AICc or AIC for binomial model with splited or grouped data
As shown in a previous plot, the two ways to use binomial data (grouped by independent variable or splited for each individual) produced two different estimations of R2 or Nagelkerke R2: https://biostatsr.blogspot.com/2021/06/nagelkerke-r2-for-binomial-data-is.html Let try if the problem occurs with AIC and AICc: Grouped data: n <- c(3, 4, 5, 2, 2) data_grouped <- data.frame(x=n, y=5-n) cof_grouped <- c(10, 7, 2, 3, 4) res_grouped <- glm(cbind(x,y) ~ cof_grouped, data=data_grouped, family=binomial()) cof_grouped2 <- c(9, 7, 2, 3, 4) res_grouped2 <- glm(cbind(x,y) ~ cof_grouped2, data=data_grouped, family=binomial()) library(AICcmodavg) # or library(MuMIn) AIC(res_grouped2)-AIC(res_grouped) AICc(res_grouped2)-AICc(res_grouped) Splited data: n <- c(3, 4, 5, 2, 2) data_splited <- data.frame(x=unlist(lapply(n, ...