statsmodels.discrete.count_model.ZeroInflatedNegativeBinomialResults.conf_int¶

ZeroInflatedNegativeBinomialResults.conf_int(alpha=0.05, cols=None)¶

Construct confidence interval for the fitted parameters.

Parameters

alphafloat, optional: The significance level for the confidence interval. The default alpha = .05 returns a 95% confidence interval.
colsarray_like, optional: Specifies which confidence intervals to return.

Returns

array_like: 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.

Notes

The confidence interval is based on the standard normal distribution if self.use_t is False. If self.use_t is True, then uses a Student’s t with self.df_resid_inference (or self.df_resid if df_resid_inference is not defined) degrees of freedom.

Examples

>>> import statsmodels.api as sm
>>> data = sm.datasets.longley.load(as_pandas=False)
>>> 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]])