# statsmodels.base.model.GenericLikelihoodModelResults¶

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

Parameters:
model`A` `DiscreteModel` `instance`
mlefit`instance` `of` `LikelihoodResults`

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

Attributes:
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`ndarray`

The standard errors of the coefficients.

df_resid`float`

See model definition.

df_model`float`

See model definition.

fitted_values`ndarray`

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`

 `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 See specific model class docstring `predict`([exog, transform]) Call self.model.predict with self.params as the first argument. 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.
 `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.