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.

cov_params_default¶

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

Type:¶: ndarray

cov_robust¶

covariance of the parameter estimates that is robust

Type:¶: ndarray

cov_naive¶

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

Type:¶: ndarray

cov_robust_bc¶

covariance of the parameter estimates that is robust and bias reduced

Type:¶: ndarray

converged¶

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

Type:¶: bool

cov_type¶

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

Type:¶: str

fit_history¶

Contains information about the iterations.

Type:¶: dict

fittedvalues¶

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

Type:¶: ndarray

model¶

Pointer to GEE model instance that called fit.

Type:¶: class instance

normalized_cov_params¶

See GEE docstring

Type:¶: ndarray

params¶

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

Type:¶: ndarray

scale¶

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

Type:¶: float

score_norm¶

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

Type:¶: float

bse¶

The standard errors of the fitted GEE parameters.

Type:¶: ndarray

Methods

`conf_int`([alpha, cols, cov_type])	Returns confidence intervals for the fitted parameters.
`cov_params`([r_matrix, column, scale, cov_p, ...])	Compute the variance/covariance matrix.
`f_test`(r_matrix[, cov_p, invcov])	Compute the F-test for a joint linear hypothesis.
`get_distribution`([exog, exposure, offset, ...])	Return a instance of the predictive distribution.
`get_hat_matrix_diag`([observed])	Compute the diagonal of the hat matrix
`get_influence`([observed])	Get an instance of GLMInfluence with influence and outlier measures
`get_margeff`([at, method, atexog, dummy, count])	Get marginal effects of the fitted model.
`get_prediction`([exog, exposure, offset, ...])	Compute prediction results for GLM compatible models.
`info_criteria`(crit[, scale, dk_params])	Return an information criterion for the model.
`initialize`(model, params, **kwargs)	Initialize (possibly re-initialize) a Results instance.
`llf_scaled`([scale])	Return the log-likelihood at the given scale, using the estimated scale if the provided scale is None.
`load`(fname)	Load a pickled results instance
`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, ...])	Conditional Expectation Partial Residuals (CERES) plot.
`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 fitted regression model.
`predict`([exog, transform])	Call self.model.predict with self.params as the first argument.
`pseudo_rsquared`([kind])	Pseudo R-squared
`qic`([scale, n_step])	Returns the QIC and QICu information criteria.
`remove_data`()	Remove data arrays, all nobs arrays from result and 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.
`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
`summary2`([yname, xname, title, alpha, ...])	Experimental summary for regression Results
`t_test`(r_matrix[, cov_p, 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.
`wald_test`(r_matrix[, cov_p, invcov, use_f, ...])	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.

Properties

`aic`	Akaike Information Criterion -2 * llf + 2 * (df_model + 1)
`bic`	Bayes Information Criterion
`bic_deviance`	Bayes Information Criterion
`bic_llf`	Bayes Information Criterion
`bse`
`centered_resid`	Returns the residuals centered within each group.
`deviance`	See statsmodels.families.family for the distribution-specific deviance functions.
`fittedvalues`	The estimated mean response.
`llf`	Value of the loglikelihood function evalued at params.
`llnull`	Log-likelihood of the model fit with a constant as the only regressor
`mu`	See GLM docstring.
`null`	Fitted values of the null model
`null_deviance`	The value of the deviance function for the model fit with a constant as the only regressor.
`pearson_chi2`	Pearson's Chi-Squared statistic is defined as the sum of the squares of the Pearson residuals.
`pvalues`	The two-tailed p values for the t-stats of the params.
`resid`	The response residuals.
`resid_anscombe`	Anscombe residuals.
`resid_anscombe_scaled`	Scaled Anscombe residuals.
`resid_anscombe_unscaled`	Unscaled Anscombe residuals.
`resid_centered`	Returns the residuals centered within each group.
`resid_centered_split`	Returns the residuals centered within each group.
`resid_deviance`	Deviance residuals.
`resid_pearson`	Pearson residuals.
`resid_response`	Response residuals.
`resid_split`	Returns the residuals, the endogeneous data minus the fitted values from the model.
`resid_working`	Working residuals.
`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.
`tvalues`	Return the t-statistic for a given parameter estimate.
`use_t`	Flag indicating to use the Student's distribution in inference.