mnlHess

beta

<code>mnlHess</code> computes -Expected[Hessian] for Multinomial Logit Model

models

bayesm 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),
  Multinomial Logit (MNL) and Multinomial Probit (MNP),
  Multivariate Probit,
  Negative Binomial (Poisson) Regression,
  Multivariate Mixtures of Normals (including clustering),
  Hierarchical Linear Models with normal prior and covariates,
  Hierarchical Linear Models with mixture of normals 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 Rossi, Allenby and McCulloch. 

Peter Rossi

bayesm

Bayesian Inference for Marketing/Micro-econometrics

mnlHess function

<ul><li>multinomial logit</li><li>hessian</li></ul>

concept

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

mnlHess: Computes -Expected Hessian for Multinomial Logit

Description

Usage

Arguments

Value

concept

Warning

Details

References

See Also

Examples