I am attempting a GLMM with nested fixed effects. Most examples of nesting that I see deal with random effects, but my experimental design is hierarchical by nature and I am interested in making comparisons between treatments at the nested level. My response is proportion fat (continuous), and I am interested in how feeding treatment (3 levels) influenced fat accumulation in different ages within each treatment (two levels), in each season (two levels). within each season, I am also interested in how both ages within each feeding treatment differed from the corresponding age groups in the control. I'm not interested in comparing between seasons. Here are the two models I am using to answer these questions:
GLMMadmb(proportion ~ season/age/treatment + leanweight + (1|source plot), family=beta) GLMMadmb(proportion ~ season/treatment/age + leanweight + (1|source plot), family = beta)
My output is exactly what I was expecting based on graphing the data ... but the lack of papers that use nested fixed effects in a mixed model makes me wonder if I am missing something ...
For instance, if I simply look at the interaction of
season * age * treatment I get comparisons between months not within them, and between age groups but not between the same age in different treeatments. With interactions alone, my coefficents are (
leanweight, season2, age2, treatment2, treatment3, season2:age2, season2:treatment2, season2:treatment3, age2:treatment2, age2:treatment3, season3:age2:treatment2, season3:age2:treatment2)
However when I nest season/age/treatment I get the coefficents I want which are (
avgleantwt, season3, season1:age2, season3:age2, season1:age1:treat1, season1:age2:treat1, season2:age1:treat1, season2:age2:treat1, season1:age1:treat2, season1:age2:treat2, season2:age1:treat2, season2:age2:treatment2, season2:age2:treat3) season2:age2:treat3) season1:age1:treat3, season1:age2:treat3, season2:age1:treatment3, season2:age2:treatment3)