powered by

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

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

Returns a list of with the following components:

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

dim

densities from mixture model for each data point.

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

newdata

The uncertainty associated with the classification.

A vector of values giving the MAP classification.

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

'MclustDR'

MclustDR.

the dimensions of the reduced subspace used for prediction.

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

MclustDR

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.

Luca Scrucca

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

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)

Run the code above in your browser using DataLab