Thank you for your response Ista.  Do you have a recommendations how I
might address variation in the contrasts?

Regards,
Charles

On Tue, May 1, 2012 at 12:51 PM, Ista Zahn <istazahn@gmail.com> wrote:

> 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@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
> >
> >        [[alternative HTML version deleted]]
> >
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>

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