Install Pakcages

install.packages(“MuMIn”)

#Stands for Multi-Model Inference #Calculates AICc scores #Automated model generation

install.packages(“AICcmodavg”)

Make Model

View(SD_banding_data) banding <- SD_banding_data banding_na<-na.omit(banding) model <- glm(fat~species+mass+tarsus, data = banding_na, family=gaussian)

band_model<- glm(fat~species+mass+tarsus, data = banding_na, family=gaussian, na.action = na.fail)

AICc_band_models <- dredge( #construct all possible models band_model, #use band model as a reference rank = “AICc”, #use AICc scores to compare fixed = “species”)

Check model output

summary(model)

Get model list

model_list <- get.models(AICc_band_models, #retrieve models from dredged data subset = TRUE) model_list[1] # Run ANOVA

anova(model)

Get AIC scores

AIC(model) AICc(model)

Get all model combos

AICc_band_models <- dredge(model, rank = “AICc”, fixed = “species”)

Make model name list

model_name_list<-NULL

for (i in 1:10){ model_name_list = c(model_name_list, as.character(model_list[[i]][[‘formula’]])) }

model_name_listb <- model_name_list[seq(3, length(model_name_list), 3)] # Make AIC table modavg_table<-aictab(model_list, modnames = model_name_list, second.ord = TRUE, sort = TRUE) modavg_table