ObsLogLikelihood: Approximates the observed log likelihood.
In clustMD: Model Based Clustering for Mixed Data

ObsLogLikelihood

R Documentation

Approximates the observed log likelihood.

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
)

`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.

Output required for clustMD function.