statsmodels.regression.process_regression.ProcessMLEResults

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

Results class for Gaussian process regression models

Attributes:

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

dot product of Hessian, Jacobian, Jacobian, Hessian of likelihood

name should be covhjh

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

Methods

bootstrap([nrep, method, disp, store, rng])	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
f_test(r_matrix[, cov_p, invcov])	Compute the F-test for a joint linear hypothesis
get_nlfun(fun)	Get delta-method function for nonlinear tests, not implemented
get_prediction([exog, which, transform, ...])	Compute prediction results when endpoint transformation is valid
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, use_t])	Compute a t-test for 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, invcov, use_f, ...])	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

covariance_group

Methods

bootstrap([nrep, method, disp, store, rng])	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, invcov])	Compute the F-test for a joint linear hypothesis
get_nlfun(fun)	Get delta-method function for nonlinear tests, not implemented
get_prediction([exog, which, transform, ...])	Compute prediction results when endpoint transformation is valid
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, use_t])	Compute a t-test for 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, invcov, use_f, ...])	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