[R-sig-ME] GLMM for Combined experiments and overdispersed data
Juan Pablo Edwards Molina
edwardsmolina at gmail.com
Mon Apr 24 13:56:08 CEST 2017
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
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
m3 = glmer(resp ~ trt * farm + (1|tree_id), family = binomial, data=df)
m4 = glmer(resp ~ trt + farm + (1|tree_id), family = binomial, data=df)
In case of significant
[ trt * farm ], should I report the results for each farm?
Thanks again Thierry,
On Mon, Apr 24, 2017 at 4:29 AM, Thierry Onkelinx <thierry.onkelinx at inbo.be>
> 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
> Best regards,
> ir. Thierry Onkelinx
> Instituut voor natuur- en bosonderzoek / Research Institute for Nature and
> team Biometrie & Kwaliteitszorg / team Biometrics & Quality Assurance
> Kliniekstraat 25
> 1070 Anderlecht
> 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
>> 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
>> am not sure of how to include it. This is my approach:
>> m2 = glmer(resp ~ trt + (1|farm/bk) + (1|tree_id), family = binomial,
>> 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 =
>> binomial, data=df)
>> I really appreciate your suggestions about my model specifications...
>> *Juan Edwards- - - - - - - - - - - - - - - - - - - - - - - -# PhD student
>> - ESALQ-USP/Brazil*
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>> R-sig-mixed-models at r-project.org mailing list
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