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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