[R] Help please! How to code a mixed-model with 2 within-subject factors using lme or lmer?

[Adam D. I. Kramer]

On Sun, 14 Sep 2008, roberto toro wrote:

> Thanks for answering Mark!
>
> I tried with the coding of the interaction you suggested:
>
>> tfac<-with(vlt,interaction(Lobe,Tissue,drop=T))
>> mod<-lme(Volume~Sex*Lobe*Tissue,random=~1|Subject/tfac,data=vlt)
>
> But is it normal that the DF are 2303? DF is 2303 even for the estimate of
> LobeO that has only 662 values (331 for Tissue=white and 331 for
> Tissue=grey). I'm not sure either that Sex, Lobe and Tissue are correctly
> handled.... why are there different estimates called Sex:LobeO, Sex:LobeP,
> etc, and not just Sex:Lobe as with aov()?. Why there's Tissuew, but not
> Sex1, for example?

lme is basically doing a regression, not an ANOVA as you're used to it. You
may want anova(mod) instead of summary(mod) to see aggregated effects. Or,
you could define contrasts among your levels by assigning to
contrasts(vlt$Lobe), for example.

Also, in the above model, you're only looking at modeling a separate average
volume for each subject-within-tfac; if I read you correctly, you actually
want to model a lobe and tissue effect for each subject for each tfac, in
which case you would want something like what was in my last post.

--Adam

>
> Thanks again!
> roberto
>
> ps1. How would you code this with lmer()?
> ps2. this is part of the output of mod<-lme:
>> summary(mod)
> Linear mixed-effects model fit by REML
> Data: vlt
>       AIC      BIC    logLik
>  57528.35 57639.98 -28745.17
>
> Random effects:
> Formula: ~1 | Subject
>        (Intercept)
> StdDev:    11294.65
>
> Formula: ~1 | tfac %in% Subject
>        (Intercept) Residual
> StdDev:    10569.03 4587.472
>
> Fixed effects: Volume ~ Sex * Lobe * Tissue
>                       Value Std.Error   DF    t-value p-value
> (Intercept)        245224.61  1511.124 2303  162.27963  0.0000
> Sex                  2800.01  1866.312  329    1.50029  0.1345
> LobeO             -180794.83  1526.084 2303 -118.46975  0.0000
> LobeP             -131609.27  1526.084 2303  -86.23984  0.0000
> LobeT              -73189.97  1526.084 2303  -47.95932  0.0000
> Tissuew            -72461.05  1526.084 2303  -47.48168  0.0000
> Sex:LobeO            -663.27  1884.789 2303   -0.35191  0.7249
> Sex:LobeP           -2146.08  1884.789 2303   -1.13863  0.2550
> Sex:LobeT            1379.49  1884.789 2303    0.73191  0.4643
> Sex:Tissuew          5387.65  1884.789 2303    2.85849  0.0043
> LobeO:Tissuew       43296.99  2158.209 2303   20.06154  0.0000
> LobeP:Tissuew       50952.21  2158.209 2303   23.60856  0.0000
> LobeT:Tissuew      -15959.31  2158.209 2303   -7.39470  0.0000
> Sex:LobeO:Tissuew   -5228.66  2665.494 2303   -1.96161  0.0499
> Sex:LobeP:Tissuew   -1482.83  2665.494 2303   -0.55631  0.5781
> Sex:LobeT:Tissuew   -6037.49  2665.494 2303   -2.26506  0.0236
>
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