Login Register

static BinaryResults.resid_dev()

statsmodels.discrete.discrete_model.BinaryResults.resid_dev

static BinaryResults.resid_dev() [source]

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.

Links:

http://statsmodels.sourceforge.net/stable/generated/statsmodels.discrete.discrete_model.BinaryResults.resid_dev.html

doc_statsmodels

2025-01-10 15:47:30

Comments

Leave a Comment

Please login to continue.

Popular Articles

static BinaryResults.resid_response() statsmodels.discrete.discrete_model.BinaryResults.resid_response static BinaryResults.resid_response() [source] The response residuals Notes Respo

static BinaryResults.resid_pearson() statsmodels.discrete.discrete_model.BinaryResults.resid_pearson static BinaryResults.resid_pearson() [source] Pearson residuals Notes Pearson resi

static ProbitResults.resid_dev() statsmodels.discrete.discrete_model.ProbitResults.resid_dev static ProbitResults.resid_dev() Deviance residuals Notes Deviance residuals are defin

InverseGaussian.resid_dev() statsmodels.genmod.families.family.InverseGaussian.resid_dev InverseGaussian.resid_dev(endog, mu, scale=1.0) [source] Returns the deviance residua

static LogitResults.resid_dev() statsmodels.discrete.discrete_model.LogitResults.resid_dev static LogitResults.resid_dev() Deviance residuals Notes Deviance residuals are defined