statsmodels.othermod.betareg.BetaResults¶

class statsmodels.othermod.betareg.BetaResults(model, mlefit)[source]¶

Results class for Beta regression

This class inherits from GenericLikelihoodModelResults and not all inherited methods might be appropriate in this case.

Attributes:¶

aic

Akaike information criterion

bic

Bayesian information criterion

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

covjac

covariance of parameters based on outer product of jacobian of log-likelihood

covjhj

covariance of parameters based on HJJH

dot product of Hessian, Jacobian, Jacobian, Hessian of likelihood

name should be covhjh

df_modelwc

Model WC

fitted_precision

In-sample predicted precision

fittedvalues

In-sample predicted mean, conditional expectation.

hessv

cached Hessian of log-likelihood

llf

Log-likelihood of model

llnull

Value of the constant-only loglikelihood

llr

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

llr_pvalue

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

Cox-Snell Likelihood-Ratio pseudo-R-squared.

1 - exp((llnull - .llf) * (2 / nobs))

pvalues

The two-tailed p values for the t-stats of the params.

resid

Response residual

resid_pearson

Pearson standardize residual

score_obsv

cached Jacobian of log-likelihood

tvalues

Return the t-statistic for a given parameter estimate.

use_t

Flag indicating to use the Student’s distribution in inference.

Methods

bootstrap(*args, **kwargs)	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, invcov])	Compute the F-test for a joint linear hypothesis.
get_distribution([exog, exog_precision, ...])	Return a instance of the predictive distribution.
get_distribution_params([exog, ...])	Return distribution parameters converted from model prediction.
get_influence()	Get an instance of MLEInfluence with influence and outlier measures
get_nlfun(fun)	This is not Implemented
get_prediction([exog, which, transform, ...])	Compute prediction results when endpoint transformation is valid.
initialize(model, params, **kwargs)	Initialize (possibly re-initialize) a Results instance.
load(fname)	Load a pickled results instance
normalized_cov_params()	See specific model class docstring
predict([exog, transform])	Call self.model.predict with self.params as the first argument.
pseudo_rsquared([kind])	McFadden's pseudo-R-squared.
remove_data()	Remove data arrays, all nobs arrays from result and model.
save(fname[, remove_data])	Save a pickle of this instance.
set_null_options([llnull, attach_results])	Set the fit options for the Null (constant-only) model.
summary([yname, xname, title, alpha])	Summarize the Regression Results
t_test(r_matrix[, cov_p, 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, invcov, use_f, ...])	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

aic	Akaike information criterion
bic	Bayesian information criterion
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
covjac	covariance of parameters based on outer product of jacobian of log-likelihood
covjhj	covariance of parameters based on HJJH
df_modelwc	Model WC
fitted_precision	In-sample predicted precision
fittedvalues	In-sample predicted mean, conditional expectation.
hessv	cached Hessian of log-likelihood
llf	Log-likelihood of model
llnull	Value of the constant-only loglikelihood
llr	Likelihood ratio chi-squared statistic; -2*(llnull - llf)
llr_pvalue	The chi-squared probability of getting a log-likelihood ratio statistic greater than llr.
prsquared	Cox-Snell Likelihood-Ratio pseudo-R-squared.
pvalues	The two-tailed p values for the t-stats of the params.
resid	Response residual
resid_pearson	Pearson standardize residual
score_obsv	cached Jacobian of log-likelihood
tvalues	Return the t-statistic for a given parameter estimate.
use_t	Flag indicating to use the Student's distribution in inference.