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

Description Usage Arguments Value See Also

Description

Internal function.

Usage

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M.step(tau, N, sumTauEz, J, OrdIndx, D, G, Y, CnsIndx, sumTauS, model, a,
  nom.ind.Z)

Arguments

tau

a N x G matrix of cluster membership probabilities.

N

number of observations.

sumTauEz

the sum across all observations of observed and expected latent continuous values mutiplied by the posterior probability of belonging to each cluster.

J

the number of variables.

OrdIndx

the sum of the number of continuous and ordinal (including binary) variables.

D

dimension of the latent data.

G

the number of mixture components.

Y

a N x J data matrix.

CnsIndx

the number of continuous variables.

sumTauS

the sum across all observations of outer product of observed and expected latent continuous values mutiplied by the posterior probability of belonging to each cluster.

model

which clustMD covariance model is fitted.

a

a G x D matrix of the entries of A.

nom.ind.Z

the latent dimensions corresponding to each nominal variable.

Value

Output required for clustMD function.

See Also

clustMD



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