statsmodels.genmod.generalized_estimating_equations.GEEResults

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

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

Attributes

cov_params_defaultndarray

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

cov_robustndarray

covariance of the parameter estimates that is robust

cov_naivendarray

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

cov_robust_bcndarray

covariance of the parameter estimates that is robust and bias reduced

convergedbool

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

cov_typestring

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

fit_historydict

Contains information about the iterations.

fittedvaluesarray

Returns the fitted values from the model.

modelclass instance

Pointer to GEE model instance that called fit.

normalized_cov_paramsarray

See specific model class docstring

paramsarray

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

scalefloat

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

score_normfloat

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

bsearray

The standard errors of the parameter estimates.

Methods

bse()	The standard errors of the parameter estimates.
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.
get_margeff([at, method, atexog, dummy, count])	Get marginal effects of the fitted model.
initialize(model, params, **kwd)	Initialize (possibly re-initialize) a Results instance.
llf()	Log-likelihood of model
load(fname)	load a pickle, (class method)
normalized_cov_params()	See specific model class docstring
params_sensitivity(dep_params_first, …)	Refits the GEE model using a sequence of values for the dependence parameters.
plot_added_variable(focus_exog[, …])	Create an added variable plot for a fitted regression model.
plot_ceres_residuals(focus_exog[, frac, …])	Produces a CERES (Conditional Expectation Partial Residuals) plot for a fitted regression model.
plot_isotropic_dependence([ax, xpoints, min_n])	Create a plot of the pairwise products of within-group residuals against the corresponding time differences.
plot_partial_residuals(focus_exog[, ax])	Create a partial residual, or ‘component plus residual’ plot for a fited regression model.
predict([exog, transform])	Call self.model.predict with self.params as the first argument.
pvalues()	The two-tailed p values for the t-stats of the params.
qic([scale])	Returns the QIC and QICu information criteria.
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
score_test()	Return the results of a score test for a linear constraint.
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
t_test_pairwise(term_name[, method, alpha, …])	perform pairwise t_test with multiple testing corrected p-values
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.
wald_test_terms([skip_single, …])	Compute a sequence of Wald tests for terms over multiple columns

resid_anscombe
resid_deviance
resid_pearson
resid_response
resid_working