[R-sig-ME] lme interaction result strange

[Ista Zahn]

Hi Charles,

My first guess is that you have (a) categorical variable(s) in your
predictors, and that the contrasts in SAS are different than those in
R.

Best,
Ista

On Tue, May 1, 2012 at 1:39 PM, Charles Determan Jr <deter088 at umn.edu> wrote:
> Dear R users,
>
> I have been working on replicating some linear mixed models from SAS.  The
> first one matches perfectly when the SAS model is simple with the three
> separate factors.
>
> fit=lme(var~group+Event_name+Died,
>    data=liv34,
>    random=~1|ID)
> anova.lme(fit, type="marginal", adjustSigma=F)
>
> However, once I put an interaction into the formula the values don't match.
>
> fit=lme(var~group+Event_name+Died+Event_name*Died,
>    data=liv34,
>    random=~1|ID)
> anova.lme(fit, type="marginal", adjustSigma=F)
>
>                          numDF denDF     F-value      p-value
> (Intercept)               1        91       111.20483  <.0001
> group                      1        23        0.46632     0.5015
> Event_name            5        91        1.14042     *0.3449*
> Died                       1        23        0.50989    * 0.4824*
> Event_name:Died     5       91        1.10436     0.3637
> Done.
>
> The numbers *bold* don't match up.  They should be approximately .0290 and
> .1318 respectively.  The other two are still exact matches.  I know looking
> for exact matches is ambitious but the numbers should be at least similar
> that the conclusions don't change so drastically.
>
> Any thoughts as to why this discrepancy is happening would be most
> appreciated.
>
> Regards,
>
> Charles
>
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>
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