# statsmodels.genmod.generalized_linear_model.GLMResults¶

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
df_modelfloat

See GLM.df_model

df_residfloat

See GLM.df_resid

fit_historydict

Contains information about the iterations. Its keys are iterations, deviance and params.

modelclass instance

Pointer to GLM model instance that called fit.

nobsfloat

The number of observations n.

normalized_cov_paramsndarray

See specific model class docstring

paramsndarray

The coefficients of the fitted model. Note that interpretation of the coefficients often depends on the distribution family and the data.

pvaluesndarray

The two-tailed p-values for the parameters.

scalefloat

The estimate of the scale / dispersion for the model fit. See GLM.fit and GLM.estimate_scale for more information.

stand_errorsndarray

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 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 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 Linear 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.