This class summarizes the fit of a marginal regression model using GEE.
Returns: | **Attributes** : cov_params_default : ndarray
cov_robust : ndarray
cov_naive : ndarray
cov_robust_bc : ndarray
converged : bool
cov_type : string
fit_history : dict
fittedvalues : array
model : class instance
normalized_cov_params : array
params : array
scale : float
score_norm : float
bse : array
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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. |
get_margeff([at, method, atexog, dummy, count]) | Get marginal effects of the fitted 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_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() | |
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 |
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. |
wald_test_terms([skip_single, ...]) | Compute a sequence of Wald tests for terms over multiple columns |
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. |
get_margeff([at, method, atexog, dummy, count]) | Get marginal effects of the fitted 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_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() | |
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 |
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. |
wald_test_terms([skip_single, ...]) | Compute a sequence of Wald tests for terms over multiple columns |
Attributes
use_t |