statsmodels.regression.process_regression.ProcessMLEResults¶
-
class
statsmodels.regression.process_regression.
ProcessMLEResults
(model, mlefit)[source]¶ Results class for Gaussian process regression models.
Methods
aic
()Akaike information criterion bic
()Bayesian information criterion bootstrap
([nrep, method, disp, store])simple bootstrap to get mean and variance of estimator bse
()The standard errors of the parameter estimates. bsejac
()standard deviation of parameter estimates based on covjac bsejhj
()standard deviation of parameter estimates based on covHJH 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. covariance
(time, scale, smooth)Returns a fitted covariance matrix. covariance_group
(group)covjac
()covariance of parameters based on outer product of jacobian of log-likelihood covjhj
()covariance of parameters based on HJJH df_modelwc
()Model WC f_test
(r_matrix[, cov_p, scale, invcov])Compute the F-test for a joint linear hypothesis. get_nlfun
(fun)This is not Implemented hessv
()cached Hessian of log-likelihood initialize
(model, params, **kwd)Initialize (possibly re-initialize) a Results instance. llf
()Log-likelihood of model load
(fname)load a pickle, (class method); use only on trusted files, as unpickling can run arbitrary code. normalized_cov_params
()See specific model class docstring predict
([exog, transform])Call self.model.predict with self.params as the first argument. 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 save
(fname[, remove_data])save a pickle of this instance score_obsv
()cached Jacobian of log-likelihood summary
([yname, xname, title, alpha])Summarize the 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 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