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bayesm (version 3.1-6)

llmnp: 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)

Value

Value of log-likelihood (sum of log prob of observed multinomial outcomes)

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

Warning

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

Author

Peter Rossi, Anderson School, UCLA, perossichi@gmail.com.

Details

X is (p1)nxk matrix. Use createX with DIFF=TRUE to create X.

Model for each obs: w=Xbeta+e with e N(0,Sigma).

Censoring mechanism:
if y=j(j<p),wj>max(wj) and wj>0
if y=p,w<0

To use GHK, we must transform so that these are rectangular regions e.g. if y=1,w1>0 and w1w1>0.

Define Aj such that if j=1,,p1 then Ajw=Ajmu+Aje>0 is equivalent to y=j. Thus, if y=j, we have Aje>Ajmu. Lower truncation is Ajmu and cov=AjSigmat(Aj). For j=p, e<mu.

References

For further discussion, see Chapters 2 and 4, Bayesian Statistics and Marketing by Rossi, Allenby, and McCulloch.

See Also

createX, rmnpGibbs

Examples

Run this code
if (FALSE) ll=llmnp(beta,Sigma,X,y,r)

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