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)

Class to contain GLM results.

GLMResults inherits from statsmodels.LikelihoodModelResults

See also

statsmodels.base.model.LikelihoodModelResults

Attributes

df_modelfloat

See GLM.df_model

df_residfloat

See GLM.df_resid

fit_historydict

Contains information about the iterations. Its keys are iterations, deviance and params.

modelclass instance

Pointer to GLM model instance that called fit.

nobsfloat

The number of observations n.

normalized_cov_paramsndarray

See specific model class docstring

paramsndarray

The coefficients of the fitted model. Note that interpretation of the coefficients often depends on the distribution family and the data.

pvaluesndarray

The two-tailed p-values for the parameters.

scalefloat

The estimate of the scale / dispersion for the model fit. See GLM.fit and GLM.estimate_scale for more information.

stand_errorsndarray

The standard errors of the fitted GLM. #TODO still named bse

Methods

conf_int([alpha, cols])	Construct confidence interval for the fitted parameters.
cov_params([r_matrix, column, scale, cov_p, …])	Compute the variance/covariance matrix.
f_test(r_matrix[, cov_p, scale, invcov])	Compute the F-test for a joint linear hypothesis.
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, **kwargs)	Initialize (possibly re-initialize) a Results instance.
load(fname)	Load a pickled results instance
normalized_cov_params()	See specific model class docstring
plot_added_variable(focus_exog[, …])	Create an added variable plot for a fitted regression model.
plot_ceres_residuals(focus_exog[, frac, …])	Conditional Expectation Partial Residuals (CERES) plot.
plot_partial_residuals(focus_exog[, ax])	Create a partial residual, or ‘component plus residual’ plot for a fitted regression model.
predict([exog, transform])	Call self.model.predict with self.params as the first argument.
remove_data()	Remove data arrays, all nobs arrays from result and model.
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.
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.

Methods

conf_int([alpha, cols])	Construct confidence interval for the fitted parameters.
cov_params([r_matrix, column, scale, cov_p, …])	Compute the variance/covariance matrix.
f_test(r_matrix[, cov_p, scale, invcov])	Compute the F-test for a joint linear hypothesis.
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, **kwargs)	Initialize (possibly re-initialize) a Results instance.
load(fname)	Load a pickled results instance
normalized_cov_params()	See specific model class docstring
plot_added_variable(focus_exog[, …])	Create an added variable plot for a fitted regression model.
plot_ceres_residuals(focus_exog[, frac, …])	Conditional Expectation Partial Residuals (CERES) plot.
plot_partial_residuals(focus_exog[, ax])	Create a partial residual, or ‘component plus residual’ plot for a fitted regression model.
predict([exog, transform])	Call self.model.predict with self.params as the first argument.
remove_data()	Remove data arrays, all nobs arrays from result and model.
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.
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.

Properties

aic	Akaike Information Criterion -2 * llf + 2 * (df_model + 1)
bic	Bayes Information Criterion
bic_deviance	Bayes Information Criterion
bic_llf	Bayes Information Criterion
bse	The standard errors of the parameter estimates.
deviance	See statsmodels.families.family for the distribution-specific deviance functions.
fittedvalues	The estimated mean response.
llf	Value of the loglikelihood function evalued at params.
llnull	Log-likelihood of the model fit with a constant as the only regressor
mu	See GLM 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.
pvalues	The two-tailed p values for the t-stats of the params.
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	Response residuals.
resid_working	Working residuals.
tvalues	Return the t-statistic for a given parameter estimate.
use_t	Flag indicating to use the Student’s distribution in inference.