I am new to lme4 and I am not sure if I understand correctly. If I want to know if there is an interaction between A and B, I have to write two models and then compare them with anova and the one with the lowest AIC is the one that fits the data better.
So I wrote this two models:
model1 <- glmer(accuracy ~ var1 + var2 + (1 | participant), data = xdat, family = binomial()) model2 <- glmer(accuracy ~ var1 * var2 + (1 | participant), data = xdat, family = binomial())
Model one has a lower AIC value. Model2 did not converge. In the output, model2 tells me there is an interaction at one of the levels of var1 with one of the levels of var2.
Can I say there is no interaction because the best model does not include it, or should I say there is because the model designed to test it includes it and says at one of the levels of the variables it is significant, even if the thing did not converge?