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

 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, _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 initialize(model, params, **kwargs) Initialize (possibly re-initialize) a Results instance. load(fname) Load a pickled results instance See specific model class docstring partial_values(smooth_index[, include_constant]) contribution of a smooth term to the linear prediction 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(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 fitted regression model. predict([exog, exog_smooth, transform]) ” 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. test_significance(smooth_index) hypothesis test that a smooth component is zero. 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 deviance - df_resid * log(nobs) bse The standard errors of the parameter estimates. cv deviance See statsmodels.families.family for the distribution-specific deviance functions. edf fittedvalues Linear predicted values for the fitted model. gcv hat_matrix_diag hat_matrix_trace 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 Respnose 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.