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, 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)	Call 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 of Results, not implemented
t_test(r_matrix[, cov_p, use_t])	Compute a t-test for 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, invcov, use_f, ...])	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, 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)	Call 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 of Results, not implemented
t_test(r_matrix[, cov_p, use_t])	Compute a t-test for 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, invcov, use_f, ...])	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