statsmodels.discrete.discrete_model.LogitResults.resid_pearson¶

LogitResults.resid_pearson()¶

Pearson residuals

Notes

Pearson residuals are defined to be

$r_j = \frac{(y - M_jp_j)}{\sqrt{M_jp_j(1-p_j)}}$

where $p_j=cdf(X\beta)$ and $M_j$ is the total number of observations sharing the covariate pattern $j$ .

For now $M_j$ is always set to 1.