statsmodels.miscmodels.ordinal_model.OrderedResults¶

class statsmodels.miscmodels.ordinal_model.OrderedResults(model, mlefit)[source]¶

Results class for OrderedModel

This class inherits from GenericLikelihoodModelResults and not all inherited methods might be appropriate in this case.

Methods

bootstrap([nrep, method, disp, store])	simple bootstrap to get mean and variance of estimator
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_nlfun(fun)	This is not Implemented
get_prediction([exog, which, transform, ...])	Compute prediction results when endpoint transformation is valid.
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()	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.
summary([yname, xname, title, alpha])	Summarize the 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	Akaike information criterion
bic	Bayesian information criterion
bse	The standard errors of the parameter estimates.
bsejac	standard deviation of parameter estimates based on covjac
bsejhj	standard deviation of parameter estimates based on covHJH
covjac	covariance of parameters based on outer product of jacobian of log-likelihood
covjhj	covariance of parameters based on HJJH
df_modelwc	Model WC
hessv	cached Hessian of log-likelihood
llf	Log-likelihood of model
llnull	Value of the loglikelihood of model without explanatory variables
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_prob	probability residual
score_obsv	cached Jacobian of log-likelihood
tvalues	Return the t-statistic for a given parameter estimate.
use_t	Flag indicating to use the Student's distribution in inference.