predict.MclustDR: Classify multivariate observations on a dimension reduced...

In mclust: Gaussian Mixture Modelling for Model-Based Clustering, Classification, and Density Estimation

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 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.

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)

mclust documentation built on Nov. 16, 2023, 5:10 p.m.