statsmodels.sandbox.tsa.fftarma.ArmaFft.spdshift ArmaFft.spdshift(n) [source] power spectral density using fftshift currently returns two-sided according to fft frequencies, use first half

statsmodels.genmod.generalized_estimating_equations.GEEMargins.tvalues static GEEMargins.tvalues() [source]

statsmodels.sandbox.distributions.extras.SkewNorm_gen.est_loc_scale SkewNorm_gen.est_loc_scale(*args, **kwds) est_loc_scale is deprecated! This function is deprecated, use self.fit_loc_scale(data) instead.

statsmodels.discrete.discrete_model.MultinomialResults.wald_test MultinomialResults.wald_test(r_matrix, cov_p=None, scale=1.0, invcov=None, use_f=None) Compute a Wald-test for a joint linear hypothesis. 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 test can be given as a string. See the examples.

statsmodels.iolib.table.SimpleTable.remove SimpleTable.remove() L.remove(value) ? remove first occurrence of value. Raises ValueError if the value is not present.

statsmodels.regression.quantile_regression.QuantRegResults.wald_test QuantRegResults.wald_test(r_matrix, cov_p=None, scale=1.0, invcov=None, use_f=None) Compute a Wald-test for a joint linear hypothesis. 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 test can be given as a string. See the examples.

statsmodels.sandbox.regression.anova_nistcertified.anova_ols statsmodels.sandbox.regression.anova_nistcertified.anova_ols(y, x) [source]

statsmodels.stats.inter_rater.cohens_kappa statsmodels.stats.inter_rater.cohens_kappa(table, weights=None, return_results=True, wt=None) [source] Compute Cohen?s kappa with variance and equal-zero test Parameters: table : array_like, 2-Dim square array with results of two raters, one rater in rows, second rater in columns weights : array_like The interpretation of weights depends on the wt argument. If both are None, then the simple kappa is computed. see wt for the case when wt is not N

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

statsmodels.genmod.cov_struct.Exchangeable.covariance_matrix_solve Exchangeable.covariance_matrix_solve(expval, index, stdev, rhs) [source] Solves matrix equations of the form covmat * soln = rhs and returns the values of soln, where covmat is the covariance matrix represented by this class. Parameters: expval: array-like : The expected value of endog for each observed value in the group. index: integer : The group index. stdev : array-like The standard deviation of endog for each obse