momMix: Compute Posterior Expectation of Normal Mixture Model Moments

In bayesm: Bayesian Inference for Marketing/Micro-Econometrics

Compute Posterior Expectation of Normal Mixture Model Moments

Description

momMix averages the moments of a normal mixture model over MCMC draws.

Usage

momMix(probdraw, compdraw)

Arguments

probdraw	R x ncomp list of draws of mixture probs
compdraw	list of length R of draws of mixture component moments

Details

R	is the number of MCMC draws in argument list above.
ncomp	is the number of mixture components fitted.
compdraw	is a list of lists of lists with mixture components.
compdraw[[i]]	is ith draw.
compdraw[[i]][[j]][[1]]	is the mean parameter vector for the jth component, ith MCMC draw.
compdraw[[i]][[j]][[2]]	is the UL decomposition of \Sigma^{-1} for the jth component, ith MCMC draw

Value

A list containing:

mu	posterior expectation of mean
sigma	posterior expectation of covariance matrix
sd	posterior expectation of vector of standard deviations
corr	posterior expectation of correlation matrix

Warning

This routine is a utility routine that does not check the input arguments for proper dimensions and type.

Author(s)

Peter Rossi, Anderson School, UCLA, [e-mail].

References

For further discussion, see Chapter 5, Bayesian Statistics and Marketing by Rossi, Allenby, and McCulloch.

See Also

rmixGibbs

bayesm documentation built on Sept. 24, 2023, 1:07 a.m.