Graphics Goodness of Fit Plots gofplots.qqplot(data[, dist, distargs, a, ...]) Q-Q plot of the quantiles of x versus the quantiles/ppf of a distribution. gofplots.qqline(ax, line[, x, y, dist, fmt]) Plot a reference line for a qqplot. gofplots.qqplot_2samples(data1, data2[, ...]) Q-Q Plot of two samples? quantiles. gofplots.ProbPlot(data[, dist, fit, ...]) Class for convenient construction of Q-Q, P-P, and probability plots. Boxplots boxplots.violinplot(data[, ax, labels, ...]) Make a v

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

statsmodels.genmod.families.links.NegativeBinomial.inverse_deriv NegativeBinomial.inverse_deriv(z) [source] Derivative of the inverse of the negative binomial transform Parameters: z : array-like Usually the linear predictor for a GLM or GEE model Returns: The value of the inverse of the derivative of the negative binomial : link :

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.

Generalized Method of Moments gmm statsmodels.gmm contains model classes and functions that are based on estimation with Generalized Method of Moments. Currently the general non-linear case is implemented. An example class for the standard linear instrumental variable model is included. This has been introduced as a test case, it works correctly but it does not take the linear structure into account. For the linear case we intend to introduce a specific implementation which will be faster and n

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.miscmodels.count.PoissonOffsetGMLE.information PoissonOffsetGMLE.information(params) Fisher information matrix of model Returns -Hessian of loglike evaluated at params.