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

statsmodels.sandbox.regression.gmm.NonlinearIVGMM.momcond NonlinearIVGMM.momcond(params)

Formulas: Fitting models using R-style formulas Link to Notebook GitHub Since version 0.5.0, statsmodels allows users to fit statistical models using R-style formulas. Internally, statsmodels uses the patsy package to convert formulas and data to the matrices that are used in model fitting. The formula framework is quite powerful; this tutorial only scratches the surface. A full description of the formula language can be found in the patsy docs: Patsy formula language description Loading m

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]