statsmodels.regression.process_regression.ProcessMLEResults¶
-
class
statsmodels.regression.process_regression.
ProcessMLEResults
(model, mlefit)[source]¶ Results class for Gaussian process regression models.
Methods
bootstrap
([nrep, method, disp, store])simple bootstrap to get mean and variance of estimator
conf_int
([alpha, cols])Construct confidence interval for the fitted parameters.
cov_params
([r_matrix, column, scale, cov_p, …])Compute the variance/covariance matrix.
covariance
(time, scale, smooth)Returns a fitted covariance matrix.
covariance_group
(group)f_test
(r_matrix[, cov_p, scale, invcov])Compute the F-test for a joint linear hypothesis.
get_nlfun
(fun)This is not Implemented
initialize
(model, params, **kwargs)Initialize (possibly re-initialize) a Results instance.
load
(fname)Load a pickled results instance
See specific model class docstring
predict
([exog, transform])Call self.model.predict with self.params as the first argument.
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
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.
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
Akaike information criterion
Bayesian information criterion
The standard errors of the parameter estimates.
standard deviation of parameter estimates based on covjac
standard deviation of parameter estimates based on covHJH
covariance of parameters based on outer product of jacobian of log-likelihood
covariance of parameters based on HJJH
Model WC
cached Hessian of log-likelihood
Log-likelihood of model
The two-tailed p values for the t-stats of the params.
cached Jacobian of log-likelihood
Return the t-statistic for a given parameter estimate.
Flag indicating to use the Student’s distribution in inference.