statsmodels.discrete.discrete_model.MultinomialResults

class statsmodels.discrete.discrete_model.MultinomialResults(model, mlefit)[source]

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, invcov])

Compute the F-test for a joint linear hypothesis.

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

Get marginal effects of the fitted model.

get_prediction()

Not implemented for Multinomial

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

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.

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([alpha, float_format])

Experimental function to summarize regression results

t_test(r_matrix[, cov_p, 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, 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

bic

bse

fittedvalues

Linear predictor XB.

im_ratio

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.