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

In clustMD: Model Based Clustering for Mixed Data

Description

Internal function.

Usage

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

clustMD documentation built on May 2, 2019, 2:09 a.m.