statsmodels.base.distributed_estimation.DistributedResults

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

Class to contain model results

Parameters

modelclass 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.

paramsndarray

Parameter estimates from the fit model.

Attributes

bse

The standard errors of the parameter estimates.

llf

Log-likelihood of model

pvalues

The two-tailed p values for the t-stats of the params.

tvalues

Return the t-statistic for a given parameter estimate.

use_t

Flag indicating to use the Student’s distribution in inference.

Methods

conf_int([alpha, cols])	Construct confidence interval for the fitted parameters.
cov_params([r_matrix, column, scale, cov_p, …])	Compute the variance/covariance matrix.
f_test(r_matrix[, cov_p, scale, invcov])	Compute the F-test for a joint linear hypothesis.
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, *args, **kwargs)	Calls self.model.predict for the provided exog.
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.
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.

Methods

conf_int([alpha, cols])	Construct confidence interval for the fitted parameters.
cov_params([r_matrix, column, scale, cov_p, …])	Compute the variance/covariance matrix.
f_test(r_matrix[, cov_p, scale, invcov])	Compute the F-test for a joint linear hypothesis.
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, *args, **kwargs)	Calls self.model.predict for the provided exog.
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.
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.

Properties

bse	The standard errors of the parameter estimates.
llf	Log-likelihood of model
pvalues	The two-tailed p values for the t-stats of the params.
tvalues	Return the t-statistic for a given parameter estimate.
use_t	Flag indicating to use the Student’s distribution in inference.