Likelihood ratio test statsmodels

models, python, statistics, statsmodels

Solution

I don't see any problem.

Generalized Linear Models are Maximum Likelihood models, if the scale is the one implied by the family.

statsmodels.GLM doesn't currently implement Quasi-Likelihood methods where the scale can deviate from those of the underlying family, e.g. overdispersed Poisson, so the Likelihood Ratio test can be applied.

implementation detail: `compare_lr_test` was supposed to be added for all LikelihoodModels, but I didn't check whether it's correct (or raises exception) for all models that inherit from it.

Problem

In statsmodels ordinary least squares have likelihood ratio test implemented ``` OLSResults.compare_lr_test(restricted) ``` That is not true for the generalized linear model (GLM). I tried to implemtent copyng the OLS implementation: ``` from scipy import stats llf_full = results.llf llf_restr = results_res.llf df_full = results.df_resid df_restr = results_res.df_resid lrdf = (df_restr - df_full) lrstat = -2*(llf_restr - llf_full) lr_pvalue = stats.chi2.sf(lrstat, df=lrdf) lr_pvalue ``` it looks strightforward, but the fact that this is not implemented make me suspicious. Is this correct?

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