llmnl: Evaluate Log Likelihood for Multinomial Logit Model

In bayesm: Bayesian Inference for Marketing/Micro-Econometrics

Evaluate Log Likelihood for Multinomial Logit Model

Description

llmnl evaluates log-likelihood for the multinomial logit model.

Usage

llmnl(beta, y, X)

Arguments

beta	k x 1 coefficient vector
y	n x 1 vector of obs on y (1,..., p)
X	n*p x k design matrix (use createX to create X)

Details

Let \mu_i = X_i beta, then Pr(y_i=j) = exp(\mu_{i,j}) / \sum_k exp(\mu_{i,k}).
X_i is the submatrix of X corresponding to the ith observation. X has n*p rows.

Use createX to create X.

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, [e-mail].

References

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

See Also

createX, rmnlIndepMetrop

Examples

## Not run: ll=llmnl(beta,y,X)

bayesm documentation built on Sept. 24, 2023, 1:07 a.m.