statsmodels.regression.linear_model.RegressionResults.condition_number static RegressionResults.condition_number() [source] Return condition number of exogenous matrix. Calculated as ratio of largest to smallest eigenvalue.

statsmodels.regression.mixed_linear_model.MixedLM.hessian_full MixedLM.hessian_full(params) [source] Calculates the Hessian matrix for the mixed effects model with respect to the parameterization in which the covariance matrix is represented directly (without square-root transformation). Parameters: params : MixedLMParams or array-like The model parameters at which the Hessian is calculated. If array-like, must contain the packed parameters in a form that is compatible with this model inst

statsmodels.sandbox.distributions.extras.SkewNorm2_gen.entropy SkewNorm2_gen.entropy(*args, **kwds) Differential entropy of the RV. Parameters: arg1, arg2, arg3,... : array_like The shape parameter(s) for the distribution (see docstring of the instance object for more information). loc : array_like, optional Location parameter (default=0). scale : array_like, optional Scale parameter (default=1).

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.ProbitResults.resid_generalized static ProbitResults.resid_generalized() [source] Generalized residuals Notes The generalized residuals for the Probit model are defined

statsmodels.sandbox.tsa.fftarma.ArmaFft.padarr ArmaFft.padarr(arr, maxlag, atend=True) [source] pad 1d array with zeros at end to have length maxlag function that is a method, no self used Parameters: arr : array_like, 1d array that will be padded with zeros maxlag : int length of array after padding atend : boolean If True (default), then the zeros are added to the end, otherwise to the front of the array Returns: arrp : ndarray zero-padded array Notes This is mainly written to

statsmodels.discrete.discrete_model.DiscreteResults.llnull static DiscreteResults.llnull() [source]

statsmodels.sandbox.distributions.transformed.Transf_gen.fit Transf_gen.fit(data, *args, **kwds) Return MLEs for shape, location, and scale parameters from data. MLE stands for Maximum Likelihood Estimate. Starting estimates for the fit are given by input arguments; for any arguments not provided with starting estimates, self._fitstart(data) is called to generate such. One can hold some parameters fixed to specific values by passing in keyword arguments f0, f1, ..., fn (for shape parameters)

statsmodels.tsa.vector_ar.var_model.VARResults.plot_forecast VARResults.plot_forecast(steps, alpha=0.05, plot_stderr=True) [source] Plot forecast

statsmodels.discrete.discrete_model.CountResults.resid static CountResults.resid() [source] Residuals Notes The residuals for Count models are defined as where . Any exposure and offset variables are also handled.