statsmodels.genmod.generalized_estimating_equations.GEEMargins.conf_int GEEMargins.conf_int(alpha=0.05) [source] Returns the confidence intervals of the marginal effects Parameters: alpha : float Number between 0 and 1. The confidence intervals have the probability 1-alpha. Returns: conf_int : ndarray An array with lower, upper confidence intervals for the marginal effects.

statsmodels.genmod.generalized_estimating_equations.GEEMargins.get_margeff GEEMargins.get_margeff(at='overall', method='dydx', atexog=None, dummy=False, count=False) [source] Get marginal effects of the fitted model. Parameters: at : str, optional Options are: ?overall?, The average of the marginal effects at each observation. ?mean?, The marginal effects at the mean of each regressor. ?median?, The marginal effects at the median of each regressor. ?zero?, The marginal effects at zero for

statsmodels.genmod.generalized_estimating_equations.GEE.from_formula classmethod GEE.from_formula(formula, groups, data, subset=None, time=None, offset=None, exposure=None, *args, **kwargs) [source]

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.genmod.generalized_estimating_equations.GEE.predict GEE.predict(params, exog=None, offset=None, exposure=None, linear=False) [source] Return predicted values for a marginal regression model fit using GEE. Parameters: params : array-like Parameters / coefficients of a marginal regression model. exog : array-like, optional Design / exogenous data. If exog is None, model exog is used. offset : array-like, optional Offset for exog if provided. If offset is None, model offset is

statsmodels.genmod.generalized_estimating_equations.GEE.cluster_list GEE.cluster_list(array) [source] Returns array split into subarrays corresponding to the cluster structure.

statsmodels.genmod.generalized_estimating_equations.GEE.estimate_scale GEE.estimate_scale() [source] Returns an estimate of the scale parameter phi at the current parameter value.

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.genmod.families.family.Gaussian.starting_mu Gaussian.starting_mu(y) Starting value for mu in the IRLS algorithm. Parameters: y : array The untransformed response variable. Returns: mu_0 : array The first guess on the transformed response variable. Notes Only the Binomial family takes a different initial value.

statsmodels.genmod.families.family.Gaussian.weights Gaussian.weights(mu) Weights for IRLS steps Parameters: mu : array-like The transformed mean response variable in the exponential family Returns: w : array The weights for the IRLS steps Notes w = 1 / (link?(mu)**2 * variance(mu))