# statsmodels.discrete.discrete_model.LogitResults.resid_dev¶

LogitResults.resid_dev()

Deviance residuals

Notes

Deviance residuals are defined

$d_j = \pm\left(2\left[Y_j\ln\left(\frac{Y_j}{M_jp_j}\right) + (M_j - Y_j\ln\left(\frac{M_j-Y_j}{M_j(1-p_j)} \right) \right] \right)^{1/2}$

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.