statsmodels.discrete.discrete_model.DiscreteModel class statsmodels.discrete.discrete_model.DiscreteModel(endog, exog, **kwargs) [source] Abstract class for discrete choice models. This class does not do anything itself but lays out the methods and call signature expected of child classes in addition to those of statsmodels.model.LikelihoodModel. Methods cdf(X) The cumulative distribution function of the model. cov_params_func_l1(likelihood_model, xopt, ...) Computes cov_params on a reduce

statsmodels.discrete.discrete_model.MultinomialResults.llr_pvalue static MultinomialResults.llr_pvalue()

statsmodels.sandbox.regression.gmm.IVRegressionResults.scale static IVRegressionResults.scale()

statsmodels.tsa.arima_process.ArmaProcess.from_coeffs classmethod ArmaProcess.from_coeffs(arcoefs, macoefs, nobs=100) [source] Create ArmaProcess instance from coefficients of the lag-polynomials Parameters: arcoefs : array-like Coefficient for autoregressive lag polynomial, not including zero lag. The sign is inverted to conform to the usual time series representation of an ARMA process in statistics. See the class docstring for more information. macoefs : array-like Coefficient for mov

statsmodels.discrete.discrete_model.NegativeBinomialResults.conf_int NegativeBinomialResults.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 th

statsmodels.sandbox.distributions.transformed.LogTransf_gen class statsmodels.sandbox.distributions.transformed.LogTransf_gen(kls, *args, **kwargs) [source] Distribution based on log/exp transformation the constructor can be called with a distribution class and generates the distribution of the transformed random variable Methods cdf(x, *args, **kwds) Cumulative distribution function of the given RV. entropy(*args, **kwds) Differential entropy of the RV. est_loc_scale(*args, **kwds) est_

statsmodels.robust.robust_linear_model.RLMResults.normalized_cov_params RLMResults.normalized_cov_params()

statsmodels.sandbox.regression.anova_nistcertified.anova_oneway statsmodels.sandbox.regression.anova_nistcertified.anova_oneway(y, x, seq=0) [source]

statsmodels.regression.linear_model.OLSResults.save OLSResults.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 remove_dat

statsmodels.sandbox.regression.gmm.IVRegressionResults.mse_resid static IVRegressionResults.mse_resid()