statsmodels.miscmodels.ordinal_model.OrderedResults

class statsmodels.miscmodels.ordinal_model.OrderedResults(model, mlefit)

Results class for OrderedModel

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

Attributes:

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

dot product of Hessian, Jacobian, Jacobian, Hessian of likelihood

name should be covhjh

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. llr has a chi-squared distribution with degrees of freedom df_model.

prsquared

McFadden’s pseudo-R-squared. 1 - (llf / llnull)

pvalues

The two-tailed p values for the t-stats of the params

resid_prob

Probability residual

Probability-scale residual is P(Y < y) − P(Y > y) where Y is the observed choice and y is a random variable corresponding to the predicted distribution.

Calculations based on work by:

Shepherd BE, Li C, Liu Q (2016) Probability-scale residuals for continuous, discrete, and censored data. The Canadian Journal of Statistics. 44:463-476.

Li C and Shepherd BE (2012) A new residual for ordinal outcomes. Biometrika. 99: 473-480

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

Methods

bootstrap([nrep, method, disp, store, rng])	Simple bootstrap to get mean and variance of estimator
conf_int([alpha])	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)	Get delta-method function for nonlinear tests, 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 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

Methods

bootstrap([nrep, method, disp, store, rng])	Simple bootstrap to get mean and variance of estimator
conf_int([alpha])	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)	Get delta-method function for nonlinear tests, 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 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