statsmodels.tsa.ar_model.AutoRegResults.wald_test_terms¶
-
AutoRegResults.
wald_test_terms
(skip_single=False, extra_constraints=None, combine_terms=None)¶ Compute a sequence of Wald tests for terms over multiple columns.
This computes joined Wald tests for the hypothesis that all coefficients corresponding to a term are zero. Terms are defined by the underlying formula or by string matching.
- Parameters
- skip_singlebool
If true, then terms that consist only of a single column and, therefore, refers only to a single parameter is skipped. If false, then all terms are included.
- extra_constraints
ndarray
Additional constraints to test. Note that this input has not been tested.
- combine_terms{
list
[str
],None
} Each string in this list is matched to the name of the terms or the name of the exogenous variables. All columns whose name includes that string are combined in one joint test.
- Returns
WaldTestResults
The result instance contains table which is a pandas DataFrame with the test results: test statistic, degrees of freedom and pvalues.
Examples
>>> res_ols = ols("np.log(Days+1) ~ C(Duration, Sum)*C(Weight, Sum)", data).fit() >>> res_ols.wald_test_terms() <class 'statsmodels.stats.contrast.WaldTestResults'> F P>F df constraint df denom Intercept 279.754525 2.37985521351e-22 1 51 C(Duration, Sum) 5.367071 0.0245738436636 1 51 C(Weight, Sum) 12.432445 3.99943118767e-05 2 51 C(Duration, Sum):C(Weight, Sum) 0.176002 0.83912310946 2 51
>>> res_poi = Poisson.from_formula("Days ~ C(Weight) * C(Duration)", data).fit(cov_type='HC0') >>> wt = res_poi.wald_test_terms(skip_single=False, combine_terms=['Duration', 'Weight']) >>> print(wt) chi2 P>chi2 df constraint Intercept 15.695625 7.43960374424e-05 1 C(Weight) 16.132616 0.000313940174705 2 C(Duration) 1.009147 0.315107378931 1 C(Weight):C(Duration) 0.216694 0.897315972824 2 Duration 11.187849 0.010752286833 3 Weight 30.263368 4.32586407145e-06 4