def get_influence(self):
    '\n        Influence and outlier measures\n\n        See notes section for influence measures that do not apply for\n        zero inflated models.\n\n        Returns\n        -------\n        MLEInfluence\n            The instance has methods to calculate the main influence and\n            outlier measures as attributes.\n\n        See Also\n        --------\n        statsmodels.stats.outliers_influence.MLEInfluence\n\n        Notes\n        -----\n        ZeroInflated models have functions that are not differentiable\n        with respect to sample endog if endog=0. This means that generalized\n        leverage cannot be computed in the usual definition.\n\n        Currently, both the generalized leverage, in `hat_matrix_diag`\n        attribute and studetized residuals are not available. In the influence\n        plot generalized leverage is replaced by a hat matrix diagonal that\n        only takes combined exog into account, computed in the same way as\n        for OLS. This is a measure for exog outliers but does not take\n        specific features of the model into account.\n        '
    from statsmodels.stats.outliers_influence import MLEInfluence
    return MLEInfluence(self)
Influence and outlier measures

See notes section for influence measures that do not apply for
zero inflated models.

Returns
-------
MLEInfluence
    The instance has methods to calculate the main influence and
    outlier measures as attributes.

See Also
--------
statsmodels.stats.outliers_influence.MLEInfluence

Notes
-----
ZeroInflated models have functions that are not differentiable
with respect to sample endog if endog=0. This means that generalized
leverage cannot be computed in the usual definition.

Currently, both the generalized leverage, in `hat_matrix_diag`
attribute and studetized residuals are not available. In the influence
plot generalized leverage is replaced by a hat matrix diagonal that
only takes combined exog into account, computed in the same way as
for OLS. This is a measure for exog outliers but does not take
specific features of the model into account.
def get_influence(self):
    '\n        Influence and outlier measures\n\n        See notes section for influence measures that do not apply for\n        zero inflated models.\n\n        Returns\n        -------\n        MLEInfluence\n            The instance has methods to calculate the main influence and\n            outlier measures as attributes.\n\n        See Also\n        --------\n        statsmodels.stats.outliers_influence.MLEInfluence\n\n        Notes\n        -----\n        ZeroInflated models have functions that are not differentiable\n        with respect to sample endog if endog=0. This means that generalized\n        leverage cannot be computed in the usual definition.\n\n        Currently, both the generalized leverage, in `hat_matrix_diag`\n        attribute and studetized residuals are not available. In the influence\n        plot generalized leverage is replaced by a hat matrix diagonal that\n        only takes combined exog into account, computed in the same way as\n        for OLS. This is a measure for exog outliers but does not take\n        specific features of the model into account.\n        '
    from statsmodels.stats.outliers_influence import MLEInfluence
    return MLEInfluence(self)