GEEResults.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: |
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Returns: |
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.
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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
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© 2006–2008 Scipy Developers
© 2006 Jonathan E. Taylor
Licensed under the 3-clause BSD License.
http://www.statsmodels.org/stable/generated/statsmodels.genmod.generalized_estimating_equations.GEEResults.t_test_pairwise.html