PHRegResults.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.

resultresult instance

The results of an estimated model with a categorical main effect.


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.

methodstr or list of strings

multiple testing p-value correction, default is ‘hs’, see stats.multipletesting


significance level for multiple testing reject decision.

factor_labelsNone, 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.

resultsinstance of a simple Results class

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.


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