statsmodels.genmod.generalized_linear_model.GLMResults¶
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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
Attributes
normalized_cov_params
()See specific model class docstring df_model (float) See GLM.df_model df_resid (float) See GLM.df_resid fit_history (dict) Contains information about the iterations. Its keys are iterations, deviance and params. model (class instance) Pointer to GLM model instance that called fit. nobs (float) The number of observations n. params (ndarray) The coefficients of the fitted model. Note that interpretation of the coefficients often depends on the distribution family and the data. pvalues (ndarray) The two-tailed p-values for the parameters. scale (float) The estimate of the scale / dispersion for the model fit. See GLM.fit and GLM.estimate_scale for more information. stand_errors (ndarray) The standard errors of the fitted GLM. #TODO still named bse Methods
conf_int
([alpha, cols])Construct confidence interval 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_hat_matrix_diag
([observed])Compute the diagonal of the hat matrix get_influence
([observed])Get an instance of GLMInfluence with influence and outlier measures get_prediction
([exog, exposure, offset, …])compute prediction results initialize
(model, params, **kwargs)Initialize (possibly re-initialize) a Results instance. load
(fname)Load a pickled results instance normalized_cov_params
()See specific model class docstring plot_added_variable
(focus_exog[, …])Create an added variable plot for a fitted regression model. plot_ceres_residuals
(focus_exog[, frac, …])Conditional Expectation Partial Residuals (CERES) plot. 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. remove_data
()Remove data arrays, all nobs arrays from result and model. 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. 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
conf_int
([alpha, cols])Construct confidence interval 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_hat_matrix_diag
([observed])Compute the diagonal of the hat matrix get_influence
([observed])Get an instance of GLMInfluence with influence and outlier measures get_prediction
([exog, exposure, offset, …])compute prediction results initialize
(model, params, **kwargs)Initialize (possibly re-initialize) a Results instance. load
(fname)Load a pickled results instance normalized_cov_params
()See specific model class docstring plot_added_variable
(focus_exog[, …])Create an added variable plot for a fitted regression model. plot_ceres_residuals
(focus_exog[, frac, …])Conditional Expectation Partial Residuals (CERES) plot. 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. remove_data
()Remove data arrays, all nobs arrays from result and model. 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. 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
aic
Akaike Information Criterion -2 * llf + 2*(df_model + 1) bic
Bayes Information Criterion deviance - df_resid * log(nobs) bse
The standard errors of the parameter estimates. deviance
See statsmodels.families.family for the distribution-specific deviance functions. fittedvalues
Predicted values for the fitted model. llf
Value of the loglikelihood function evalued at params. llnull
Log-likelihood of the model fit with a constant as the only regressor mu
See GLM docstring. null
Fitted values of the null model null_deviance
The value of the deviance function for the model fit with a constant as the only regressor. pearson_chi2
Pearson’s Chi-Squared statistic is defined as the sum of the squares of the Pearson residuals. pvalues
The two-tailed p values for the t-stats of the params. resid_anscombe
Anscombe residuals. resid_anscombe_scaled
Scaled Anscombe residuals. resid_anscombe_unscaled
Unscaled Anscombe residuals. resid_deviance
Deviance residuals. resid_pearson
Pearson residuals. resid_response
Respnose residuals. resid_working
Working residuals. tvalues
Return the t-statistic for a given parameter estimate. use_t
Flag indicating to use the Student’s distribution in inference.