method, ctol=1e-06, start_params=None, params_niter=1, first_dep_update=0, cov_type='robust', ddof_scale=None, scaling_factor=1.0)[source]

Fits a marginal regression model using generalized estimating equations (GEE).


The maximum number of iterations


The convergence criterion for stopping the Gauss-Seidel iterations


A vector of starting values for the regression coefficients. If None, a default is chosen.


The number of Gauss-Seidel updates of the mean structure parameters that take place prior to each update of the dependence structure.


No dependence structure updates occur before this iteration number.


One of “robust”, “naive”, or “bias_reduced”.

ddof_scalescalar or None

The scale parameter is estimated as the sum of squared Pearson residuals divided by N - ddof_scale, where N is the total sample size. If ddof_scale is None, the number of covariates (including an intercept if present) is used.


The estimated covariance of the parameter estimates is scaled by this value. Default is 1, Stata uses N / (N - g), where N is the total sample size and g is the average group size.

An instance of the GEEResults class or subclass


If convergence difficulties occur, increase the values of first_dep_update and/or params_niter. Setting first_dep_update to a greater value (e.g. ~10-20) causes the algorithm to move close to the GLM solution before attempting to identify the dependence structure.

For the Gaussian family, there is no benefit to setting params_niter to a value greater than 1, since the mean structure parameters converge in one step.