scikits.statsmodels.base.model.GenericLikelihoodModelResults

class scikits.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 :

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

bootstrap([nrep, method, disp, store])	simple bootstrap to get mean and variance of estimator
conf_int([alpha, cols, method])	Returns the confidence interval of the fitted parameters.
cov_params([r_matrix, column, scale, other])	Returns the variance/covariance matrix.
f_test(r_matrix[, q_matrix, scale, invcov])	Compute an Fcontrast/F-test for a contrast matrix.
get_nlfun(fun)
initialize(model, params, **kwd)
normalized_cov_params()
t([column])	deprecated: Return the t-statistic for a given parameter estimate.
t_test(r_matrix[, q_matrix, scale])	Compute a tcontrast/t-test for a row vector array of the form Rb = q