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# scikits.statsmodels.glm.GLMResults

class `scikits.statsmodels.glm.``GLMResults`(model, params, normalized_cov_params, scale)

  Class to contain GLM results.

  GLMResults inherits from statsmodels.LikelihoodModelResults

  Parameters: | See statsmodels.LikelihoodModelReesults :
  Returns: | **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.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) llf : float Value of the loglikelihood function evalued at params. See statsmodels.family.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. pinv_wexog : array See GLM docstring. resid_anscombe : array Anscombe residuals. See statsmodels.family.family for distribution- specific Anscombe residuals. resid_deviance : 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

  See also

  `statsmodels.LikelihoodModelResults`

  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.

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