logMargDenNR

vector of log-likelihoods evaluated at length(ll) MCMC draws

<code>logMargDenNR</code> computes log marginal density using the Newton-Raftery approximation.Note: this approximation can be influenced by outliers in the vector of log-likelihoods. Use 
  with <strong>care</strong> .

distribution

bayesm covers many important models used
  in marketing and micro-econometrics applications. 
  The package includes:
  Bayes Regression (univariate or multivariate dep var),
  Multinomial Logit (MNL) and Multinomial Probit (MNP),
  Multivariate Probit,
  Negative Binomial Regression,
  Multivariate Mixtures of Normals,
  Hierarchical Linear Models with normal prior and covariates,
  Hierarchical Multinomial Logits with mixture of normals prior
     and covariates,
  Hierarchical Negative Binomial Regression Models,
  Bayesian analysis of choice-based conjoint data,
  Bayesian treatment of linear instrumental variables models,
  and
  Analyis of Multivariate Ordinal survey data with scale
     usage heterogeneity (as in Rossi et al, JASA (01)).
  For further reference, consult our book, Bayesian Statistics and
  Marketing by Allenby, McCulloch and Rossi. 

Peter Rossi

bayesm

Bayesian Inference for Marketing/Micro-econometrics

logMargDenNR function

<ul><li>Newton-Raftery approximation</li><li>bayes</li><li>marginal likelihood</li><li>density</li></ul>

concept

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

logMargDenNR: Compute Log Marginal Density Using Newton-Raftery Approx

Description

Usage

Arguments

Value

concept

Warning

References