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
See specific model class docstring
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
(ndarray) The coefficients of the fitted model. Note that interpretation of the coefficients often depends on the distribution family and the data.
pvalues
(ndarray) The two-tailed p-values for the parameters.
scale
(float) The estimate of the scale / dispersion for the model fit. See GLM.fit and GLM.estimate_scale for more information.
stand_errors
(ndarray) 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
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 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
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 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
Akaike Information Criterion -2 * llf + 2*(df_model + 1)
Bayes Information Criterion deviance - df_resid * log(nobs)
The standard errors of the parameter estimates.
See statsmodels.families.family for the distribution-specific deviance functions.
Predicted values for the fitted model.
Value of the loglikelihood function evalued at params.
Log-likelihood of the model fit with a constant as the only regressor
See GLM docstring.
Fitted values of the null model
The value of the deviance function for the model fit with a constant as the only regressor.
Pearson’s Chi-Squared statistic is defined as the sum of the squares of the Pearson residuals.
The two-tailed p values for the t-stats of the params.
Anscombe residuals.
Scaled Anscombe residuals.
Unscaled Anscombe residuals.
Deviance residuals.
Pearson residuals.
Respnose residuals.
Working residuals.
Return the t-statistic for a given parameter estimate.
Flag indicating to use the Student’s distribution in inference.