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.
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.
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.