eMixMargDen

<code>eMixMargDen</code> assumes that a multivariate mixture of normals has been fitted
 via MCMC (using <code>rnmixGibbs</code>). For each MCMC draw, <code>eMixMargDen</code> computes 
 the marginal densities for each component in the multivariate mixture on a user-supplied
 grid and then averages over the MCMC draws.

multivariate

models

Covers many important models used
in marketing and micro-econometrics applications.
The package includes:
Bayes Regression (univariate or multivariate dep var),
Bayes Seemingly Unrelated Regression (SUR),
Binary and Ordinal Probit,
Multinomial Logit (MNL) and Multinomial Probit (MNP),
Multivariate Probit,
Negative Binomial (Poisson) Regression,
Multivariate Mixtures of Normals (including clustering),
Dirichlet Process Prior Density Estimation with normal base,
Hierarchical Linear Models with normal prior and covariates,
Hierarchical Linear Models with a mixture of normals prior and covariates,
Hierarchical Multinomial Logits with a mixture of normals prior
and covariates,
Hierarchical Multinomial Logits with a Dirichlet Process prior and covariates,
Hierarchical Negative Binomial Regression Models,
Bayesian analysis of choice-based conjoint data,
Bayesian treatment of linear instrumental variables models,
Analysis of Multivariate Ordinal survey data with scale
usage heterogeneity (as in Rossi et al, JASA (01)),
Bayesian Analysis of Aggregate Random Coefficient Logit Models as in BLP (see
Jiang, Manchanda, Rossi 2009)
For further reference, consult our book, Bayesian Statistics and
Marketing by Rossi, Allenby and McCulloch (Wiley first edition 2005 and second forthcoming) and Bayesian Non- and Semi-Parametric
Methods and Applications (Princeton U Press 2014).

Peter Rossi

bayesm

Bayesian Inference for Marketing/Micro-Econometrics

eMixMargDen function

<dl> <dt>grid</dt>
<dd>array of grid points, <code>grid[,i]</code> are ordinates for <code>i</code>th dimension of the density</dd> <dt>probdraw</dt>
<dd>array where each row contains a draw of probabilities of the mixture component</dd> <dt>compdraw</dt>
<dd>list of lists of draws of mixture component moments</dd></dl>

Arguments

This routine is a utility routine that does not check the input arguments for proper dimensions and type. To avoid errors, call with output from <code>rnmixGibbs</code>.

Warning

Peter Rossi, Anderson School, UCLA, <a href="/link/perossichi%40gmail.com?package=bayesm&version=3.1-6" data-mini-rdoc="bayesm::perossichi@gmail.com">perossichi@gmail.com</a>.

Author

Compute Marginal Densities of A Normal Mixture Averaged over MCMC Draws — eMixMargDen

<dl>

 <dt>grid</dt>
<dd>array of grid points, <code>grid[,i]</code> are ordinates for <code>i</code>th dimension of the density</dd>

 <dt>probdraw</dt>
<dd>array where each row contains a draw of probabilities of the mixture component</dd>

 <dt>compdraw</dt>
<dd>list of lists of draws of mixture component moments</dd>

</dl>

Peter Rossi, Anderson School, UCLA, <a href='mailto:perossichi@gmail.com'>perossichi@gmail.com</a>.

`length(compdraw)`	is the number of MCMC draws.
`compdraw[[i]]`	is a list draws of mu and of the inverse Cholesky root for each of mixture components.
`compdraw[[i]][[j]]`	is `j`th component.
`compdraw[[i]][[j]]$mu`	is mean vector.
`compdraw[[i]][[j]]$rooti`	is the UL decomp of \(\Sigma^{-1}\).

eMixMargDen: Compute Marginal Densities of A Normal Mixture Averaged over MCMC Draws

Description

Usage

Value

Arguments

Warning

Author

Details

References

See Also