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

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.
cv()
deviance() See statsmodels.families.family for the distribution-specific deviance functions.
edf()
f_test(r_matrix[, cov_p, scale, invcov]) Compute the F-test for a joint linear hypothesis.
fittedvalues() Predicted values for the fitted model.
gcv()
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
hat_matrix_diag()
hat_matrix_trace()
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); use only on trusted files, as unpickling can run arbitrary code.
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