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