# In which order should factors be removed when performing model simplification (lmer)?

I am a bachelor student in biology and for a project work, I have a model with a design like this (A, B & C are fixed factors, D is random and nested in C):

lmer1 = lmer(y~A+B+C+A:B+A:C+B:C+A:B:C+(1+A+B|D:C))
summary(lmer1)


If A:B:C is not significant, I can simplify the model by removing this term:

lmer2 = lmer(y~A+B+C+A:B+A:C+B:C+(1+A+B|D:C))
anova (lmer1,lmer2)
summary(lmer2)


If now the p value from the ANOVA table >0.05, I can proceed with lmer2. But here is my question: how should I simplify further if there are still unsignificant fixed and random terms? Should the next step be removing A:B (or A:C or B:C) from the fixed part or removing from the random part of the model?

• This is actually a giant can of worms. e.g. dynamicecology.wordpress.com/2014/10/02/… . I would personally recommend not doing any model simplification unless it's absolutely necessary for model interpretation. – Ben Bolker Oct 5 '14 at 21:48
• let me rephrase that slightly (I would insert "data-driven") before "model simplification" – Ben Bolker Oct 6 '14 at 0:10