[R-sig-ME] GLMM for Combined experiments and overdispersed data

[Juan Pablo Edwards Molina]

I´m sorry... I´m new in the list, and when I figured out that the question
would suit best in the mixed model list I had already post it in general
R-help. I don´t know if there´s a way to "cancel a question"... I will take
care of it from now on.

Dear Thierry, thanks for your answer.
Yes, I am not interested in the effect of a specific farm, they simply
represent the total of farms from the region where I want to suggest the
best treatments.

I Followed your suggestions, but still have a couple of doubts,

1- May "farm" be include as a simple fixed effect or interacting with the
treatment?

m3 = glmer(resp ~ trt * farm + (1|tree_id), family = binomial, data=df)
m4 = glmer(resp ~ trt + farm + (1|tree_id), family = binomial, data=df)

​2 - ​
In case of significant
​[ trt * farm ], should I report the results for each farm?​

Thanks again Thierry,

Juan Edwards


*Juan*

On Mon, Apr 24, 2017 at 4:29 AM, Thierry Onkelinx <thierry.onkelinx at inbo.be>
wrote:

> Dear Juan,
>
> Use unique id's for random effects variables. So each bk should only be
> present in one farm. And each tree_id should be present in only one bk. In
> case each block has different treatments then each tree_id should be unique
> to one combination of bk and trt.
>
> Farm has too few levels to be a random effects. So either model is as a
> fixed effect or drop it. In case you drop it, the information will be
> picked up by bk. Note that trt + (1|farm) is less complex than trt * farm.
>
> Assuming that you are not interested in the effect of a specific farm, you
> could use sum, polynomial or helmert contrasts for the farms. Unlike the
> default treatment contrast, these type of contrasts sum to zero. Thus the
> effect of trt will be that for the average farm instead of the reference
> farm.
>
> Best regards,
>
> 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 22:32 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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>
>
>

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