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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 bool(x) -> bool

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