Internal function.
E.step(
N,
G,
D,
CnsIndx,
OrdIndx,
zlimits,
mu,
Sigma,
Y,
J,
K,
norms,
nom.ind.Z,
patt.indx,
pi.vec,
model,
perc.cut
)Output required for clustMD function.
number of observations.
number of mixture components.
dimension of the latent data.
the number of continuous variables.
the sum of the number of continuous and ordinal (including binary) variables.
the truncation points for the latent data.
a D x G matrix of means.
a D x D x G array of covariance parameters.
an N x J data matrix.
the number of observed variables.
the number of levels for each variable.
a matrix of standard normal deviates.
the latent dimensions corresponding to each nominal variable.
a list of length equal to the number of observed response patterns. Each entry of the list details the observations for which that response pattern was observed.
mixing weights.
the covariance model fitted to the data.
threshold parameters.
clustMD