statsmodels.gam.generalized_additive_model.GLMGamResults¶
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class
statsmodels.gam.generalized_additive_model.
GLMGamResults
(model, params, normalized_cov_params, scale, **kwds)[source]¶ Results class for generalized additive models, GAM.
This inherits from GLMResults.
Warning: some inherited methods might not correctly take account of the penalization
GLMGamResults inherits from GLMResults All methods related to the loglikelihood function return the penalized values.
Notes
status: experimental
Attributes
edf list of effective degrees of freedom for each column of the design matrix. hat_matrix_diag diagonal of hat matrix gcv generalized cross-validation criterion computed as gcv = scale / (1. - hat_matrix_trace / self.nobs)**2
cv cross-validation criterion computed as cv = ((resid_pearson / (1 - hat_matrix_diag))**2).sum() / nobs
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. cv
()deviance
()See statsmodels.families.family for the distribution-specific deviance functions. edf
()f_test
(r_matrix[, cov_p, scale, invcov])Compute the F-test for a joint linear hypothesis. fittedvalues
()Linear predicted values for the fitted model. gcv
()get_hat_matrix_diag
([observed, _axis])Compute the diagonal of the hat matrix get_influence
([observed])Get an instance of GLMInfluence with influence and outlier measures get_prediction
([exog, exog_smooth, transform])compute prediction results hat_matrix_diag
()hat_matrix_trace
()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. partial_values
(smooth_index[, include_constant])contribution of a smooth term to the linear prediction 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
(smooth_index[, plot_se, cpr, …])plot the contribution of a smooth term to the linear prediction plot_partial_residuals
(focus_exog[, ax])Create a partial residual, or ‘component plus residual’ plot for a fited regression model. predict
([exog, exog_smooth, transform])” 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 test_significance
(smooth_index)hypothesis test that a smooth component is zero. 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