ObsLogLikelihood: Approximates the observed log likelihood.

In clustMD: Model Based Clustering for Mixed Data

Description

Approximates the observed log likelihood.

Usage

ObsLogLikelihood(N, CnsIndx, G, Y, mu, Sigma, pi.vec, patt.indx, zlimits, J,
  OrdIndx, probs.nom, model, perc.cut, K)

Arguments

N	the number of observations.
CnsIndx	the number of continuous variables.
G	the number of mixture components.
Y	an N x J data matrix.
mu	a D x G matrix of means.
Sigma	a D x D x G array of covariance parameters.
pi.vec	the mixing weights.
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.
zlimits	the truncation points for the latent data.
J	the number of variables.
OrdIndx	the sum of the number of continuous and ordinal (including binary) variables.
probs.nom	an array containing the response probabilities for each nominal variable for each cluster
model	the covariance model fitted to the data.
perc.cut	threshold parameters.
K	the number of levels for each variable.

Value

Output required for clustMD function.

See Also

clustMD

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