clustMD (version 1.0)

E.step: E-step of the (MC)EM algorithm.

Description

Internal function.

Usage

E.step(N, G, pi.vec, Y, OrdIndx, CnsIndx, D, perc.cut, mu, Sigma, Ez, J, probs.nom, K)

Arguments

N

Number of observations.

G

Number of mixture components.

pi.vec

Mixing weights.

Y

An N x J data matrix.

OrdIndx

The number of continuous and ordinal (including binary) variables.

CnsIndx

The number of continuous variables.

D

Dimension of the latent data.

perc.cut

Threshold parameters.

mu

A D x G matrix of means.

Sigma

A D x D x G array of covariance parameters.

Ez

A N x D x G array of latent expected values.

J

The number of observed variables.

probs.nom

Probabilities of nominal responses by group.

K

The number of levels for each variable.

Value

• Output required for clustMD function.

Details

E-step: an internal function.

References

McParland, D. and Gormley, I.C. (2014). Model based clustering for mixed data: clustMD. Technical report, University College Dublin.

See Also

clustMD