statsmodels.discrete.discrete_model.MultinomialResults

class statsmodels.discrete.discrete_model.MultinomialResults(model, mlefit)

A results class for multinomial 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 loglikelihood

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
normalized_cov_params()	See specific model class docstring
pred_table()	Returns the J x J 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.
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([alpha, float_format])	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.

margeff

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
margeff()
normalized_cov_params()	See specific model class docstring
pred_table()	Returns the J x J 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.
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([alpha, float_format])	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

aic
bic
bse
fittedvalues	Linear predictor XB.
llf	Log-likelihood of model
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.
prsquared	McFadden’s pseudo-R-squared.
pvalues	The two-tailed p values for the t-stats of the params.
resid_misclassified	Residuals indicating which observations are misclassified.
resid_response	Respnose residuals.
tvalues	Return the t-statistic for a given parameter estimate.
use_t	Flag indicating to use the Student’s distribution in inference.