[R-sig-ME] Very weird lmer results, compared to SAS proc mix

[Ben Bolker]

  Can you post the summaries of these models, so we can see if there
might be something else odd going on?  (Making the data, or an otherwise
reproducible example, available would be even better ...)
   It might also be useful to post the parameter estimates from PROC
MIXED.  Maybe (it's a long shot) PROC MIXED automatically includes the
fixed effect of age (which would be highly correlated with exposure,
which might explain the non-significance of exposure)?

Yong Wu wrote:
> Sorry to bother you. I am struggling in this issue for long time. Wish
> somebody can help me.
> 
> I first used lmer to do the following analysis.
> fullmodel=lmer(BMI~1+exposure+(age|ID),data, REML=FALSE)
>           reducemodel=lmer(BMI~1+(age|ID),data, REML=FALSE)
>           anova(full,red)
> The "fullmodel" has AIC of 6874 and "reducemodel" has AIC of 7106, which
> cause "anova" analysis giving the p-value< 2.2e-16 . This result is
> definitely wrong
> 
> I then did the similar study by SAS.
> The fullmodel is:
> proc mixed;
> class exposure;
> model BMI=exposure;
> random age /sub=id;
> run;
> The AIC is 7099.7, and type 3 test of fixed effect, exposure, got
> p-value=0.74.
> 
> The reducemodel is:
> proc mixed;
>  class exposure;
>  model BMI=;
>  random age /sub=id;
>  run;
>  The AIC is 7101.2.
> 
> The SAS result is correct.
> 
> Could somebody help me to explain why lmer is wrong?
> 
> I do not even dare to use lmer now, since I can not trust its result. Thanks
> in advance for any of your answer.
> 
> Best,
> Yong
> ,
> 
> 	[[alternative HTML version deleted]]
> 
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-- 
Ben Bolker
Associate professor, Biology Dep't, Univ. of Florida
bolker at ufl.edu / people.biology.ufl.edu/bolker
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