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
- 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
- convergedbool
indicator for convergence of the optimization. True if the norm of the score is smaller than a threshold
- cov_type
str
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 specific model class 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.
- cov_params_default
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, scale, invcov])Compute the F-test for a joint linear hypothesis.
get_margeff
([at, method, atexog, dummy, count])Get marginal effects of the fitted model.
initialize
(model, params, **kwargs)Initialize (possibly re-initialize) a Results instance.
load
(fname)Load a pickled results instance
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 fitted regression model.
predict
([exog, transform])Call self.model.predict with self.params as the first argument.
qic
([scale])Returns the QIC and QICu information criteria.
Remove data arrays, all nobs arrays from result and model.
save
(fname[, remove_data])Save a pickle of this instance.
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
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.
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.
Properties
Returns the residuals centered within each group.
Returns the fitted values from the model.
Log-likelihood of model
The two-tailed p values for the t-stats of the params.
Returns the residuals, the endogeneous data minus the fitted values from the model.
Returns the residuals centered within each group.
Returns the residuals centered within each group.
Returns the residuals, the endogeneous data minus the fitted values from the model.
Returns the residuals centered within each group.
Returns the residuals, the endogeneous data minus the fitted values from the model.
Return the t-statistic for a given parameter estimate.
Flag indicating to use the Student’s distribution in inference.