# 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

Parameters: statsmodels.LikelihoodModelReesults (See) – **Attributes** aic (float) – Akaike Information Criterion -2 * llf + 2*(df_model + 1) bic (float) – Bayes Information Criterion deviance - df_resid * log(nobs) deviance (float) – See statsmodels.families.family for the distribution-specific deviance functions. 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. fittedvalues (array) – Linear predicted values for the fitted model. dot(exog, params) llf (float) – Value of the loglikelihood function evalued at params. See statsmodels.families.family for distribution-specific loglikelihoods. model (class instance) – Pointer to GLM model instance that called fit. mu (array) – See GLM docstring. nobs (float) – The number of observations n. normalized_cov_params (array) – See GLM docstring null_deviance (float) – The value of the deviance function for the model fit with a constant as the only regressor. params (array) – The coefficients of the fitted model. Note that interpretation of the coefficients often depends on the distribution family and the data. pearson_chi2 (array) – Pearson’s Chi-Squared statistic is defined as the sum of the squares of the Pearson residuals. pvalues (array) – The two-tailed p-values for the parameters. resid_anscombe (array) – Anscombe residuals. See statsmodels.families.family for distribution- specific Anscombe residuals. Currently, the unscaled residuals are provided. In a future version, the scaled residuals will be provided. resid_anscombe_scaled (array) – Scaled Anscombe residuals. See statsmodels.families.family for distribution-specific Anscombe residuals. resid_anscombe_unscaled (array) – Unscaled Anscombe residuals. See statsmodels.families.family for distribution-specific Anscombe residuals. resid_deviance (array) – Deviance residuals. See statsmodels.families.family for distribution- specific deviance residuals. resid_pearson (array) – Pearson residuals. The Pearson residuals are defined as (endog - mu)/sqrt(VAR(mu)) where VAR is the distribution specific variance function. See statsmodels.families.family and statsmodels.families.varfuncs for more information. resid_response (array) – Respnose residuals. The response residuals are defined as endog - fittedvalues resid_working (array) – Working residuals. The working residuals are defined as resid_response/link’(mu). See statsmodels.family.links for the derivatives of the link functions. They are defined analytically. scale (float) – The estimate of the scale / dispersion for the model fit. See GLM.fit and GLM.estimate_scale for more information. stand_errors (array) – The standard errors of the fitted GLM. #TODO still named bse

Methods

 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

Attributes

 use_t