I have a data set as follows:
Response variable; proportion (i.e., 0.15, 0.94, 0.26) Predictor variables; both fixed, one continuous (400,700,1000,1300), one catagorical (2-levels of species)
Code for the model used: glm_necrosis <- glm(necrosis ~ Sp + Treat + Sp*Treat, family = gaussian)
I have run a linear regression in R and plotted my residuals against predicted values and while there is no clear non-linear trend in the data, homogeneity of variance was violated (classic wedge shape). A q-q plot also showed that the data was highly leptokurtic and non-normal. I tried a number of transformations (arcsine, log, squared, etc.), and nothing could improve on the normality or variance assumptions.
Following the arcsine transformation, this is what the residuals looked like.
I would like to know what my options might be for continuing. Would a generalized additive model be appropriate, or are there non-parametric linear regression alternatives that might would better?
I appreciate any assistance.