Hi all - Probably a fundamental misunderstanding on my part here :: but I've implemented the logit log-likelihood function as it was derived in the lecture slides (to the best of my knowledge), and am attempting to optimize the vector of coefficients (beta)......the code gives me an output in the proper format (and is consistent for any reasonable starting values for "par" in optim), but the output is not in the range of the coefficients estimated by Zelig when I use it to check the function.Am I completely missing something having to do with constraints or reparametrization here? Conceptually, I can't think of what the problem would be, yet I must be misunderstanding something here.....right? likelihood.logit <- function(beta, y, X){ likelihood.logit <- (-1)*sum(log(1+exp((1-2*y)*(X%*%beta)))) return(likelihood.logit) } beta <- c() y <- Incident X <- as.matrix(cbind(Temperature, Pressure)) MLE.logit <- optim(par=c(.5,.5), fn=likelihood.logit, y=y, X=X, method="BFGS", control=list(fnscale=-1))$par MLE.logit Many thanks, Rachel _______________________________________________ gov2001-l mailing list gov2001-l at lists.fas.harvard.edu http://lists.fas.harvard.edu/mailman/listinfo/gov2001-l