[R-sig-ME] Status of the development package lme4a

[Adam D. I. Kramer]

At risk of annoying people who don't want to hear about a package they can't
use in order to help some other people be ABLE to use the package, for those
who may be new to R-Forge and have trouble getting ahold of lme4a, here is
the method:

svn checkout svn://svn.r-forge.r-project.org/svnroot/lme4

...lme4a is part of the r-forge lme4 package, so if you try the standard
means of installing r-forge packages (e.g.,
install.packages("lme4a",repos="http://r-forge.r-project.org") or the above
but using lme4a instead of lme4, or clicking on the "download lme4a source"
link from the lme4 r-forge group), you will fail.

Hope this helps others suffer less.

--Adam

On Fri, 25 Jun 2010, Douglas Bates wrote:

> I have often referred to the development version of the lme4 package,
> called lme4a.  At the risk of annoying people who don't want to hear
> more about a package that they can't yet use, I provide this update.
>
> The sources for lme4a are available from the SVN archive on R-forge
> but binary packages are not.  I hope that will change in the near
> future.
>
> I have switched to using the marvelous Rcpp package created by Dirk
> Eddelbuettel and Romain Fran?ois, which I heartily recommend to those
> writing C++/C code to be loaded into R.  Recently Romain has been on a
> "code rant" creating "syntactic sugar" that makes it much easier to
> write expressions using R vectors in C++ and it has just been too
> tempting for me to use these capabilities.  That is why binary
> packages are not available. Some of the code in the lme4a package
> depends on the "Rcpp du jour", more or less, and doesn't build on
> systems like win-builder or R-forge because of that dependency.  When
> Dirk and Romain are ready to release Rcpp_0.8.3 to CRAN we'll be able
> to pursue making binary packages available.
>
> Another change from lme4 to lme4a is the use of the bobyqa optimizer
> from the minqa package, instead of the nlminb optimizer.  Generally I
> have been pleased with the results from bobyqa but I am always on the
> lookout for good optimizers that will handle nonlinear objective
> functions subject to box constraints on the parameters.  The lme4a
> code is constructed so that the user can create a function to evaluate
> the deviance without doing the actual optimization to get the
> parameter estimates.  This allows for experimentation with other
> optimizers.  At this summer's useR! conference Stefan Theussl, Kurt
> Hornik and David Meyer will talk about their R Optimization
> Infrastructure package and I look forward to perhaps writing a generic
> interface to several different optimizers through that.  (Note to
> Stefan et al: and I would also like to write the interface glue for
> the optimizers in the minqa package for ROI, once you document what
> must be written.)
>
> Generally I am pleased with both the quality of the results and the
> speed of the package.  For glmer and nlmer there are two optimizations
> - the first involving only the variance-component parameters and the
> second involving the variance component parameters and the fixed
> effects.  A value of 0 for the optional argument nAGQ suppresses the
> second optimization, which can take much longer than the first.  In
> many cases the second optimization doesn't improve the result much but
> I have seen cases where the result from the second optimization is
> considerably better than that from the first.  (It should always be at
> least as good as the first because the converged values from the first
> optimization are used as the starting values for the second.)  I
> enclose an example where there is a big difference. This is a slight
> modification of the R code in Doran, Bates, Bliese and Dowling
> (http://www.jstatsoft.org/v20/i02).  The good news is that the results
> from these model fits are better than the results quoted in that paper
> (the bad news is that we should now post a correction).
>
> As I mentioned in a thread started by Dave Atkins, the optional
> argument nAGQ to glmer and nlmer can be given the value 0, in which
> case a faster algorithm that iterates over the variance-component
> parameters only is used.
>