NegativeBinomialResults.t_test_pairwise(term_name, method='hs', alpha=0.05, factor_labels=None)

perform pairwise t_test with multiple testing corrected p-values

This uses the formula design_info encoding contrast matrix and should work for all encodings of a main effect.

  • result (result instance) – The results of an estimated model with a categorical main effect.

  • term_name (str) – name of the term for which pairwise comparisons are computed. Term names for categorical effects are created by patsy and correspond to the main part of the exog names.

  • method (str or list of strings) – multiple testing p-value correction, default is ‘hs’, see stats.multipletesting

  • alpha (float) – significance level for multiple testing reject decision.

  • factor_labels (None, list of str) – Labels for the factor levels used for pairwise labels. If not provided, then the labels from the formula design_info are used.


results – The results are stored as attributes, the main attributes are the following two. Other attributes are added for debugging purposes or as background information.

  • result_frame : pandas DataFrame with t_test results and multiple testing corrected p-values.

  • contrasts : matrix of constraints of the null hypothesis in the t_test.

Return type

instance of a simple Results class


Status: experimental. Currently only checked for treatment coding with and without specified reference level.

Currently there are no multiple testing corrected confidence intervals available.


>>> res = ols("np.log(Days+1) ~ C(Weight) + C(Duration)", data).fit()
>>> pw = res.t_test_pairwise("C(Weight)")
>>> pw.result_frame
         coef   std err         t         P>|t|  Conf. Int. Low
2-1  0.632315  0.230003  2.749157  8.028083e-03        0.171563
3-1  1.302555  0.230003  5.663201  5.331513e-07        0.841803
3-2  0.670240  0.230003  2.914044  5.119126e-03        0.209488
     Conf. Int. Upp.  pvalue-hs reject-hs
2-1         1.093067   0.010212      True
3-1         1.763307   0.000002      True
3-2         1.130992   0.010212      True