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

df_model¶

See GLM.df_model

Type:¶

float

df_resid¶

See GLM.df_resid

Type:¶

float

fit_history¶

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

Type:¶

dict

model¶

Pointer to GLM model instance that called fit.

Type:¶

class instance

nobs¶

The number of observations n.

Type:¶

float

normalized_cov_params¶

See GLM docstring

Type:¶

ndarray

params¶

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

Type:¶

ndarray

pvalues¶

The two-tailed p-values for the parameters.

Type:¶

ndarray

scale¶

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

Type:¶

float

stand_errors¶

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

Type:¶

ndarray

See also

statsmodels.base.model.LikelihoodModelResults

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_distribution([exog, exposure, offset, ...])	Return a instance of the predictive distribution.
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_margeff([at, method, atexog, dummy, count])	Get marginal effects of the fitted model.
get_prediction([exog, exposure, offset, ...])	Compute prediction results for GLM compatible models.
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.
score_test([exog_extra, params_constrained, ...])	score test for restrictions or for omitted variables
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.