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edit approved Oct 5, 2014 at 20:21
David Arenburg
edit approved Oct 5, 2014 at 20:21
David Arenburg
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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)

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)

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?

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?

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?

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Archaeopteryx_93
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Archaeopteryx_93
Archaeopteryx_93
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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?