[R] fundamental guide to use of numerical optimizers?

Greg Snow Greg.Snow at imail.org
Thu Dec 15 19:05:48 CET 2011

This really depends on more than just the optimizer, a lot can depend on what the data looks like and what question is being asked.  In bootstrapping it is possible to get bootstrap samples for which there is no unique correct answer to converge to, for example if there is a category where there ends up being no data due to the bootstrap but you still want to estimate a parameter for that category then there are an infinite number of possible answers that are all equal in the likelihood so there will be a lack of convergence on that parameter.  A stratified bootstrap or semi-parametric bootstrap can be used to avoid this problem (but may change the assumptions being made as well), or you can just throw out all those samples that don't have a full answer (which could be what your presenter did).

Gregory (Greg) L. Snow Ph.D.
Statistical Data Center
Intermountain Healthcare
greg.snow at imail.org

> -----Original Message-----
> From: r-help-bounces at r-project.org [mailto:r-help-bounces at r-
> project.org] On Behalf Of Paul Johnson
> Sent: Thursday, December 15, 2011 9:38 AM
> To: R-help
> Subject: [R] fundamental guide to use of numerical optimizers?
> 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
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