Hi Mariano, (Sorry for resending. I accidentally didn’t reply-all.) Because Poisson GLMs use a log link, your offset should be log(cap).
model2 <- glm(visits ~ offset(log(cap)) + block + hybrid + distance, family=poisson, data=sunflower_data) Then, using the DHARMa package, I can see the data are overdispersed relative to the Poisson distribution. library(DHARMa) simulationOutput <- simulateResiduals(fittedModel = model2, n = 1000) testDispersion(simulationOutput = simulationOutput, alternative ="greater") So you should use the negative binomial distribution instead. library(MASS) model3 <- glm.nb(visits ~ offset(log(cap)) + block + hybrid + distance, data=sunflower_data) You can also check residuals using DHARMa (https://cran.r-project.org/web/packages/DHARMa/vignettes/DHARMa.html <https://cran.r-project.org/web/packages/DHARMa/vignettes/DHARMa.html>) simulationOutput <- simulateResiduals(fittedModel = model3, n = 1000) plotSimulatedResiduals(simulationOutput = simulationOutput) cheers, Mollie ——————————— Mollie E. Brooks, Ph.D. Research Scientist National Institute of Aquatic Resources Technical University of Denmark > On 15May 2018, at 17:26, Mariano Devoto <mdev...@agro.uba.ar> wrote: > > Hi. I am trying to model the visits of pollinators to sunflower heads in a > field experiment. My response variable is the number of insect visits in > 15' periods looking at a variable number of flower heads at a time. The > experiment includes three sunflower hybrids. Samples were taken at three > distances from the field margin (2, 20 and 100m). There are two complete > blocks in the experiment. I added the number of flower heads as an offset > variable as they varied among samples. > > Here is a workable code: > > require(RCurl) #need this to download data from Google Drive > my.file <- getURL(" > https://docs.google.com/spreadsheets/d/e/2PACX-1vQq-Clbksm1S3Qq2_V9FBuEfUVno11Dytk0v9eJ2j7e7FpXHYqigaqJlcTyt5u2ipfVUjrKbAFL294e/pub?output=csv > ") > sunflower_data <- read.csv(textConnection(my.file), head=T) > str(sunflower_data) > model1 <- glm(visits ~ offset(cap) + block + hybrid + distance, > family=poisson, data=sunflower_data) > summary(model1) > plot(model1) > #First diagnostic plot suggests residuals have a trend > #Other diagnostic plots suggest observation #35 is too influential, so... > > model2 <- glm(visits ~ offset(cap) + block + hybrid + distance, > family=poisson, data=sunflower_data[-35,]) > summary(model2) > plot(model2) > #things look a bit better, but residuals in diagnostic plot #1 still show a > trend. What should I try next? > > Any advice is much appreciated. > > Mariano > > *Dr. Mariano Devoto* > > Profesor Adjunto - Cátedra de Botánica General, Facultad de Agronomía de la > UBA > Investigador Adjunto del CONICET > > Av. San Martín 4453 - C1417DSE - C. A. de Buenos Aires - Argentina > +5411 4524-8069 > *https://www.researchgate.net/profile/Mariano_Devoto > <https://www.researchgate.net/profile/Mariano_Devoto>* > > <http://www.avg.com/email-signature?utm_medium=email&utm_source=link&utm_campaign=sig-email&utm_content=webmail> > Libre > de virus. www.avg.com > <http://www.avg.com/email-signature?utm_medium=email&utm_source=link&utm_campaign=sig-email&utm_content=webmail> > <#DAB4FAD8-2DD7-40BB-A1B8-4E2AA1F9FDF2> > > [[alternative HTML version deleted]] > > _______________________________________________ > R-sig-ecology mailing list > R-sig-ecology@r-project.org > https://stat.ethz.ch/mailman/listinfo/r-sig-ecology [[alternative HTML version deleted]] _______________________________________________ R-sig-ecology mailing list R-sig-ecology@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-ecology