statsmodels.base.model.GenericLikelihoodModelResults

class statsmodels.base.model.GenericLikelihoodModelResults(model, mlefit)

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

modelA DiscreteModel instance

mlefitinstance of LikelihoodResults

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

Attributes

aicfloat

Akaike information criterion

bicfloat

Bayesian information criterion

bsearray

The standard errors of the parameter estimates.

df_residfloat

See model definition.

df_modelfloat

See model definition.

fitted_valuesarray

Linear predictor XB.

llffloat

Log-likelihood of model

llnullfloat

Value of the constant-only loglikelihood

llrfloat

Likelihood ratio chi-squared statistic; -2*(llnull - llf)

llr_pvaluefloat

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.

prsquaredfloat

McFadden’s pseudo-R-squared. 1 - (llf/llnull)

Methods

aic()	Akaike information criterion
bic()	Bayesian information criterion
bootstrap([nrep, method, disp, store])	simple bootstrap to get mean and variance of estimator
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
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
df_modelwc()	Model WC
f_test(r_matrix[, cov_p, scale, invcov])	Compute the F-test for a joint linear hypothesis.
get_nlfun(fun)	This is not Implemented
hessv()	cached Hessian of log-likelihood
initialize(model, params, **kwd)	Initialize (possibly re-initialize) a Results instance.
llf()	Log-likelihood of model
load(fname)	load a pickle, (class method)
normalized_cov_params()	See specific model class docstring
predict([exog, transform])	Call self.model.predict with self.params as the first argument.
pvalues()	The two-tailed p values for the t-stats of the params.
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