[R] GLMM for Combined experiments and overdispersed data
Juan Pablo Edwards Molina
edwardsmolina at gmail.com
Fri Apr 21 20:57:46 CEST 2017
I am analyzing data from 3 field experiments (farms=3) for a citrus flower
disease: response variable is binomial because the flower can only be
diseased or healthy.
I have particular interest in comparing 5 fungicide spraying systems
Each farm had 4 blocks (bk=4) including 2 trees as subsamples (tree=2) in
which I assessed 100 flowers each one. This is a quick look of the data:
farm trt bk tree dis tot <fctr> <fctr> <fctr>
<fctr> <int> <int>
iaras cal 1 1 0 100
iaras cal 1 2 1 100
iaras cal 2 1 1 100
iaras cal 2 2 3 100
iaras cal 3 1 0 100
iaras cal 3 2 5 100...
The model I considered was:
resp <- with(df, cbind(dis, tot-dis))
m1 = glmer(resp ~ trt + (1|farm/bk) , family = binomial, data=df)
I tested the overdispersion with the overdisp_fun() from GLMM page
chisq ratio p logp
4.191645e+02 3.742540e+00 4.804126e-37 -8.362617e+01
As ratio (residual dev/residual df) > 1, and the p-value < 0.05, I
considered to add the observation level random effect (link
to deal with the overdispersion.
farm trt bk tree dis tot tree_id <fctr> <fctr>
<fctr> <fctr> <int> <int> <fctr>
iaras cal 1 1 0 100 1
iaras cal 1 2 1 100 2
iaras cal 2 1 1 100 3...
so now was added a random effect for each row (tree_id) to the model, but I
am not sure of how to include it. This is my approach:
m2 = glmer(resp ~ trt + (1|farm/bk) + (1|tree_id), family = binomial, data=df)
I also wonder if farm should be a fixed effect, since it has only 3
m3 = glmer(resp ~ trt * farm + (1|farm:bk) + (1|tree_id), family =
I really appreciate your suggestions about my model specifications...
*Juan Edwards- - - - - - - - - - - - - - - - - - - - - - - -# PhD student
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