M.step: M-step of the (MC)EM algorithm
In clustMD: Model Based Clustering for Mixed Data

M.step

R Documentation

M-step of the (MC)EM algorithm

Internal function.

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

`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.

Output required for clustMD function.