BinaryResults.wald_test_terms(skip_single=False, extra_constraints=None, combine_terms=None, scalar=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.


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.


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.

scalarbool, optional

Flag indicating whether the Wald test statistic should be returned as a sclar float. The current behavior is to return an array. This will switch to a scalar float after 0.14 is released. To get the future behavior now, set scalar to True. To silence the warning and retain the legacy behavior, set scalar to False.


The result instance contains table which is a pandas DataFrame with the test results: test statistic, degrees of freedom and pvalues.


>>> 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

Last update: Jul 16, 2024