Dear List, Once again I ask for your kind advice. I am analyzing the structure of pollination networks in conventional vs organic avocado farms. I have four different response variables: interaction richness (a count), interaction evenness, interaction selectivity and nestedness (all three take values in the [0, 1] interval). The explanatory variables are: farm type (fixed factor, 2 levels), sampling season (fixed factor, 2 levels), and farm identity (random factor). Additionally, the number of species in the network (a count) might affect some of the response variables (interaction richness and nestedness probably, although I am not sure about the other two), so I decided to "correct" for that effect in some of the models. I am generally familiar with models dealing with count data, but I am not too confident modelling 0-1 bounded continuous data. For each variable I've searched the literature and online resources to try and find the best R function, model and error structure.
I have pasted below a workable code that deals sequentially with each response variable. Would you say the models correctly are defined in each case? I am particularly concerned with (1) the suitability of the "simulateResidual" function in the Dharma package (which I recently learned about) as a way to check the fit of the model, and (2) the validity of the offset term in model 4. Your comments and suggestions will be greatly appreciated. Best regards, Mariano ############################### require(RCurl); require(visreg); require(lme4); require(DHARMa); library(lmerTest); require(glmmTMB); require(effects) my.file <- getURL(" https://docs.google.com/spreadsheets/d/e/2PACX-1vRn3_aM-OlKldXlEB45qKjL9jMoY_-CP2saOI8HteTTx4_AZv-card1sce4MDbqwYJ8kllJUaysfcBR/pub?output=csv ") avocado_data <- read.csv(textConnection(my.file), head=T) str(avocado_data) ##Model for Interaction richness model1 <- glmer(irich ~ offset(log(species)) + type + season + (1|farm), family=poisson, data=avocado_data) summary(model1) #diagnostic plots plot(model1) simulationOutput <- simulateResiduals(fittedModel = model2, n = 1000) testOverdispersion(simulationOutput = simulationOutput, alternative ="greater") #the data are not overdispersed relative to the Poisson distribution plotSimulatedResiduals(simulationOutput = simulationOutput) visreg(model1, pch=16, cex=1.5, rug=FALSE, ylab="Interaction richness", line.par=list(col="darkgreen"), points.par=list(col="red", cex=1.5), overlay=TRUE) ##Model for Interaction evenness model2 <- lmer(ieven~ type + season + (1|farm), data=avocado_data) summary(model2) #diagnostic plots plot(model2) simulationOutput <- simulateResiduals(fittedModel = model2, n = 1000) plotSimulatedResiduals(simulationOutput = simulationOutput) visreg(model2, pch=16, cex=1.5, rug=FALSE, ylab="Interaction evenness", line.par=list(col="darkgreen"), points.par=list(col="red", cex=1.5), overlay=TRUE) ##Model for Interaction selectivity model3 <- lmer(h2 ~ type + season + (1|farm), data=avocado_data) summary(model3) #diagnostic plots plot(model3) simulationOutput <- simulateResiduals(fittedModel = model3, n = 1000) plotSimulatedResiduals(simulationOutput = simulationOutput) visreg(model3, pch=16, cex=2, rug=FALSE, ylab="Interaction selectivity", line.par=list(col="darkgreen"), points.par=list(col="red", cex=1.5), overlay=TRUE) #model for Nestedness model4 <- glmmTMB(nestedness/100 ~ offset(log(species)) + type + season + (1|farm), data=avocado_data, family=list(family="beta", link="logit")) summary(model4) #diagnostic plots simulationOutput <- simulateResiduals(fittedModel = model4, n = 1000) plotSimulatedResiduals(simulationOutput = simulationOutput) visreg(model4, pch=16, cex=2, rug=FALSE, ylab="Nestedness", line.par=list(col="darkgreen"), points.par=list(col="red", cex=1.5), overlay=TRUE) #does not work with glmmTMB :-( plot(allEffects(model4)) #doesn't work either . Any alternatives? ############################### *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>* [[alternative HTML version deleted]] _______________________________________________ R-sig-ecology mailing list R-sig-ecology@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-ecology