static NegativeBinomialResults.bse()

statsmodels.discrete.discrete_model.NegativeBinomialResults.bse static NegativeBinomialResults.bse()

static MultinomialResults.pvalues()

statsmodels.discrete.discrete_model.MultinomialResults.pvalues static MultinomialResults.pvalues()

static MultinomialResults.resid_misclassified()

statsmodels.discrete.discrete_model.MultinomialResults.resid_misclassified static MultinomialResults.resid_misclassified() [source] Residuals indicating which observations are misclassified. Notes The residuals for the multinomial model are defined as where is the index of the category for the endogenous variable and is the index of the predicted probabilities for each category. That is, the residual is a binary indicator that is 0 if the category with the highest predicted probability

static MultinomialResults.tvalues()

statsmodels.discrete.discrete_model.MultinomialResults.tvalues static MultinomialResults.tvalues() Return the t-statistic for a given parameter estimate.

static MultinomialResults.prsquared()

statsmodels.discrete.discrete_model.MultinomialResults.prsquared static MultinomialResults.prsquared()

static MultinomialResults.llr_pvalue()

statsmodels.discrete.discrete_model.MultinomialResults.llr_pvalue static MultinomialResults.llr_pvalue()

static MultinomialResults.llr()

statsmodels.discrete.discrete_model.MultinomialResults.llr static MultinomialResults.llr()

static MultinomialResults.llnull()

statsmodels.discrete.discrete_model.MultinomialResults.llnull static MultinomialResults.llnull()

static MultinomialResults.fittedvalues()

statsmodels.discrete.discrete_model.MultinomialResults.fittedvalues static MultinomialResults.fittedvalues()

static MultinomialResults.llf()

statsmodels.discrete.discrete_model.MultinomialResults.llf static MultinomialResults.llf()