statsmodels.discrete.count_model.ZeroInflatedPoissonResults.conf_int¶

ZeroInflatedPoissonResults.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.

.. deprecated: 0.13

cols is deprecated and will be removed after 0.14 is released. cols only works when inputs are NumPy arrays and will fail when using pandas Series or DataFrames as input. You can subset the confidence intervals using slices.

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()
>>> 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]])