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

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

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.