statsmodels.tsa.statespace.mlemodel.MLEResults.wald_test_terms¶
-
MLEResults.
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_single (boolean) – 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) – not tested yet
- combine_terms (None or list of strings) – 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: test_result – The result instance contains table which is a pandas DataFrame with the test results: test statistic, degrees of freedom and pvalues.
Return type: result instance
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