statsmodels.regression.linear_model.OLSResults.HC3_se static OLSResults.HC3_se() See statsmodels.RegressionResults

statsmodels.genmod.generalized_estimating_equations.GEEResults.llf static GEEResults.llf()

statsmodels.tsa.arima_model.ARMAResults.mafreq static ARMAResults.mafreq() [source] Returns the frequency of the MA roots. This is the solution, x, to z = abs(z)*exp(2j*np.pi*x) where z are the roots.

statsmodels.genmod.generalized_linear_model.GLMResults.bic static GLMResults.bic() [source]

statsmodels.tsa.arima_model.ARIMAResults.aic static ARIMAResults.aic()

statsmodels.graphics.gofplots.qqplot_2samples statsmodels.graphics.gofplots.qqplot_2samples(data1, data2, xlabel=None, ylabel=None, line=None, ax=None) [source] Q-Q Plot of two samples? quantiles. Can take either two ProbPlot instances or two array-like objects. In the case of the latter, both inputs will be converted to ProbPlot instances using only the default values - so use ProbPlot instances if finer-grained control of the quantile computations is required. Parameters: data1, data2 : a

statsmodels.discrete.discrete_model.DiscreteModel.cov_params_func_l1 DiscreteModel.cov_params_func_l1(likelihood_model, xopt, retvals) [source] Computes cov_params on a reduced parameter space corresponding to the nonzero parameters resulting from the l1 regularized fit. Returns a full cov_params matrix, with entries corresponding to zero?d values set to np.nan.

statsmodels.discrete.discrete_model.NegativeBinomial.initialize NegativeBinomial.initialize() Initialize is called by statsmodels.model.LikelihoodModel.__init__ and should contain any preprocessing that needs to be done for a model.

statsmodels.genmod.cov_struct.Nested class statsmodels.genmod.cov_struct.Nested(cov_nearest_method='clipped') [source] A nested working dependence structure. A working dependence structure that captures a nested hierarchy of groups, each level of which contributes to the random error term of the model. When using this working covariance structure, dep_data of the GEE instance should contain a n_obs x k matrix of 0/1 indicators, corresponding to the k subgroups nested under the top-level grou

statsmodels.sandbox.distributions.extras.ACSkewT_gen class statsmodels.sandbox.distributions.extras.ACSkewT_gen [source] univariate Skew-T distribution of Azzalini class follows scipy.stats.distributions pattern but with __init__ 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_loc_scale is deprecated! expect([func, args, loc, scale, lb, ub, ...]) Calculate expected val