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The residual deviance is higher for the more complex model. Why is this the case? Can I compare the residual deviance of a GLM and a multi-level model in this way or should I use something else instead like the AIC? (which in this case gives a lower AIC for the more complex model, m2).

Poisson GLM model:

m1 <- glm(Count ~ Month + Year + Region , data = df, family = poisson(link = "log"))
summary(m1) 

residual deviance = 2465.0

Poisson multi-level model:

library(lme4)
m2 <- glmer(Count ~ Month + Year + Region  + (1|Year), data = df, family = poisson(link = "log"))
summary(m2)

residual deviance = 3921.4

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