainty associated with the classification.

classification

A vector of values giving the MAP classification.

Author(s)

Luca Scrucca

References

Scrucca, L. (2010) Dimension reduction for model-based clustering. Statistics and Computing, 20(4), pp. 471-484.

C. Fraley, A. E. Raftery, T. B. Murphy and L. Scrucca (2012). mclust Version 4 for R: Normal Mixture Modeling for Model-Based Clustering, Classification, and Density Estimation. Technical Report No. 597, Department of Statistics, University of Washington.

See Also

MclustDR.

Examples

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mod = Mclust(iris[,1:4])
dr = MclustDR(mod)
pred = predict(dr)
str(pred)

data(banknote)
mod = MclustDA(banknote[,2:7], banknote$Status)
dr = MclustDR(mod)
pred = predict(dr)
str(pred)

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