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) –
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.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_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, **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