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
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 loglikelihood

Methods

aic()

Akaike information criterion.

bic()

Bayesian information criterion.

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.

fittedvalues()

Linear predictor XB.

get_margeff([at, method, atexog, dummy, count])

Get marginal effects of the fitted model.

initialize(model, params, **kwd)

Initialize (possibly re-initialize) a Results instance.

llnull()

Value of the constant-only loglikelihood

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.

load(fname)

load a pickle, (class method)

normalized_cov_params()

See specific model class docstring

predict([exog, transform])

Call self.model.predict with self.params as the first argument.

prsquared()

McFadden’s pseudo-R-squared.

remove_data()

remove data arrays, all nobs arrays from result and model

resid_response()

Respnose residuals.

save(fname[, remove_data])

save a pickle of this instance

set_null_options([llnull, attach_results])

set fit options for 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