statsmodels.iolib.table.SimpleTable.get_colwidths SimpleTable.get_colwidths(output_format, **fmt_dict) [source] Return list, the widths of each column.

statsmodels.duration.hazard_regression.PHRegResults.baseline_cumulative_hazard_function static PHRegResults.baseline_cumulative_hazard_function() [source] A list (corresponding to the strata) containing function objects that calculate the cumulative hazard function.

statsmodels.regression.mixed_linear_model.MixedLMResults.bse_fe static MixedLMResults.bse_fe() [source] Returns the standard errors of the fixed effect regression coefficients.

statsmodels.iolib.smpickle.save_pickle statsmodels.iolib.smpickle.save_pickle(obj, fname) [source] Save the object to file via pickling. Parameters: fname : str Filename to pickle to

statsmodels.tsa.varma_process.VarmaPoly.vstack VarmaPoly.vstack(a=None, name='ar') [source] stack lagpolynomial vertically in 2d array

statsmodels.sandbox.regression.gmm.IVGMMResults.pvalues static IVGMMResults.pvalues()

statsmodels.regression.quantile_regression.QuantRegResults.fittedvalues static QuantRegResults.fittedvalues()

statsmodels.discrete.discrete_model.DiscreteResults.f_test DiscreteResults.f_test(r_matrix, cov_p=None, scale=1.0, invcov=None) Compute the F-test for a joint linear hypothesis. This is a special case of wald_test that always uses the F distribution. Parameters: r_matrix : array-like, str, or tuple array : An r x k array where r is the number of restrictions to test and k is the number of regressors. It is assumed that the linear combination is equal to zero. str : The full hypotheses to t

statsmodels.discrete.discrete_model.LogitResults.bse static LogitResults.bse()

statsmodels.iolib.smpickle.load_pickle statsmodels.iolib.smpickle.load_pickle(fname) [source] Load a previously saved object from file Parameters: fname : str Filename to unpickle Notes This method can be used to load both models and results.