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
Returns: 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