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
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
(ndarray) The standard errors of the coefficients.
df_resid
(float) See model definition.
df_model
(float) See model definition.
fitted_values
(ndarray) 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])Construct confidence interval for the fitted parameters.
cov_params
([r_matrix, column, scale, cov_p, …])Compute the variance/covariance matrix.
f_test
(r_matrix[, cov_p, scale, invcov])Compute the F-test for a joint linear hypothesis.
get_nlfun
(fun)This is not Implemented
initialize
(model, params, **kwargs)Initialize (possibly re-initialize) a Results instance.
load
(fname)Load a pickled results instance
See specific model class docstring
predict
([exog, transform])Call self.model.predict with self.params as the first argument.
Remove data arrays, all nobs arrays from result and model.
save
(fname[, remove_data])Save a pickle of this instance.
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.
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.
Methods
bootstrap
([nrep, method, disp, store])simple bootstrap to get mean and variance of estimator
conf_int
([alpha, cols])Construct confidence interval for the fitted parameters.
cov_params
([r_matrix, column, scale, cov_p, …])Compute the variance/covariance matrix.
f_test
(r_matrix[, cov_p, scale, invcov])Compute the F-test for a joint linear hypothesis.
get_nlfun
(fun)This is not Implemented
initialize
(model, params, **kwargs)Initialize (possibly re-initialize) a Results instance.
load
(fname)Load a pickled results instance
See specific model class docstring
predict
([exog, transform])Call self.model.predict with self.params as the first argument.
Remove data arrays, all nobs arrays from result and model.
save
(fname[, remove_data])Save a pickle of this instance.
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.
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.
Properties
Akaike information criterion
Bayesian information criterion
The standard errors of the parameter estimates.
standard deviation of parameter estimates based on covjac
standard deviation of parameter estimates based on covHJH
covariance of parameters based on outer product of jacobian of log-likelihood
covariance of parameters based on HJJH
Model WC
cached Hessian of log-likelihood
Log-likelihood of model
The two-tailed p values for the t-stats of the params.
cached Jacobian of log-likelihood
Return the t-statistic for a given parameter estimate.
Flag indicating to use the Student’s distribution in inference.