summary.bayesm.nmix

an object of class "bayesm.nmix" -- a list of lists of draws

object

optional character vector of names fo reach dimension of the density

names

number of draws to burn-in (def: .1*nrow(probdraw))

burnin

<code>summary.bayesm.nmix</code> is an S3 method to display summaries of the distribution implied
 by draws of Normal Mixture Components. Posterior means and Variance-Covariance matrices are
 displayed.Note: 1st and 2nd moments may not be very interpretable for mixtures of normals. This summary function
 can take a minute or so. The current implementation is not efficient.

  plot 

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 2005) and Bayesian Non- and Semi-Parametric
Methods and Applications (Princeton U Press 2014).

summary.bayesm.nmix: Summarize Draws of Normal Mixture Components

Description

Usage

Arguments

Details

See Also

Examples