Reproducing results from an old lmer fit

I haven't installed R packages from the sources before.  Looking in the e-mail help archives, it appears that this is potentially problematic; do you have a suggested link for the entire procedure? For Windows I found a very brief description at http://win-builder.r-project.org (I think I might need more detail than this).  I can do it on
either Mac or Windows OS, is one easier than the other?

RE the branches/allcoef version you mention, I looked at https://svn.r-project.org/R/branches/ and didn't see this.

My main goal is to produce the exact results I produced before since I need to extract some of the information from this model that I hadn't saved.  I'm not sure which lmer version I had installed on 5/08.  Thus, I need to attempt to install the version that was available on 5/08 and perhaps the one preceding that, and check which one matches my results.

On Thu, Feb 26, 2009 at 10:31 AM, Afshartous, David

All,

For a paper revision I'm trying to reproduce some results from an old lmer
fit with Rv2.7.1 prior to 5/28/08.  However, when I currently load Rv2.7.1
and lmer, the variance component estimates are slightly different than the
original fit; the sessionInfo() is as follows:

sessionInfo()

R version 2.7.1 (2008-06-23)
i386-apple-darwin8.10.1

locale:
en_US.UTF-8/en_US.UTF-8/C/C/en_US.UTF-8/en_US.UTF-8

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base

other attached packages:
[1] lme4_0.999375-24   Matrix_0.999375-11 lattice_0.17-8

loaded via a namespace (and not attached):
[1] grid_2.7.1  nlme_3.1-89

Thus, I assume that I need to use the same older version of lme4 and/or
Matrix which might be responsible for the difference in the results?  If
this is possible, how is this done?

Cheers,
David

PS - for whatever it's worth, if I do the fit with lme (nlme_3.1-89) under
Rv2.7.1 the results are closer to the original lmer results.

___________________________________________________

Original lmer fit from 5/08:
Model 2:
AIC  BIC logLik MLdeviance REMLdeviance
 2813 2843  -1397       2829         2795
Random effects:
 Groups     Name            Variance Std.Dev. Corr
 subject   (Intercept)      2226.3   47.183
           Drug            2132.9   46.184  -0.865
 Residual                   13673.6  116.934

Current lmer fit:
 AIC  BIC logLik deviance REMLdev
 2815 2849  -1397     2830    2795
Random effects:
 Groups     Name               Variance Std.Dev. Corr
 Patient_no (Intercept)         2165.1   46.531
           Drug.full.reverseC  1386.3   37.233  -1.000
 Residual                      13947.5  118.100

Notice the large change in the estimated correlation with very little
change in the log-likelihood or deviance.  This is an indication that
the model is over-specified.

Are you able to install R packages from the sources?  If so, you could
try the branches/allcoef version from the SVN archive.  On an
optimization problem like this it may be more successful in converging
to the global optimum instead of the local optimum.


This, by the way, is why I am always looking for better optimization
code to incorporate in R.  The code in the nlme and lme4 packages just
evaluates the log-likelihood or the REML criterion for the model at
the observed data and a proposed value of the parameters.  The actual
optimization is done by the nlminb optimizer which is based on very
old Fortran code written by David Gay.  Even though the code is old
this optimizer is, in my experience, more reliable than the optimizers
used by optim and by nlm.

It is surprisingly difficult to find good optimization code that is
covered by an open source license.  There is not a strong tradition of
open source code in the numerical analysis world.  Many users are
enthused about the ipopt library (projects.coin-or.org/Ipopt) but even
though that code is open source it depends on other software, some of
which is commercial.

The optimization in lme4 is minimization of a real-valued function of
real parameters, some of which are subject to non-negativity
constraints.  It is not an unconstrained optimization problem but the
constraints are very simple.  The objective function can be evaluated
and, in theory, the gradient can also be evaluated.  However, for
models with non-nested random effects evaluation of the gradient is
much, much more difficult and time consuming than is evaluation of the
objective function.  Thus the ideal optimizer would allow for simple
"box constraints" on the parameters and would be derivative-free or at
least allow for numeric evaluation of the gradient.  If anyone knows
of such code covered by a valid open-source license I would be
delighted to hear of it.

Current lme fit:
  AIC      BIC    logLik
 2814.611 2848.638 -1397.305

                  StdDev    Corr
(Intercept)         47.21031 (Intr)
Drug.full.reverseC  46.14014 -0.866
Residual           116.93541

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