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