class statsmodels.base.model.GenericLikelihoodModelResults(model, mlefit)[source]

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

  • model (A DiscreteModel instance) –
  • mlefit (instance of LikelihoodResults) – This contains the numerical optimization results as returned by, in a superclass of GnericLikelihoodModels

  • *Attributes*
  • Warning most of these are not available yet
  • aic (float) – Akaike information criterion. -2*(llf - p) where p is the number of regressors including the intercept.
  • bic (float) – Bayesian information criterion. -2*`llf` + ln(nobs)*p where p is the number of regressors including the intercept.
  • bse (array) – The standard errors of the coefficients.
  • df_resid (float) – See model definition.
  • df_model (float) – See model definition.
  • fitted_values (array) – Linear predictor XB.
  • llf (float) – Value of the loglikelihood
  • llnull (float) – Value of the constant-only loglikelihood
  • llr (float) – Likelihood ratio chi-squared statistic; -2*(llnull - llf)
  • llr_pvalue (float) – 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 (float) – McFadden’s pseudo-R-squared. 1 - (llf/llnull)


bootstrap([nrep, method, disp, store]) simple bootstrap to get mean and variance of estimator
bsejac() standard deviation of parameter estimates based on covjac
bsejhj() standard deviation of parameter estimates based on covHJH
conf_int([alpha, cols, method]) Returns the confidence interval of the fitted parameters.
cov_params([r_matrix, column, scale, cov_p, …]) Returns the variance/covariance matrix.
covjac() covariance of parameters based on outer product of jacobian of log-likelihood
covjhj() covariance of parameters based on HJJH
f_test(r_matrix[, cov_p, scale, invcov]) Compute the F-test for a joint linear hypothesis.
hessv() cached Hessian of log-likelihood
initialize(model, params, **kwd)
load(fname) load a pickle, (class method)
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_obsv() cached Jacobian of log-likelihood
summary([yname, xname, title, alpha]) Summarize the 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
tvalues() Return the t-statistic for a given parameter estimate.
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