2.7.1.1.7. 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)

__init__(model, mlefit)[source]¶

Methods

__init__(model, mlefit)
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)
jacv(*args, **kwds)	jacv is deprecated, use score_obsv instead!
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