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How to calculate BIC and AIC for a GMM model in R?
I am following the answer to this question. For further reference on the AIC criteria, you can look at Wikipedia. Here is the code that should work. However, you didn’t provide any reproducible model estimation. Hence, this is without validation for your case.
How to calculate AIC for GLm Stack Overflow?
For generalized linear models (i.e., for lm, aov, and glm), -2log L is the deviance, as computed by deviance (fit). k = 2 corresponds to the traditional AIC, using k = log (n) provides the BIC (Bayes IC) instead. glm_a1$ranks returns the number of fitted parameter without accounting for the fitted variance used in gaussian families.
How to define the BIC function in R?
BIC is defined as AIC (object, …, k = log (nobs (object))) . This needs the number of observations to be known: the default method looks first for a “nobs” attribute on the return value from the logLik method, then tries the nobs generic, and if neither succeed returns BIC as NA.
What does data.frame mean in AIC function?
If multiple objects are provided, a data.frame with rows corresponding to the objects and columns representing the number of parameters in the model ( df) and the AIC or BIC. When comparing models fitted by maximum likelihood to the same data, the smaller the AIC or BIC, the better the fit.
When to pick a model with lower AIC?
In the first case you have two models (1 and 2) and you obtained their AIC like A I C 1 and A I C 2. IF you want to compare these two models based on their AIC’s, then model with lower AIC would be the preferred one i.e. if A I C 1 < A I C 2 then you pick up model 1 and vise versa.
How to find the AIC of an object?
If just one object is provided, a numeric value with the corresponding AIC (or BIC, or …, depending on k ). If multiple objects are provided, a data.frame with rows corresponding to the objects and columns representing the number of parameters in the model ( df) and the AIC or BIC.