1\name{mnlHess}
2\alias{mnlHess}
3\concept{multinomial logit}
4\concept{hessian}
5
6
7\title{ Computes --Expected Hessian for Multinomial Logit}
8
9\description{\code{mnlHess} computes expected Hessian (\eqn{E[H]}) for Multinomial Logit Model.}
10
11\usage{mnlHess(beta, y, X)}
12
13\arguments{
14  \item{beta}{ \eqn{k x 1} vector of coefficients }
15  \item{y}{ \eqn{n x 1} vector of choices, (\eqn{1,\ldots,p}) }
16  \item{X}{ \eqn{n*p x k} Design matrix }
17}
18\details{
19  See \code{\link{llmnl}} for information on structure of X array.  Use \code{\link{createX}} to make X.
20}
21
22\value{\eqn{k x k} matrix}
23
24\section{Warning}{
25This routine is a utility routine that does \strong{not} check the input arguments for proper dimensions and type.
26}
27
28\author{Peter Rossi, Anderson School, UCLA, \email{perossichi@gmail.com}.}
29
30\references{For further discussion, see Chapter 3, \emph{Bayesian Statistics and Marketing} by Rossi, Allenby, and McCulloch. \cr \url{http://www.perossi.org/home/bsm-1}}
31
32\seealso{ \code{\link{llmnl}}, \code{\link{createX}}, \code{\link{rmnlIndepMetrop}} }
33
34\examples{
35\dontrun{mnlHess(beta, y, X)}
36}
37
38\keyword{models}
39