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.

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

Notes

status: experimental

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, _axis])	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, exog_smooth, transform])	compute prediction results
`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
`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])	"
`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.
`test_significance`(smooth_index)	hypothesis test that a smooth component is zero.
`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.
`cv`
`deviance`	See statsmodels.families.family for the distribution-specific deviance functions.
`edf`
`fittedvalues`	The estimated mean response.
`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`	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.