'Pearson residuals
Notes
Pearson residuals are defined to be
.. math:: r_j = \frac{(y - M_jp_j)}{\sqrt{M_jp_j(1-p_j)}}
where :math:`p_j=cdf(X\beta)` and :math:`M_j` is the total number of
observations sharing the covariate pattern :math:`j`.
For now :math:`M_j` is always set to 1.'

@cache_readonly
def resid_pearson(self):

    endog = self.model.endog
    M = 1
    p = self.predict()
    return ((endog - (M * p)) / np.sqrt(((M * p) * (1 - p))))
