It is known that the bernoulli distribution is a special case of the binomial distribution, and when we look at the difference between the null deviance and residual deviance are equal when fitting a logistic regression model to both distributions. However, calculating the percentage deviance explained, it seems that the logistic regression model fitted with the binomial data explains far more than the bernoulli data.
I would expect the two models to have the same goodness of fit, but I'm not sure which conclusion to make:
A) The two models have the same goodness of fit since they reduce the same amount of deviance.
B) The two model fitted with the binomial data is better since it results in higher percentage deviance explained vs the mode fitted with the bernoulli data.
I would like to understand which conclusion is correct, and why I can't interpret it the other way. Additionally, are these two models equal? I don't think their response variables(data) are equal since binomial response deals with proportions, and the other deals with binary outcomes, but if this is the case, why do people say these two models are equal? (Unless that statement is wrong)
I did look at this resource: Logistic Regression: Bernoulli vs. Binomial Response Variables which speaks about the difference in deviance being equal for both models.