Below the formula and output of a glm to determine if there is a relation between hatching success (proportional data from 0 to 1 + skewed towards the 1) and some other variables such as species, location and average temperature. With aid of the AIC I want to figure out which formula fits best (thus, to get the smallest AIC), but I don't get any AIC value at all! What went wrong?
Call:
glm(formula = Success ~ Species + Location + `Average temperature`,
family = quasibinomial("logit"), data = dd)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.4514 -0.5189 0.2341 0.6552 0.9869
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -5.99750 18.90781 -0.317 0.753
SpeciesRicordii -0.36436 0.60982 -0.597 0.553
LocationPuente Arriba -0.69432 0.90582 -0.767 0.448
LocationTierra -0.85959 0.71708 -1.199 0.237
`Average temperature` 0.08536 0.21218 0.402 0.689
(Dispersion parameter for quasibinomial family taken to be 0.471929)
Null deviance: 24.064 on 47 degrees of freedom
Residual deviance: 23.315 on 43 degrees of freedom
(178 observations deleted due to missingness)
AIC: NA
Number of Fisher Scoring iterations: 4
Nothing went wrong. It's not a bug- it's a feature :)
You used a quasi-model, and in quasi-models there is no valid definition of a likelihood, hence no AIC, BIC etc. values.
If you want AIC, you need a model allowing you to get a proper likelihood. In your case that would be a beta-model would work. You get it in the package gmlss and betareg, among others.
Thanks Jochen Wilhelm ! Is there perhaps another method to see if you're quasi-model is good?
Jochen Wilhelm Hello, I am interested in your answer above. So, the AIC score doesn't have any meaning if we run a quasipoisson?
I read in some journals about the lower the value of AIC scores, the more plausible the model adjustment is to the data.
I got a NA too when I run quasipoisson.
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