CODEWITHSUNDEEP
rlme4anovamixed-modelsrandom-effects
Melderon
Melderon

Reputation: 365

3 way interaction between random and fixed effects

I have a set of data that looks like this:

rep stage   line    temp    surv
1   L        149    18      0.6
2   L        149    18      0.7
3   L        149    18      0.25
1   A        149    18      1
2   A        149    18      1
3   A        149    18      1
1   L        149    25      0
2   L        149    25      0.2
3   L        149    25      0.3
1   A        149    25      1
2   A        149    25      1
3   A        149    25      1
1   L        208    18      0.6
2   L        208    18      0.4
3   L        208    18      0.55
1   A        208    18      1
2   A        208    18      1
3   A        208    18      1
1   L        208    25      0
2   L        208    25      0.05
3   L        208    25      0.05
1   A        208    25      1
2   A        208    25      0.857142857
3   A        208    25      0.7

Where rep is replicate, stage is the life stage of fruit fly I am working with (L = larvae, A = adult), line is a number assignment of the genetic line, temp is the rearing temperature, and surv is proportion that survived.

What I want to do, using the lme4 package in R, is fit a 3-way interaction model (linear mixed model) to run an ANOVA. My original model:

surv_3w.aov<-lmer(surv~stage*line*temp + (1|rep), data=dat_3w)

works but I want to treat line as a random effect. I think I am correctly treating rep as a grouping variable, (1|rep), but I am not sure.

I tried this model: 

surv_3w.aov<-lmer(surv~stage*temp*(1|line) + (1|rep), data=dat_3w)

but then my 3-way interaction is gone.

Basically, I am asking help making a 3-way interaction model between line, stage, and temp where line is random and rep is a grouping variable

Upvotes: 0

Views: 1819

Answers (1)

Erdne Ht&#225;brob
Erdne Ht&#225;brob

Reputation: 879

duplicate of a lot of similar questions, but this for example.

The code for the 3-way interaction model you're asking is:

surv_3w.aov<-lmer(surv~stage*line*temp + (1 + line |rep), data=dat_3w)

Whether this is really what you need, I don't know.

Upvotes: 2

Related Questions