# Transforming data for homoscedasticity for Linear Mixed Effects models

I have a model based on a dataset that respects all linear model assumptions except for homoscedasticity. When I just ignore the problem of heteroscedasticity, the p-value, for the interaction with group, in my model is <.00001. I definitely know that there is something as per my previous studies and the literature in this field. However, I would like to be honest regarding my analyses and assumptions. Is this assumption really needed if the other 3 main ones are respected (independence, linearity, absence of collinearity) for the interpretation of the p-value in the mixed effects models?

When I run the following on my lmer model called mod:

plot(fitted(mod),residuals(mod))


I get a cone shape distribution. I then try to log transform it, and recheck the model, for the interaction with group in my model the p value goes to .40. Quite a jump! My data comes brain activity from patients and healthy individual, just to clarify.

This is my model:

lmer(value ~ dist*group + (1|patientnumber), dat1)

This is how I obtained the p-value:

Anova(mod)