# statsmodels.gam.generalized_additive_model.GLMGamResults¶

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

 Akaike Information Criterion -2 * llf + 2*(df_model + 1) Bayes Information Criterion deviance - df_resid * log(nobs) 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. See statsmodels.families.family for the distribution-specific deviance functions. f_test(r_matrix[, cov_p, scale, invcov]) Compute the F-test for a joint linear hypothesis. Linear predicted values for the fitted model. 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, **kwd) Initialize (possibly re-initialize) a Results instance. Value of the loglikelihood function evalued at params. Log-likelihood of the model fit with a constant as the only regressor load(fname) load a pickle, (class method) See GLM docstring. See specific model class docstring Fitted values of the null model 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’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]) ” The two-tailed p values for the t-stats of the params. remove data arrays, all nobs arrays from result and model Anscombe residuals. Scaled Anscombe residuals. Unscaled Anscombe residuals. Deviance residuals. Pearson residuals. Respnose residuals. 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. 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
 cv edf gcv hat_matrix_diag hat_matrix_trace