R help - Apr 2017 - GLMM for Combined experiments and overdispersed data

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
(trt=5).

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
<http://glmm.wikidot.com/faq>

        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
<http://r.789695.n4.nabble.com/Question-on-overdispersion-td3049898.html>)
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
levels...

m3 = glmer(resp ~ trt * farm + (1|farm:bk) + (1|tree_id), family binomial,
data=df)

I really appreciate your suggestions about my model specifications...




*Juan? Edwards- - - - - - - - - - - - - - - - - - - - - - - -# PhD student
- ESALQ-USP/Brazil?*

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Please don't cross post. You've send the message to the mixed models
mailing list as well (which more appropriate).

ir. Thierry Onkelinx
Instituut voor natuur- en bosonderzoek / Research Institute for Nature and
Forest
team Biometrie & Kwaliteitszorg / team Biometrics & Quality Assurance
Kliniekstraat 25
1070 Anderlecht
Belgium

To call in the statistician after the experiment is done may be no more
than asking him to perform a post-mortem examination: he may be able to say
what the experiment died of. ~ Sir Ronald Aylmer Fisher
The plural of anecdote is not data. ~ Roger Brinner
The combination of some data and an aching desire for an answer does not
ensure that a reasonable answer can be extracted from a given body of data.
~ John Tukey

2017-04-21 20:57 GMT+02:00 Juan Pablo Edwards Molina <
edwardsmolina at gmail.com>:
> 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
> (trt=5).
>
> 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
> <http://glmm.wikidot.com/faq>
>
>         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
>
<http://r.789695.n4.nabble.com/Question-on-overdispersion-td3049898.html>)
> 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
> levels...
>
> m3 = glmer(resp ~ trt * farm + (1|farm:bk) + (1|tree_id), family >
binomial, data=df)
>
> I really appreciate your suggestions about my model specifications...
>
>
>
>
> *Juan? Edwards- - - - - - - - - - - - - - - - - - - - - - - -# PhD student
> - ESALQ-USP/Brazil?*
>
>         [[alternative HTML version deleted]]
>
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