statsmodels.gam.generalized_additive_model.GLMGamResults

class statsmodels.gam.generalized_additive_model.GLMGamResults(model, params, normalized_cov_params, scale, **kwds)

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
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])	”
remove_data()	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.

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
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])	”
remove_data()	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	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.