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

[Douglas Bates]

On Sat, Mar 27, 2010 at 11:25 PM, Yong Wu <wuyong88 at gmail.com> 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)

Both models are an unusual specification.  You have a random effect
with respect to age but no fixed effect for age.  This means that the
mean slope with respect to age across all ID's is constrained to be
zero, which for BMI is unlikely.  I think your model should be

BMI ~ 1 + exposure + age + (1 + age|ID)

I might even start with

BMI ~ 1 + exposure*age + (1 + age|ID)

unless exposure is at age zero.
> 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
> ,
>
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>
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