statsmodels.discrete.discrete_model.DiscreteResults¶
-
class
statsmodels.discrete.discrete_model.
DiscreteResults
(model, mlefit, cov_type='nonrobust', cov_kwds=None, use_t=None)[source]¶ A results class for the discrete dependent variable models.
- Parameters
- model
A
DiscreteModel
instance
- paramsarray_like
The parameters of a fitted model.
- hessianarray_like
The hessian of the fitted model.
- scale
float
A scale parameter for the covariance matrix.
- model
- Attributes
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.
get_margeff
([at, method, atexog, dummy, count])Get marginal effects of the fitted model.
initialize
(model, params, **kwargs)Initialize (possibly re-initialize) a Results instance.
load
(fname)Load a pickled results instance
See specific model class docstring
predict
([exog, transform])Call self.model.predict with self.params as the first argument.
Remove data arrays, all nobs arrays from result and model.
save
(fname[, remove_data])Save a pickle of this instance.
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, 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
Akaike information criterion.
Bayesian information criterion.
The standard errors of the parameter estimates.
Linear predictor XB.
Log-likelihood of model
Value of the constant-only loglikelihood
Likelihood ratio chi-squared statistic; -2*(llnull - llf)
The chi-squared probability of getting a log-likelihood ratio statistic greater than llr.
McFadden’s pseudo-R-squared.
The two-tailed p values for the t-stats of the params.
Respnose residuals.
Return the t-statistic for a given parameter estimate.
Flag indicating to use the Student’s distribution in inference.