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

[Mark Difford]

Hi Roberto,

It's difficult to comment further on specifics without access to your data
set. A general point is that the output from summary(aov.object) is not
directly comparable with summary(lme.object). The latter gives you a summary
of a fitted linear regression model, not an analysis of variance model, and
what you "see" will depend on what contrasts were in place when the model
was fitted.

If you haven't changed these then they will be so-called treatment
contrasts. What you are seeing for Lobe (which plainly is coded as a factor)
in the output from summary(lme.object) are the regression coefficients for
each level of Lobe relative to its reference/treatment/baseline level, which
is your (Intercept). If you fitted your model with, say, Helmert or
sum-to-zero contrasts then these values would change.

To see what your current reference level is do levels(dataset$Lobe). See
?levels.

What you want to look at to begin with is: anova(lme.object).

HTH, Mark.


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?
> 
> 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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