statsmodels.base.distributed_estimation.DistributedResults¶
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class
statsmodels.base.distributed_estimation.
DistributedResults
(model, params)[source]¶ Class to contain model results
Parameters: model : class instance
class instance for model used for distributed data, this particular instance uses fake data and is really only to allow use of methods like predict.
params : array
parameter estimates from the fit model.
Methods
bse
()The standard errors of the parameter estimates. 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. f_test
(r_matrix[, cov_p, scale, invcov])Compute the F-test for a joint linear hypothesis. initialize
(model, params, **kwd)Initialize (possibly re-initialize) a Results instance. llf
()Log-likelihood of model load
(fname)load a pickle, (class method); use only on trusted files, as unpickling can run arbitrary code. normalized_cov_params
()See specific model class docstring predict
(exog, *args, **kwargs)Calls self.model.predict for the provided exog. 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 summary
()Summary 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