statsmodels.discrete.discrete_model.BinaryResults

class statsmodels.discrete.discrete_model.BinaryResults(model, mlefit, cov_type='nonrobust', cov_kwds=None, use_t=None)

A results class for binary data

Parameters:

modelA DiscreteModel instance

paramsarray_like

The parameters of a fitted model.

hessianarray_like

The hessian of the fitted model.

scalefloat

A scale parameter for the covariance matrix.

Attributes:

df_residfloat

See model definition.

df_modelfloat

See model definition.

llffloat

Value of the log-likelihood.

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
get_influence()	Get an instance of MLEInfluence with influence and outlier measures
get_margeff([at, method, atexog, dummy, count])	Get marginal effects of the fitted model
get_prediction([exog, transform, which, ...])	Compute prediction results when endpoint transformation is valid.
info_criteria(crit[, dk_params])	Return an information criterion for the model
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
pred_table([threshold])	Prediction table
predict([exog, transform])	Call self.model.predict with self.params as the first argument
remove_data()	Remove data arrays, all nobs arrays from result and model
save(fname[, remove_data])	Save a pickle of this instance
score_test([exog_extra, params_constrained, ...])	score test for restrictions or for omitted variables
set_null_options([llnull, attach_results])	Set the fit options for the Null (constant-only) model.
summary([yname, xname, title, alpha, yname_list])	Summarize the Regression Results.
summary2([yname, xname, title, alpha, ...])	Experimental function to summarize regression results.
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

get_distribution

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
get_distribution([exog, transform])
get_influence()	Get an instance of MLEInfluence with influence and outlier measures
get_margeff([at, method, atexog, dummy, count])	Get marginal effects of the fitted model
get_prediction([exog, transform, which, ...])	Compute prediction results when endpoint transformation is valid.
info_criteria(crit[, dk_params])	Return an information criterion for the model
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
pred_table([threshold])	Prediction table
predict([exog, transform])	Call self.model.predict with self.params as the first argument
remove_data()	Remove data arrays, all nobs arrays from result and model
save(fname[, remove_data])	Save a pickle of this instance
score_test([exog_extra, params_constrained, ...])	score test for restrictions or for omitted variables
set_null_options([llnull, attach_results])	Set the fit options for the Null (constant-only) model.
summary([yname, xname, title, alpha, yname_list])	Summarize the Regression Results.
summary2([yname, xname, title, alpha, ...])	Experimental function to summarize regression results.
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

aic	Akaike information criterion.
bic	Bayesian information criterion.
bse	The standard errors of the parameter estimates
fittedvalues	Linear predictor XB.
im_ratio
llf	Log-likelihood of model
llnull	Value of the constant-only log-likelihood.
llr	Likelihood ratio chi-squared statistic; -2*(llnull - llf)
llr_pvalue	The chi-squared probability of getting a log-likelihood ratio statistic greater than llr.
prsquared	McFadden's pseudo-R-squared.
pvalues	The two-tailed p values for the t-stats of the params
resid_dev	Deviance residuals
resid_pearson	Pearson residuals
resid_response	The response residuals
tvalues	Return the t-statistic for a given parameter estimate
use_t	Flag indicating to use the Student's distribution in inference