mclust (version 6.1)

predict.MclustDR: Classify multivariate observations on a dimension reduced subspace by Gaussian finite mixture modeling

Description

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

Usage

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

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.

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.

Author

Luca Scrucca

References

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

See Also

MclustDR.

Examples

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)