I have percentage disease data taken from leaves of wheat in a disease trial which were artificially inoculated with isolates of disease from different source plants.
The basic question is, are disease isolates sourced from the same cultivar better at infecting those cultivars? We are looking for an interaction. I am trying to conduct a logit linked beta glm to determine if there is an interaction between the source cultivar variable and the inoculated host cultivar variable.
My model looks something like this:
beta_model=betareg(mean.proportion.disease ~ Source.CV + inoculant.Host + Source.CV*Inoculant.Host, data = study, link =c('logit'))
I have also tried a binomial glm:
binom_model = glm(mean.proportion.disease ~ Source.CV + inoculant.Host + Source.CV*Inoculant.Host, data = study, family = binomial(link = 'logit'))
Both models give me residuals that look like this:
Is there any way I can improve on this and get normal residuals? I might also be worth noting that there are a few zeros in my data so proportions are transformed according to: $(y*(n-1)+0.5)/n)$.
Lastly, you've probably already guessed I am a beginner in this realm of statistics, and as such I'm thinking about glm's in the context of simple linear regression. So I can't really understand what the intercept in glm output is telling me, if anyone can help me with this I'd very grateful.
Any comments appreciated.