genmod.generalized_estimating_equations.GEEResults()

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) [source]

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.
cov_params([r_matrix, column, scale, cov_p, ...])	Returns the variance/covariance matrix.
f_test(r_matrix[, cov_p, scale, invcov])	Compute the F-test for a joint linear hypothesis.
fittedvalues()	Returns the fitted values from the model.
initialize(model, params, **kwd)
llf()
load(fname)	load a pickle, (class method)
normalized_cov_params()
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.
predict([exog, transform])	Call self.model.predict with self.params as the first argument.
pvalues()
remove_data()	remove data arrays, all nobs arrays from result and model
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.
save(fname[, remove_data])	save a pickle of this instance
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
t_test(r_matrix[, cov_p, scale, use_t])	Compute a t-test for a each linear hypothesis of the form Rb = q
tvalues()	Return the t-statistic for a given parameter estimate.
wald_test(r_matrix[, cov_p, scale, invcov, ...])	Compute a Wald-test for a joint linear hypothesis.

Attributes

use_t