llmnp {bayesm} | R Documentation |
Evaluate Log Likelihood for Multinomial Probit Model
Description
llmnp
evaluates the log-likelihood for the multinomial probit model.
Usage
llmnp(beta, Sigma, X, y, r)
Arguments
beta |
k x 1 vector of coefficients |
Sigma |
(p-1) x (p-1) covariance matrix of errors |
X |
n*(p-1) x k array where X is from differenced system |
y |
vector of n indicators of multinomial response (1, ..., p) |
r |
number of draws used in GHK |
Details
is
matrix. Use
createX
with DIFF=TRUE
to create .
Model for each obs: with
.
Censoring mechanism:
if and
if
To use GHK, we must transform so that these are rectangular regions
e.g. if and
.
Define such that if
then
is equivalent to
. Thus, if
, we have
. Lower truncation is
and
. For
,
.
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, Anderson School, UCLA, perossichi@gmail.com.
References
For further discussion, see Chapters 2 and 4, Bayesian Statistics and Marketing by Rossi, Allenby, and McCulloch.
See Also
Examples
## Not run: ll=llmnp(beta,Sigma,X,y,r)