statsmodels.discrete.count_model.ZeroInflatedGeneralizedPoissonResults.conf_int

ZeroInflatedGeneralizedPoissonResults.conf_int(alpha=0.05, cols=None, method='default')

Returns the confidence interval of the fitted parameters.

Parameters:	alpha (float, optional) – The significance level for the confidence interval. ie., The default alpha = .05 returns a 95% confidence interval. cols (array-like, optional) – cols specifies which confidence intervals to return method (string) – Not Implemented Yet Method to estimate the confidence_interval. “Default” : uses self.bse which is based on inverse Hessian for MLE “hjjh” : “jac” : “boot-bse” “boot_quant” “profile”
Returns:	conf_int – Each row contains [lower, upper] limits of the confidence interval for the corresponding parameter. The first column contains all lower, the second column contains all upper limits.
Return type:	array

Examples

>>> import statsmodels.api as sm
>>> data = sm.datasets.longley.load()
>>> data.exog = sm.add_constant(data.exog)
>>> results = sm.OLS(data.endog, data.exog).fit()
>>> results.conf_int()
array([[-5496529.48322745, -1467987.78596704],
       [    -177.02903529,      207.15277984],
       [      -0.1115811 ,        0.03994274],
       [      -3.12506664,       -0.91539297],
       [      -1.5179487 ,       -0.54850503],
       [      -0.56251721,        0.460309  ],
       [     798.7875153 ,     2859.51541392]])

>>> results.conf_int(cols=(2,3))
array([[-0.1115811 ,  0.03994274],
       [-3.12506664, -0.91539297]])

Notes

The confidence interval is based on the standard normal distribution. Models wish to use a different distribution should overwrite this method.