3.5.3.3.4. 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.
3.5.3.3.4.1. Methods¶
__init__ (model, params, cov_params, scale[, ...]) |
|
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. |