Re: [R] [R-pkgs] New version of lme4 and new mailing list R-SIG-mixed-models

From: Marc Schwartz <marc_schwartz_at_comcast.net>
Date: Fri 26 Jan 2007 - 01:26:49 GMT

On Thu, 2007-01-25 at 17:12 -0600, Douglas Bates wrote:
> Version 0.9975-11 of the lme4 package has been uploaded to CRAN. The
> source package should be available on the mirrors in a day or two and
> binary packages should follow soon after.
>
> There are several changes in this release of the package. The most
> important is the availability of a development version of lmer called,
> for the time being, lmer2. At present lmer2 only fits linear mixed
> models. Generalized linear mixed models will be added "soon".
> Furthermore there is no mcmcsamp method for a model fit by lmer2.
> This deficiency will also be rectified "soon". Once I have all the
> capabilities and methods currently available for lmer also available
> for the new representation I will remove the old representation and
> rename lmer2 as lmer.
>
> The current version of lmer will continue to be available throughout
> the migration process. You don't have to change anything about your
> use of that function unless you want to try the new one. It would be
> a good idea, however, to save the data and the call to lmer in
> addition to saving an lmer object, if you so choose, so that you can
> recreate the fitted model when the development version becomes the
> release version.
>
> The package contains a vignette giving the details of the new implementation.
>
> The reason I am releasing a development version in parallel with the
> production version is because I would like feedback from useR's
> regarding the development version. In my experience, testing it
> myself and with colleagues whom I visited recently, I have found that
> lmer2 is faster and more reliable than the current lmer. In
> particular, on some difficult model fits I have been able to get
> substantially better parameter estimates (i.e. the deviance at the
> lmer2 estimates is perhaps 4 or 5 lower than that at the lmer
> estimates) with lmer2 than I could with lmer.
>
> If you have fit a linear mixed model using lmer and are willing to try
> it with lmer2 I would appreciate your telling me if the parameter
> estimates are comparable and which fit was faster (use system.time()
> to check). I'm primarily interested in models fit to large data sets
> or "difficult" fits.
>
> We have established a new mailing list, R-SIG-mixed-models, for
> discussion of R software to fit mixed-effects models, especially lmer.
> See https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models for
> information or to subscribe.
>
> I know that I have said this before but this is the last time that I
> am going to change the underlying representation. Really - trust me -
> this is the last time. My theory of software development is expressed
> in a line from an old blues song, "you just keep doing it wrong till
> you do it right". I'm convinced that this time I have it right. That
> statement sounds like "famous last words", doesn't it? :-)

Doug,

Your efforts are most appreciated.

To the points you raise in the last paragraph, I might note the following quote:

"In developing a methodology, leave room for improvement.  It is absolutely critical not to kill a field by doing too  good a job on the first outing." -

          "How to be a Highly Cited Author in the Mathematical Sciences", 
           Dr. David Donoho, Stanford University, 2002
           http://www.in-cites.com/scientists/DrDavidDonoho.html

:-)

Best regards,

Marc Schwartz



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