I am examining the difference between a physical feature of different species of animals. Due to the nature of my experiments I'm using a nonlinear mixed model with the following setup:
lme(log10(feature) ~ log10(Body.mass) + factor(Trial.Number), random = ~1 | IndividualID, data=animals, subset=Frfactor=="low", na.action=na.omit )
subset=Frfactor=="low" refers to a specific speed range that I'm interested in.
I get great results which I'm happy about. But now I want to see how species affects my feature. Since the same conditions apply (tons of repeated effects) I've kept the lme and changed the structure to:
lme(log10(feature) ~ specfactor + factor(Trial.Number), random = ~1 | IndividualID, data=animals, subset=Frfactor=="low", na.action=na.omit )
where specfactor lists the names of the species. Looking at the p values it looks like these species are not significantly different from the intercept (which is specfactorserval). However when I create a boxplot, it certainly looks like there are some big interspecies differences!
I guess because the lme is a test for regressions, it doesn't really make sense to use when comparing the feature against categorical variables. But I still need to account for repeated effects. My question is if there's a better way to test for significance between species using the boxplot? I need the usual- p-values, confidence intervals. The "list" command seems to fall short of such comparisons. I don't know if a t-test would cut it.
PS I originally posted an image of my test results and of the boxplot, but I'm too new of a user to be allowed....