llmnp(bayesm)R Documentation

Evaluate Log Likelihood for Multinomial Probit Model

Description

llmnp evaluates the log-likelihood for the multinomial probit model.

Usage

llmnp(X, y, beta, Sigma, r)

Arguments

X X is n*(p-1) x k array. X is from differenced system.
y y is vector of n indicators of multinomial response (1, ..., p).
beta k x 1 vector of coefficients
Sigma (p-1) x (p-1) Covariance matrix of errors
r number of draws used in GHK

Details

X is (p-1)*n x k matrix. Use createX with DIFF=TRUE to create X.

Model for each obs: w = Xbeta + e. e ~N(0,Sigma)
censoring mechanism:
if y=j (j<p), w_j > max(w_-j) and w_j >0
if y=p, w < 0

To use GHK, we must transform so that these are rectangular regions e.g. if y=1, w_1 > 0 and w_1 - w_-1 > 0. Define Aj such that if j=1,...,p-1, Ajw = Ajmu + Aje > 0 is equivalent to y=j implies Aje > -Ajmu. Lower truncation is -Ajmu and cov = AjSigma t(Aj). For p, e < - mu

Value

value of log-likelihood (sum of log prob of observed multinomial outcomes).

Warning

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

Author(s)

Peter Rossi, Graduate School of Business, University of Chicago, Peter.Rossi@ChicagoGsb.edu.

References

For further discussion, see Bayesian Statistics and Marketing by Allenby, McCulloch, and Rossi, Chapters 2 and 4.
http://gsbwww.uchicago.edu/fac/peter.rossi/research/bsm.html

See Also

createX, rmnpGibbs

Examples

##
## Not run: ll=llmnp(X,y,beta,Sigma,r)

[Package bayesm version 0.0 Index]