statsmodels.discrete.discrete_model.MultinomialResults¶
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class
statsmodels.discrete.discrete_model.
MultinomialResults
(model, mlefit)[source]¶ A results class for multinomial data
Parameters: model : A DiscreteModel instance
params : array_like
The parameters of a fitted model.
hessian : array_like
The hessian of the fitted model.
scale : float
A scale parameter for the covariance matrix.
Attributes
df_resid (float) See model definition. df_model (float) See model definition. llf (float) 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 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. 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.