statsmodels.regression.process_regression.ProcessMLEResults

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

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