Thanks Ben, this is very helpful. I've attached the comparison of results between HLM and lme4 in text here. I hope they maintain readability through the list. The fixed effects are nearly identical, while the variance components are slightly different. 

I tried installing the source version of lme4, as you suggested, but still did not have success. I did get the following error message, however, so if the -3 version is significantly different than the -2 version I was able to install then it could still be an option. 

Warning: unable to access index for repository http://lme4.r-forge.r-project.org/repos/bin/windows/contrib/3.0

  There is a binary version available (and will be installed) but the
  source version is later:
         binary       source
lme4 0.999999-2 0.99999911-3

Thanks again for your help,
Daniel

Parameter                             HLM                                            lme4

Fixed Effects                                                                            

Intercept                         251.032824 (0.102705)                      251.03099  (0.10443)

Growth                               5.911175 (0.04071)                        5.90961 (0.03776

Variance Components                                                              

Student intercept              41.38687                                          41.51581

Student slope                     0.50270                                          0.45086

Within student                   9.78905                                          9.84352

School intercept                 5.72659                                          5.97047

School slope                       0.85956                                          0.72623


On May 31, 2013, at 3:00 AM, r-sig-mixed-models-request@r-project.org wrote:

> Daniel Anderson <daniela@...> writes:
> 
>> Dear all, 
> 
>> I am trying to fit a cross-classified growth model with the lme4
>> package. The model is cross-classified because students moved
>> between schools during the three years of the study (for a similar
>> application, see Luo & Kwok, 2012). I have estimated the following
>> model with both lme4 and the HLM software.
> 
>> m.1<-lmer(MthScale ~ Clock + (Clock | mastid) + (Clock | School), 
>> data=dta2, REML = F, verbose = T)
>> 
>> where, 
>> MthScale is a mathematics achievement test, 
>> Clock is a time variable coded 0, 1, 2,
>> mastid = id for students,
>> School = id for schools.
> 
> 
>> When I fit the model I get the following error message "In
>> mer_finalize(ans) : false convergence (8)". Yet, my results are
>> quite similar to the results I obtained from the HLM software, which
>> gave me no such error (see attached).
> 
>> I have seen that some people have had success using David Hughes
>> approach
>> (http://davidhughjones.blogspot.com/2009/11/lme-false-convergence.html),
>> but that did not work for me. I also read that it could be a result
>> of the optimizer not reaching its predefined minimum, but that the
>> result is likely to be a minimum
>> (https://stat.ethz.ch/pipermail/r-sig-mixed-models/2011q1/015743.html).
>> Finally, I tried
> 
>> fitting the following much simpler model, and still received the
>> same error message:
> 
>> m.2<-lmer(MthScale ~ Clock + (Clock | mastid),
>> data=dta2, REML = F, verbose = T)
>> 
>> So, my questions are (a) are there any other ideas for
>>  getting the model to converge without the warning
>> message, and/or (b) is it safe to continue with model 
>> building despite the warning?
> 
>> Below is the output from the verbose = T argument. I've also
>> attached a word document comparing the estimates between lme4 and
>> the HLM software.
> 
> The Word document didn't make it to the list, I think (probably
> got stripped by the mailing list software).  Any chance of
> attaching as a text file?
> 
>  If the estimates from HLM and lme4 are close (i.e. the differences
> are much smaller than the estimated standard errors), then I can't
> see any reason to worry very much ...
> 
>  Are you willing to give the development version of lme4
> a try?  It's slightly more robust and eminently more tunable.
> I think you should be able to install it via
> 
> install.packages("lme4",repos=c("http://lme4.r-forge.r-project.org/repos",
>         getOption("repos")))
> 
> 
> 
> 
> _______________________________________________
> R-sig-mixed-models mailing list
> R-sig-mixed-models@r-project.org
> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models

Thanks,
--
Daniel Anderson
Research Assistant
Behavioral Research and Teaching
University of Oregon











	[[alternative HTML version deleted]]

