Help please! How to code a mixed-model with 2 within-subject factors using lme or lmer?
Hi Roberto, The other thing you can do --- if you don't wish to step across to lmer(), where you will be able to exactly replicate the crossed-factor error structure --- is stay with aov(... + Error()), but fit the factor you are interested in last. Assume it is Sex. Then fit your model as aov.model <- aov(Volume ~ Lobe * Tissue * Sex + Error(Subject/(Lobe * Tissue)) This should give you a so-called "Type-II" test for Sex. You may verify this by fitting the model without the Error term and using Anova() from the car package (which does Type-II/III tests) to look at the SS and F values. I say should, because the only concern I have is whether this procedure is affected by the presence of an Error term in the model. Establishing this is beyond my capabilities. Regards, 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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