statsmodels.regression.process_regression.ProcessMLEResults

class statsmodels.regression.process_regression.ProcessMLEResults(model, mlefit)[source]

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])

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)

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, 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, 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.