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

In clustMD: Model Based Clustering for Mixed Data

Description

Internal function.

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

N	number of observations.
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.
Y	an N x J data matrix.
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.
pi.vec	mixing weights.
model	the covariance model fitted to the data.
perc.cut	threshold parameters.

Value

Output required for clustMD function.

See Also

clustMD

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