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)[source]

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

Attributes

fittedvalues() Returns the fitted values from the model.
normalized_cov_params() See specific model class docstring
bse() The standard errors of the parameter estimates.
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.
model (class instance) Pointer to GEE model instance that called fit.
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.

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); use only on trusted files, as unpickling can run arbitrary code.
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_anscombe()
resid_centered() Returns the residuals centered within each group.
resid_centered_split() Returns the residuals centered within each group.
resid_deviance()
resid_pearson()
resid_response()
resid_split() Returns the residuals, the endogeneous data minus the fitted values from the model.
resid_working()
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