statsmodels.base.distributed_estimation.DistributedResults

class statsmodels.base.distributed_estimation.DistributedResults(model, params)

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