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

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.
deviance()	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.
fittedvalues()	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.
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)
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

cv
edf
gcv
hat_matrix_diag
hat_matrix_trace