Class to contain GLM results.
GLMResults inherits from statsmodels.LikelihoodModelResults
| Parameters: | See statsmodels.LikelihoodModelReesults : |
|---|
See also
statsmodels.LikelihoodModelResults
Attributes
| llf | |
| normalized_cov_params() |
| 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.family.family for the distribution-specific deviance functions. |
| df_model | float | See GLM.df_model |
| df_resid | float | See GLM.df_resid |
| fittedvalues | array | Linear predicted values for the fitted model. dot(exog, params) |
| model | class instance | Pointer to GLM model instance that called fit. |
| mu | array | See GLM docstring. |
| nobs | float | The number of observations n. |
| 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. |
| pearsonX2 | array | Pearson’s Chi-Squared statistic is defined as the sum of the squares of the Pearson residuals. |
| pinv_wexog | array | See GLM docstring. |
| resid_anscombe | array | Anscombe residuals. See statsmodels.family.family for distribution- specific Anscombe residuals. |
| resid_dev | array | Deviance residuals. See statsmodels.family.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.family.family and statsmodels.family.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 | array | 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
| conf_int([alpha, cols]) | Returns the confidence interval of the fitted parameters. |
| cov_params([r_matrix, column, scale, other]) | Returns the variance/covariance matrix. |
| f_test(r_matrix[, scale, invcov]) | Compute an Fcontrast/F-test for a contrast matrix. |
| t([column]) | Return the t-statistic for a given parameter estimate. |
| t_test(r_matrix[, scale]) | Compute a tcontrast/t-test for a row vector array. |