Usage
E.step(N, G, D, CnsIndx, OrdIndx, zlimits, mu, Sigma, Y, J, K, norms, nom.ind.Z,
patt.indx, pi.vec, model, perc.cut)
Arguments
G
number of mixture components.
D
dimension of the latent data.
CnsIndx
the number of continuous variables.
OrdIndx
the sum of the number of continuous and ordinal (including
binary) variables.
zlimits
the truncation points for the latent data.
mu
a D x G matrix of means.
Sigma
a D x D x G array of covariance parameters.
J
the number of observed variables.
K
the number of levels for each variable.
norms
a matrix of standard normal deviates.
nom.ind.Z
the latent dimensions corresponding to each nominal
variable.
patt.indx
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.
model
the covariance model fitted to the data.
perc.cut
threshold parameters.