statsmodels.genmod.generalized_estimating_equations.GEEResults

class statsmodels.genmod.generalized_estimating_equations.GEEResults(model, params, cov_params, scale, cov_type='robust', use_t=False, **kwds)

This class summarizes the fit of a marginal regression model using GEE.

Returns:

Attributes

cov_params_default : ndarray

default covariance of the parameter estimates. Is chosen among one of the following three based on cov_type

cov_robust : ndarray

covariance of the parameter estimates that is robust

cov_naive : ndarray

covariance of the parameter estimates that is not robust to correlation or variance misspecification

cov_robust_bc : ndarray

covariance of the parameter estimates that is robust and bias reduced

converged : bool

indicator for convergence of the optimization. True if the norm of the score is smaller than a threshold

cov_type : string

string indicating whether a “robust”, “naive” or “bias_reduced” covariance is used as default

fit_history : dict

Contains information about the iterations.

fittedvalues : array

Linear predicted values for the fitted model. dot(exog, params)

model : class instance

Pointer to GEE model instance that called fit.

normalized_cov_params : array

See GEE docstring

params : array

The coefficients of the fitted model. Note that interpretation of the coefficients often depends on the distribution family and the data.

scale : float

The estimate of the scale / dispersion for the model fit. See GEE.fit for more information.

score_norm : float

norm of the score at the end of the iterative estimation.

bse : array

The standard errors of the fitted GEE parameters.

Methods

bse()
centered_resid()	Returns the residuals centered within each group.
conf_int([alpha, cols, cov_type])	Returns confidence intervals for the fitted parameters.
fittedvalues()	Returns the fitted values from the model.
params_sensitivity(dep_params_first, ...)	Refits the GEE model using a sequence of values for the dependence parameters.
plot_isotropic_dependence([ax, xpoints, min_n])	Create a plot of the pairwise products of within-group residuals against the corresponding time differences.
resid()	Returns the residuals, the endogeneous data minus the fitted values from the model.
resid_centered()	Returns the residuals centered within each group.
resid_centered_split()	Returns the residuals centered within each group.
resid_split()	Returns the residuals, the endogeneous data minus the fitted values from the model.
sensitivity_params(dep_params_first, ...)	Refits the GEE model using a sequence of values for the dependence parameters.
split_centered_resid()	Returns the residuals centered within each group.
split_resid()	Returns the residuals, the endogeneous data minus the fitted values from the model.
standard_errors([cov_type])	This is a convenience function that returns the standard errors for any covariance type.
summary([yname, xname, title, alpha])	Summarize the GEE regression results

Attributes

use_t