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.

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)

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

covariance_group