Unless this is a homework problem, you would be much better off using glm().
Giovanni > Date: Fri, 30 Oct 2009 12:23:45 -0700 > From: parkbomee <bbom...@hotmail.com> > Sender: r-help-boun...@r-project.org > Importance: Normal > Precedence: list > > > --Boundary_(ID_/D+lL9iK1qLhrkPBeoxH+Q) > Content-type: text/plain > Content-transfer-encoding: 8BIT > Content-disposition: inline > Content-length: 1692 > > > Hi all, > > I am trying to estimate a simple logit model. > By using MLE, I am maximizing the log likelihood, with optim(). > The thing is, each observation has different set of choice options, so I need > a loop inside the objective function, > which I think slows down the optimization process. > > The data is constructed so that each row represent the characteristics for > one alternative, > and CS is a variable that represents choice situations. (say, 1 ~ Number of > observations) > cum_count is the ¡°cumulative¡± count of each choice situations, i.e. number > of available alternatives in each CS. > So I am maximizing the sum of [exp(U(chosen)) / sum(exp(U(all alternatives)))] > > When I have 6,7 predictors, the running time is about 10 minutes, and it > slows down exponentially as I have more predictors. (More theta¡¯s to > estimate) > I want to know if there is a way I can improve the running time. > Below is my code.. > > simple_logit = function(theta){ > realized_prob = rep(0, max(data$CS)) > theta_multiple = as.matrix(data[,4:35]) %*% as.matrix(theta) > realized_prob[1] = exp(theta_multiple[1]) / > sum(exp(theta_multiple[1:cum_count[1]])) > for (i in 2:length(realized_prob)){ > realized_prob[i] = > exp(theta_multiple[cum_count[(i-1)]+1]) / > sum(exp(theta_multiple[((cum_count[(i-1)]+1):cum_count[i])])) > } > -sum(log(realized_prob)) > } > > initial = rep(0,32) > out33 = optim(initial, simple_logit, method="BFGS", hessian=TRUE) > > > > Many thanks in advance!!! > _________________________________________________________________ > > > [[alternative HTML version deleted]] > > > --Boundary_(ID_/D+lL9iK1qLhrkPBeoxH+Q) > MIME-version: 1.0 > Content-type: text/plain; charset=us-ascii > Content-transfer-encoding: 7BIT > Content-disposition: inline > > ______________________________________________ > R-help@r-project.org mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code. > > --Boundary_(ID_/D+lL9iK1qLhrkPBeoxH+Q)-- > > ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.