I was in a presentation of optimizations fitted with both MPlus and SAS yesterday. In a batch of 1000 bootstrap samples, between 300 and 400 of the estimations did not converge. The authors spoke as if this were the ordinary cost of doing business, and pointed to some publications in which the nonconvergence rate was as high or higher.
I just don't believe that's right, and if some problem is posed so that the estimate is not obtained in such a large sample of applications, it either means the problem is badly asked or badly answered. But I've got no traction unless I can actually do better.... Perhaps I can use this opportunity to learn about R functions like optim, or perhaps maxLik. >From reading r-help, it seems to me there are some basic tips for optimization, such as: 1. It is wise to scale the data so that all columns have the same range before running an optimizer. 2. With estimates of variance parameters, don't try to estimate sigma directly, instead estimate log(sigma) because that puts the domain of the solution onto the real number line. 3 With estimates of proportions, estimate instead the logit, for the same reason. Are these mistaken generalizations? Are there other tips that everybody ought to know? I understand this is a vague question, perhaps the answers are just in the folklore. But if somebody has written them out, I would be glad to know. -- Paul E. Johnson Professor, Political Science 1541 Lilac Lane, Room 504 University of Kansas ______________________________________________ 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.