statsmodels.discrete.discrete_model.LogitResults.wald_test_terms

LogitResults.wald_test_terms(skip_single=False, extra_constraints=None, combine_terms=None, scalar=True)

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, optional

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_constraintslist[tuple], optional

Additional constraints to test, given as a list of (name, constraint_matrix) tuples in the same form as the automatically generated term constraints. Note that this input has not been tested.

combine_termslist of str, optional

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 scalar float (the default) or as an array.

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