class statsmodels.genmod.generalized_linear_model.GLMResults(model, params, normalized_cov_params, scale, cov_type='nonrobust', cov_kwds=None, use_t=None)
[source]
Class to contain GLM results.
GLMResults inherits from statsmodels.LikelihoodModelResults
Parameters: | statsmodels.LikelihoodModelReesults (See) – |
---|---|
Returns: |
|
aic () | |
bic () | |
bse () | |
conf_int ([alpha, cols, method]) | Returns the confidence interval of the fitted parameters. |
cov_params ([r_matrix, column, scale, cov_p, …]) | Returns the variance/covariance matrix. |
deviance () | |
f_test (r_matrix[, cov_p, scale, invcov]) | Compute the F-test for a joint linear hypothesis. |
fittedvalues () | |
get_prediction ([exog, exposure, offset, …]) | compute prediction results |
initialize (model, params, **kwd) | |
llf () | |
llnull () | |
load (fname) | load a pickle, (class method) |
mu () | |
normalized_cov_params () | |
null () | |
null_deviance () | |
pearson_chi2 () | |
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_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_anscombe () | |
resid_anscombe_scaled () | |
resid_anscombe_unscaled () | |
resid_deviance () | |
resid_pearson () | |
resid_response () | |
resid_working () | |
save (fname[, remove_data]) | save a pickle of this instance |
summary ([yname, xname, title, alpha]) | Summarize the Regression Results |
summary2 ([yname, xname, title, alpha, …]) | Experimental summary for 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 |
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 |
use_t |
© 2009–2012 Statsmodels Developers
© 2006–2008 Scipy Developers
© 2006 Jonathan E. Taylor
Licensed under the 3-clause BSD License.
http://www.statsmodels.org/stable/generated/statsmodels.genmod.generalized_linear_model.GLMResults.html