statsmodels.regression.process_regression.ProcessMLEResults

class statsmodels.regression.process_regression.ProcessMLEResults(model, mlefit)

Results class for Gaussian process regression models.

Attributes

use_t

Flag indicating to use the Student’s distribution in inference.

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
normalized_cov_params()	See specific model class docstring
predict([exog, transform])	Call self.model.predict with self.params as the first argument.
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
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.

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
normalized_cov_params()	See specific model class docstring
predict([exog, transform])	Call self.model.predict with self.params as the first argument.
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
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

aic	Akaike information criterion
bic	Bayesian information criterion
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
covjac	covariance of parameters based on outer product of jacobian of log-likelihood
covjhj	covariance of parameters based on HJJH
df_modelwc	Model WC
hessv	cached Hessian of log-likelihood
llf	Log-likelihood of model
pvalues	The two-tailed p values for the t-stats of the params.
score_obsv	cached Jacobian of log-likelihood
tvalues	Return the t-statistic for a given parameter estimate.
use_t	Flag indicating to use the Student’s distribution in inference.