Hi,

Actually, I just took a look at the SAS code. They also removed ID=197!

They also included InitHeight and InitAge as subject-level  
predictors. If one runs the following model

lmer(LogFEV1 ~ Height + Age + InitHeight + InitAge + (Height+Age| 
ID),data=a, subset=ID != 197)
or
lmer2(LogFEV1 ~ Height + Age + InitHeight + InitAge + (Height+Age| 
ID),data=a, subset=ID != 197),

one can compare AIC and BIC values for SAS and lmer.

Interestingly, lmer and lmer2 yield smaller AIC (-4632) and BIC  
(-4570) values than what is listed as SAS output (-4576; -4550).
Smaller ist better. Does this also mean that lmer estimates are  
"better"?

Best
Reinhold


On Sep 13, 2007, at 9:51 AM, Henric Nilsson (Private) wrote:

> Quoting Martin Maechler <maechler@stat.math.ethz.ch>:
>
>>>>>>> "DB" == Douglas Bates <bates@stat.wisc.edu>
>>>>>>>     on Thu, 6 Sep 2007 11:17:17 -0500 writes:
>>
>>     DB> On 9/6/07, Bush, Andrew J <abush@utmem.edu> wrote:
>>>> Dear Douglas,
>>
>>>> In frustration, I invoked lmer2 this morning and I'm pleased
>> to be able
>>>> to tell you that lmer2 copes well and quickly with the model  
>>>> having a
>>>> random intercept and two random covariate slopes.  I have not
>>  been able
>>>> to get lmer to converge for the model on the same data.
>>
>>     DB> Thanks for the information.
>>
>>     DB> I expect to remove the confusion between lmer and lmer2 in  
>> the near
>>     DB> future.  The development version of the lme4 package has  
>> an lmer
>>     DB> function that is close to the current lmer2 in design.  It  
>> should
>>     DB> exhibit the same convergence behavior and be slightly faster
>>  on models
>>     DB> fit to large data sets than is the current lmer2.
>>
>>     DB> This version has been in development for longer than I had  
>> expected.
>>     DB> I still have a few "infelicities" to resolve in the Laplace
>> method for
>>     DB> generalized linear mixed models before I make test versions
>> available.
>>
>>     DB> I would be interested in the data set if you would be  
>> willing to
>>     DB> provide it.  I could perhaps incorporate it in the lme4  
>> package so
>>     DB> others would have access to it.
>>
>> Yes, indeed.
>> The example might be particularly interesting as test case, not
>> only because some software implementations "converge" with
>> singular covariance matrix, but also because it
>> differs from other examples in having "many" fixed effects and
>> just one level random effects.
>
> The data set in question, and, I belive, most others from Fitzmaurice,
> Laird and Ware's (2004) book on longitudinal data analysis, is
> available along with accompanying SAS programs at
>
> http://biosun1.harvard.edu/~fitzmaur/ala/
>
> In particular, the data used above is here
>
> http://biosun1.harvard.edu/~fitzmaur/ala/fev1.txt
>
> and the SAS code is here
>
> http://biosun1.harvard.edu/~fitzmaur/ala/prog8_8.html
>
>
> HTH,
> Henric
>
>
>
>>
>> Martin
>>
>>>> -----Original Message-----
>>>> From: dmbates@gmail.com [mailto:dmbates@gmail.com] On Behalf
>> Of Douglas
>>>> Bates
>>>> Sent: Wednesday, September 05, 2007 9:22 PM
>>>> To: ajbush@bellsouth.net
>>>> Cc: r-sig-mixed-models@r-project.org
>>>> Subject: Re: [R-sig-ME] Specifying random effects for multiple
>>>> covariates via lmer
>>>>
>>>> On 9/5/07, Andy Bush <ajbush@bellsouth.net> wrote:
>>>>> While working through the text "Applied Longitudinal Analysis" by
>>>>> Fitzmaurice, Laird and Ware, I encountered a fairly simple
>> case study
>>>> (pp
>>>>> 210-7) in which a longitudinal model specifies three random  
>>>>> effects:
>>>> (1)
>>>>> random intercepts for id, (2) random slopes for covariate1
>> (Age | id),
>>>> and
>>>>> (3) random slopes for covariate2 (log(ht) | id).  I've had no
>>>> difficulty
>>>>> formulating lmer models with correlated random intercepts and  
>>>>> slopes
>>>> for
>>>>> either of the covariates individually but have not succeeded  
>>>>> when I
>>>> try to
>>>>> compose a model with correlated random intercepts and slopes  
>>>>> for two
>>>>> covariates.
>>>>
>>>>> Following is code that works well with the individual covariates
>>>> separately:
>>>>
>>>>> m1=lmer(LFEV1~Age + loght + InitAge + logbht + (1 + Age |
>>>> id),data=fev,
>>>>>        na.action=na.omit, method="REML")
>>>>
>>>>> m2=lmer(LFEV1~Age + loght + InitAge + logbht+(1 + loght |
>>>> id),data=fev,
>>>>>        na.action=na.omit, method="REML")
>>>>
>>>> Maybe I am missing the point but wouldn't the model you are
>>>> considering be written as
>>>>
>>>> lmer(LFEV1 ~ Age + loght + InitAge + logbht + (loght + Age|id),  
>>>> data =
>>>> fev, na.action = na.omit, method = "REML")
>>>>
>>>> That provides correlated random effects for the intercept, the
>>>> coefficient for loght and the coefficient for Age at each level  
>>>> of the
>>>> id factor.
>>>>
>>
>>     DB> _______________________________________________
>>     DB> R-sig-mixed-models@r-project.org mailing list
>>     DB> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
>>
>> _______________________________________________
>> R-sig-mixed-models@r-project.org mailing list
>> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
>>
>>
>
> _______________________________________________
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----
Reinhold Kliegl, Dept. of Psychology, University of Potsdam,
Karl-Liebknecht-Strasse 24-25, 14476 Potsdam, Germany
phone: +493319772868, fax: +493319772793
http://www.psych.uni-potsdam.de/people/kliegl/




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