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