@cache_readonly
    def resid_pearson(self):
        # Pearson residuals
        #model = self.model
        endog = self.model.endog
        #exog = model.exog
        # M = # of individuals that share a covariate pattern
        # so M[i] = 2 for i = two share a covariate pattern
        # use unique row pattern?
        M = 1
        p = self.predict()
        return (endog - M*p)/np.sqrt(M*p*(1-p))
Selects f from the elements of the sequence.
dataset/ETHPy150Open statsmodels/statsmodels/statsmodels/discrete/discrete_model.py/BinaryResults.resid_pearson--crossover with--dataset/ETHPy150Open EntilZha/PyFunctional/functional/pipeline.py/Sequence.select