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
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, 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

info_criteria(crit[, scale, dk_params])

Return an information criterion for the model.

initialize(model, params, **kwargs)

Initialize (possibly re-initialize) a Results instance.

llf_scaled([scale])

Return the log-likelihood at the given scale, using the estimated scale if the provided scale is None.

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.

pseudo_rsquared([kind])

Pseudo R-squared

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, 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, invcov, use_f, ...])

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.