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 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)

Methods

 `aic`() `bic`() `bootstrap`([nrep, method, disp, store]) simple bootstrap to get mean and variance of estimator `bse`() `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 `covjhj`() covariance of parameters based on HJJH `df_modelwc`() `f_test`(r_matrix[, cov_p, scale, invcov]) Compute the F-test for a joint linear hypothesis. `get_nlfun`(fun) `hessv`() cached Hessian of log-likelihood `initialize`(model, params, **kwd) `llf`() `load`(fname) load a pickle, (class method) `normalized_cov_params`() `predict`([exog, transform]) Call self.model.predict with self.params as the first argument. `pvalues`() `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 `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.

Attributes

 `use_t`