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 > > ______________________________________________ > R-help@r-project.org mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide > http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code. > > -- View this message in context: http://www.nabble.com/Help-please%21-How-to-code-a-mixed-model-with-2-within-subject-factors-using-lme-or-lmer--tp19480815p19489323.html Sent from the R help mailing list archive at Nabble.com. ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
