python - Missing Significance level for Pearson Chi-Squared Test -


i'm trying switch on stata , r python's statsmodels. find can of need missing when running negative binomial regression -- specifically, significance level chi-squared test. example, model this:

mod1 = smf.glm(formula='like_count ~ source + followers_count + \     mission_focus_patient_advocacy + assets + hashtag_count, data=df,   \     missing='drop', hasconst=none, family=sm.families.negativebinomial()).fit()

from can run mod1.summary() , main model results, seen below:

enter image description here

typically report number of observations, there, log-likelihood value, there, pearson chi-squared value, there, along 3 pieces of missing information: 1) significance level chi-squared test, 2) pseudo-r2 value such mcfadden pseudo-r-squared (i know should not reviewers want see it), , 3) results of likelihood-ratio chi-squared test (to show poisson model not work instead).

my question is, how can these things? know can access specific model results individually, including:

mod1.llf           #get log-likelihood mod1.pearson_chi2  #get chi-square

checking online documentation can't find parameters i'm looking for. contrast, know logit model, instance, following:

mod_logit.llf        #get log-likelihood mod_logit.llr        #get chi-square mod_logit.llr_pvalue #get sig. level of chi-square test mod_logit.prsquared  #get pseudo-rsquared

but in negative binomial can't seem find way significance level chi-squared test. @ bare minimum i'd this. ideally, i'd able likelihood-ratio test results pseudo-r-squared. in advance help.


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