statsmodels.genmod.generalized_estimating_equations.GEEResults.load classmethod GEEResults.load(fname) load a pickle, (class method) Parameters: fname : string or filehandle fname can be a string to a file path or filename, or a filehandle. Returns: unpickled instance :

statsmodels.genmod.generalized_estimating_equations.GEEResults.conf_int GEEResults.conf_int(alpha=0.05, cols=None, cov_type=None) [source] Returns confidence intervals for the fitted parameters. Parameters: alpha : float, optional The alpha level for the confidence interval. i.e., The default alpha = .05 returns a 95% confidence interval. cols : array-like, optional cols specifies which confidence intervals to return cov_type : string The covariance type used for computing standard err

statsmodels.discrete.discrete_model.ProbitResults.normalized_cov_params ProbitResults.normalized_cov_params()

statsmodels.genmod.generalized_estimating_equations.GEEResults.resid_split static GEEResults.resid_split() [source] Returns the residuals, the endogeneous data minus the fitted values from the model. The residuals are returned as a list of arrays containing the residuals for each cluster.

statsmodels.sandbox.distributions.extras.SkewNorm_gen.entropy SkewNorm_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.miscmodels.count.PoissonZiGMLE.jac PoissonZiGMLE.jac(*args, **kwds) jac is deprecated, use score_obs instead! Use score_obs method. jac will be removed in 0.7. Jacobian/Gradient of log-likelihood evaluated at params for each observation.

statsmodels.genmod.generalized_estimating_equations.GEE.update_cached_means GEE.update_cached_means(mean_params) [source] cached_means should always contain the most recent calculation of the group-wise mean vectors. This function should be called every time the regression parameters are changed, to keep the cached means up to date.

statsmodels.tsa.arima_process.lpol2index statsmodels.tsa.arima_process.lpol2index(ar) [source] remove zeros from lagpolynomial, squeezed representation with index Parameters: ar : array_like coefficients of lag polynomial Returns: coeffs : array non-zero coefficients of lag polynomial index : array index (lags) of lagpolynomial with non-zero elements

statsmodels.sandbox.distributions.extras.SkewNorm_gen.nnlf SkewNorm_gen.nnlf(theta, x) Return negative loglikelihood function Notes This is -sum(log pdf(x, theta), axis=0) where theta are the parameters (including loc and scale).

statsmodels.discrete.discrete_model.MultinomialModel.fit MultinomialModel.fit(start_params=None, method='newton', maxiter=35, full_output=1, disp=1, callback=None, **kwargs) [source] Fit the model using maximum likelihood. The rest of the docstring is from statsmodels.base.model.LikelihoodModel.fit Fit method for likelihood based models Parameters: start_params : array-like, optional Initial guess of the solution for the loglikelihood maximization. The default is an array of zeros. method