statsmodels.sandbox.regression.gmm.IVGMMResults.t_test_pairwise¶
-
IVGMMResults.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.
- Parameters:¶
- term_name : str¶
The 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, list[str]}¶
The multiple testing p-value correction to apply. The default is ‘hs’. See stats.multipletesting.
- alpha : float¶
The significance level for multiple testing reject decision.
- factor_labels : {list[str], None}¶
Labels for the factor levels used for pairwise labels. If not provided, then the labels from the formula design_info are used.
- Returns:¶
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:¶
MultiCompResult
Notes
Status: experimental. Currently only checked for treatment coding with and without specified reference level.
Currently there are no multiple testing corrected confidence intervals available.
Examples
>>> 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