I have been trying to figure out the best way to analyse my data for some time and I am just working myself into a pit of confusion. Briefly, I have taken a whole bunch of physiological measurements across time from a group of different plants grown under different treatments. I have 4 time points, 8 plant mutants, and 2 treatments. The measurements were ultimately destructive, and so this is NOT a repeated measures design through time - different plants were measured at each time point. I have ~8 replicates per group. At face value this is a simple 3-way ANOVA. However, the measurements were taken 9, 23, 30, and 68 days after sowing and so the values I obtained (e.g. shoot weight) vary considerably (especially, obviously, between day 9 and day 68).
Initially I constructed a model to do just this, though looking at the residuals from this model tells me that they are not normally distributed (Fig. 1 & 2).
As such, I used the package bestNormalize in R to transform my data. However, the transformed data (which were transformed by the method known as "orderNorm Transformation" in the package) show similarly terrible residuals (Fig. 3).
So, I do not know if I have transformed the data in the right way, or if a transformation after a bad Q-Q Plot of the residuals of untransformed data is correct...
Can someone please tell me what kind of analyses I should be doing on these data?
Thank you :) Aaron