Poisson model for non-integer I have a GLM with (quasi)poisson family.
My dataset has 3 variables:
rate_data
rate_benchmark
X
So fitting the model:
model <- glm(formula = rate_data ~ offset(log(rate_benchmark)) + X - 1, family = (quasi)poisson, data = data)

model_null <- glm(formula = rate_data ~ offset(log(rate_benchmark)) - 1, family = (quasi)poisson, data = data)


When using "poisson" it gives me warnings about non-integer values, which it doesnt give me for the quasipoisson. However, when testing for my beta being zero anova(model_null, model, test = "LRT") it gives me completely different deviance (hence also different p-values).
Which model am I supposed to use? My first thought was using quasipoisson, but no warnings does not necessarily mean it is correct.

The Poisson distribution deals with counts -- the actual number of objects you counted in a defined volume,  or the actual number of events you counted in a defined period of time.
If you normalized to a rate, the distribution is not Poisson.
