I am performing linear mixed effects analysis on 4 time points (same individuals), with BMI and race as fixed effects in R using lme(). I am interested in both the main effects and interactions and have included a term for within subject random effects. My model is as follows:
I then want to determine which main effect and interaction terms are significant. I have approached this by first building the model excluding the term I am interested in, for example if I wanted to know if the race term has a significant effect I would use the following:
lme.model2<-lme(outcome~visit+bmi+visit*bmi, random=~1|subject,data=lme_data,na.action=na.exclude anova(lme.model1,lme.model2) Model df AIC BIC logLik Test L.Ratio p-value lme.model1 1 26 11.166337 107.7234 20.41683 lme.model2 2 14 3.879536 55.8718 12.06023 1 vs 2 16.7132 0.1607
anova(lme.model1) numDF denDF F-value p-value (Intercept) 1 205 41515.74 <.0001 visit 3 205 19.88 <.0001 bmi 2 74 4.63 0.0128 race 1 74 6.59 0.0123 visit:bmi 6 205 2.74 0.0140 visit:race 3 205 2.22 0.0869 bmi:race 2 74 0.06 0.9436 visit:bmi:race 6 205 0.42 0.8633
I know the first example comparing the two models uses a likelihood ratio and the second uses anova with type 1 SS (which from what I read is preferable when you want to look at interactions). However, since these give different results and interpretations for race as a main effect, what is the most acceptable method of determining significance of main effects (and interactions) in my case? Is there an acceptable 'norm'?