statsmodels.base.model.GenericLikelihoodModelResults

class statsmodels.base.model.GenericLikelihoodModelResults(model, mlefit)

A results class for the discrete dependent variable models

Warning

The following description has not been updated to this version/class. Where are AIC, BIC, ….? docstring looks like copy from discretemod

Parameters:

modelDiscreteModel instance

The fitted model instance.

mlefitinstance of LikelihoodResults

This contains the numerical optimization results as returned by LikelihoodModel.fit(), in a superclass of GenericLikelihoodModels.

Attributes:

aicfloat

Akaike information criterion. -2*(llf - p) where p is the number of regressors including the intercept.

bicfloat

Bayesian information criterion. -2*`llf` + ln(nobs)*p where p is the number of regressors including the intercept.

bsendarray

The standard errors of the coefficients.

df_residfloat

See model definition.

df_modelfloat

See model definition.

fitted_valuesndarray

Linear predictor XB.

llffloat

Value of the loglikelihood

llnullfloat

Value of the constant-only loglikelihood

llrfloat

Likelihood ratio chi-squared statistic; -2*(llnull - llf)

llr_pvaluefloat

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.

prsquaredfloat

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

Methods

bootstrap([nrep, method, disp, store, rng])	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)	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
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, 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)	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
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
pvalues	The two-tailed p values for the t-stats of the params
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