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Binomial.resid_dev()

statsmodels.genmod.families.family.Binomial.resid_dev

Binomial.resid_dev(endog, mu, scale=1.0) [source]

Binomial deviance residuals

Parameters:

Parameters:	endog : array-like Endogenous response variable mu : array-like Fitted mean response variable scale : float, optional An optional argument to divide the residuals by scale. The default is 1.
Returns:	resid_dev : array Deviance residuals as defined below

endog : array-like

Endogenous response variable

mu : array-like

Fitted mean response variable

scale : float, optional

An optional argument to divide the residuals by scale. The default is 1.

Returns:

resid_dev : array

Deviance residuals as defined below

Notes

If the endogenous variable is binary:

$resid\_dev_i = sign(Y_i - \mu_i) * \sqrt{-2 * \log(I_{1,i} * \mu_i + I_{0,i} * (1 - \mu_i))}$

where $I_{1,i}$ is an indicator function that evalueates to 1 if $Y_i = 1$ . and $I_{0,i}$ is an indicator function that evaluates to 1 if $Y_i = 0$ .

If the endogenous variable is binomial:

$resid\_dev_i = sign(Y_i - \mu_i) \sqrt{2 * n_i * (Y_i * \log(Y_i / \mu_i) + (1 - Y_i) * \log(1 - Y_i)/(1 - \mu_i))}$

where $Y_i$ and $n$ are as defined in Binomial.initialize.

© 2009–2012 Statsmodels Developers
© 2006–2008 Scipy Developers
© 2006 Jonathan E. Taylor
Licensed under the 3-clause BSD License.
http://www.statsmodels.org/stable/generated/statsmodels.genmod.families.family.Binomial.resid_dev.html