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

normalized_cov_params() See specific model class docstring
pvalues() The two-tailed p values for the t-stats of the params.
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.
model (class instance) Pointer to GLM model instance that called fit.
nobs (float) The number of observations n.
params (array) The coefficients of the fitted model. Note that interpretation of the coefficients often depends on the distribution family and the data.
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() 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.
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() See statsmodels.families.family for the distribution-specific deviance functions.
f_test(r_matrix[, cov_p, scale, invcov]) Compute the F-test for a joint linear hypothesis.
fittedvalues() Predicted values for the fitted model.
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) Initialize (possibly re-initialize) a Results instance.
llf() Value of the loglikelihood function evalued at params.
llnull() Log-likelihood of the model fit with a constant as the only regressor
load(fname) load a pickle, (class method); use only on trusted files, as unpickling can run arbitrary code.
mu() See GLM docstring.
normalized_cov_params() See specific model class 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.
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() The two-tailed p values for the t-stats of the params.
remove_data() remove data arrays, all nobs arrays from result and model
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.
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