statsmodels.robust.robust_linear_model.RLMResults.conf_int RLMResults.conf_int(alpha=0.05, cols=None, method='default') Returns the confidence interval of the fitted parameters. Parameters: alpha : float, optional The significance level for the confidence interval. ie., The default alpha = .05 returns a 95% confidence interval. cols : array-like, optional cols specifies which confidence intervals to return method : string Not Implemented Yet Method to estimate the confidence_interval.

statsmodels.tsa.vector_ar.var_model.VARProcess.acorr VARProcess.acorr(nlags=None) [source] Compute theoretical autocorrelation function Returns: acorr : ndarray (p x k x k)

statsmodels.stats.proportion.samplesize_confint_proportion statsmodels.stats.proportion.samplesize_confint_proportion(proportion, half_length, alpha=0.05, method='normal') [source] find sample size to get desired confidence interval length Parameters: proportion : float in (0, 1) proportion or quantile half_length : float in (0, 1) desired half length of the confidence interval alpha : float in (0, 1) significance level, default 0.05, coverage of the two-sided interval is (approximatel

statsmodels.genmod.generalized_estimating_equations.GEE.cluster_list GEE.cluster_list(array) [source] Returns array split into subarrays corresponding to the cluster structure.

statsmodels.genmod.generalized_estimating_equations.GEEMargins.conf_int GEEMargins.conf_int(alpha=0.05) [source] Returns the confidence intervals of the marginal effects Parameters: alpha : float Number between 0 and 1. The confidence intervals have the probability 1-alpha. Returns: conf_int : ndarray An array with lower, upper confidence intervals for the marginal effects.

statsmodels.stats.weightstats.ttest_ind statsmodels.stats.weightstats.ttest_ind(x1, x2, alternative='two-sided', usevar='pooled', weights=(None, None), value=0) [source] ttest independent sample convenience function that uses the classes and throws away the intermediate results, compared to scipy stats: drops axis option, adds alternative, usevar, and weights option Parameters: x1, x2 : array_like, 1-D or 2-D two independent samples, see notes for 2-D case alternative : string The altern

statsmodels.genmod.generalized_estimating_equations.GEE.fit GEE.fit(maxiter=60, ctol=1e-06, start_params=None, params_niter=1, first_dep_update=0, cov_type='robust') [source] Fits a marginal regression model using generalized estimating equations (GEE). Parameters: maxiter : integer The maximum number of iterations ctol : float The convergence criterion for stopping the Gauss-Seidel iterations start_params : array-like A vector of starting values for the regression coefficients. If Non

statsmodels.discrete.discrete_model.DiscreteResults.save DiscreteResults.save(fname, remove_data=False) save a pickle of this instance Parameters: fname : string or filehandle fname can be a string to a file path or filename, or a filehandle. remove_data : bool If False (default), then the instance is pickled without changes. If True, then all arrays with length nobs are set to None before pickling. See the remove_data method. In some cases not all arrays will be set to None. Notes If

statsmodels.sandbox.regression.gmm.NonlinearIVGMM.fitstart NonlinearIVGMM.fitstart() [source]

statsmodels.regression.linear_model.OLS.score OLS.score(params) Score vector of model. The gradient of logL with respect to each parameter.