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 < edwardsmol...@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]] > > ______________________________________________ > R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide http://www.R-project.org/ > posting-guide.html > and provide commented, minimal, self-contained, reproducible code. [[alternative HTML version deleted]] ______________________________________________ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
