I have a big dataset (first 36 samples in image below) with proportion data (Proportion) that refer to the substrate that some insects eat: for example sample 1 eats 100% wood, sample 25 eats apx 81% wood. These data originate from counts which I transformed to proportions in order to be able to merge with some older data (that were already proportions). As a result of that not every sample is independent because for example samples 19 and 30 refer to the same insect, which does not eat any wood (0%) but eats exclusively (100%) soil.
I would like to examine the effects of the type of substrate and the group (phylogeny) the insects belong to. And in order to do that I would like to fit everything in a model. After looking around a little bit, I realized that my options are either beta regression or glm binomial. I already tried a glm-binomial model but the results made no sense at all! Categories that I expected to see huge differences (and be highly significant) were not significant at all...
On the other hand, I was under the impression that I cannot use beta regression because I have lots of '1' and '0'
Any advice on what to do and how to do it will be greatly appreciated... :)