classify: Classify New Observations

In emilygoren/MixtClust: Robust Clustering for Complete and Incomplete Data

Classify New Observations

Description

Classify new, possibly incomplete, observations arising from a finite mixture of multivariate t distributions.

Usage

classify(newobs, params)

Arguments

newobs	A matrix with new observations (rows), p columns (dimensions), and missing entries set to NA.
params	A list of parmaters defining a finite mixture of multivariate t distributions, usually obtained from MixtClust (see details).

Details

Classify new observations according to the finite mixture of t distirbutions specified by the parameter values in params, a named list with elements:

• "pi"Mixing proportions. A vector of length K that sums to one.

• "nu"Degrees of freedom. A vector of length K with entries at least equal to three (thus requiring the existance of the first two moments.)

• "mu"Locations. A K \times p matrix, where the k-th row is the location μ_k \in R^p for cluster k.

• "Sigma"Dispersions. A p \times p \times K array, where the k-th slice is the p \times p positive-definite disperion matrix Σ_k for cluster k.

Value

A vector classify each observation to the cluster 1, …, K with the highest posterior probability.

Author(s)

Emily Goren, [e-mail] based on modifications of code by Ranjan Maitra.

emilygoren/MixtClust documentation built on March 19, 2022, 2 p.m.