I have read similar posts in this website to help me assess whether my diagnostic plots are too far away from normal and if they are showing heteroscedasticity (Interpretation of residuals vs fitted plot, Interpretation of plot(glm.model)) and I researched other sources as well. I haven't been able to find plots that might resemble mine.
I have concerns about my residuals not having a normal distribution (qqplot) as they depart from the line quite a bit, but I am even more concerned about my data showing heteroscedasticity. Are my concerns well founded here?
The plots belong to this linear mixed model with one random effect (individual id) and a fixed effect that is a three way interaction:
lmer(log.prop.out ~ 1 + time*season*sex + (1|id), REML=FALSE, data=in.out)
log.prop.out is the log of a proportion, whereas time, season and sex are all categorical predictors. Should I be concerned about the log transformation not being enough to normalize my response variable?
I am struggling to decide how bad can these plots look without raising concern.
Thank you very much for your kind guidance!
Without the transformation, using directly the proportions, this is how the residuals vrs fitted plot looks like: