statsmodels.regression.quantile_regression.QuantRegResults

class statsmodels.regression.quantile_regression.QuantRegResults(model, params, normalized_cov_params=None, scale=1.0, cov_type='nonrobust', cov_kwds=None, use_t=None, **kwargs)

Results instance for the QuantReg model

Methods

HC0_se()
HC1_se()
HC2_se()
HC3_se()
aic()
bic()
bse()
centered_tss()
compare_f_test(restricted)	use F test to test whether restricted model is correct
compare_lm_test(restricted[, demean, use_lr])	Use Lagrange Multiplier test to test whether restricted model is correct
compare_lr_test(restricted[, large_sample])	Likelihood ratio test to test whether restricted model is correct
condition_number()	Return condition number of exogenous matrix.
conf_int([alpha, cols])	Returns the confidence interval of the fitted parameters.
cov_HC0()	See statsmodels.RegressionResults
cov_HC1()	See statsmodels.RegressionResults
cov_HC2()	See statsmodels.RegressionResults
cov_HC3()	See statsmodels.RegressionResults
cov_params([r_matrix, column, scale, cov_p, ...])	Returns the variance/covariance matrix.
eigenvals()	Return eigenvalues sorted in decreasing order.
ess()
f_pvalue()
f_test(r_matrix[, cov_p, scale, invcov])	Compute the F-test for a joint linear hypothesis.
fittedvalues()
fvalue()
get_prediction([exog, transform, weights, ...])	compute prediction results
get_robustcov_results([cov_type, use_t])	create new results instance with robust covariance as default
initialize(model, params, **kwd)
llf()
load(fname)	load a pickle, (class method)
mse()
mse_model()
mse_resid()
mse_total()
nobs()
normalized_cov_params()
predict([exog, transform])	Call self.model.predict with self.params as the first argument.
prsquared()
pvalues()
remove_data()	remove data arrays, all nobs arrays from result and model
resid()
resid_pearson()	Residuals, normalized to have unit variance.
rsquared()
rsquared_adj()
save(fname[, remove_data])	save a pickle of this instance
scale()
ssr()
summary([yname, xname, title, alpha])	Summarize the Regression Results
summary2([yname, xname, title, alpha, ...])	Experimental summary function to 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.
uncentered_tss()
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
wresid()

Methods

HC0_se()
HC1_se()
HC2_se()
HC3_se()
aic()
bic()
bse()
centered_tss()
compare_f_test(restricted)	use F test to test whether restricted model is correct
compare_lm_test(restricted[, demean, use_lr])	Use Lagrange Multiplier test to test whether restricted model is correct
compare_lr_test(restricted[, large_sample])	Likelihood ratio test to test whether restricted model is correct
condition_number()	Return condition number of exogenous matrix.
conf_int([alpha, cols])	Returns the confidence interval of the fitted parameters.
cov_HC0()	See statsmodels.RegressionResults
cov_HC1()	See statsmodels.RegressionResults
cov_HC2()	See statsmodels.RegressionResults
cov_HC3()	See statsmodels.RegressionResults
cov_params([r_matrix, column, scale, cov_p, ...])	Returns the variance/covariance matrix.
eigenvals()	Return eigenvalues sorted in decreasing order.
ess()
f_pvalue()
f_test(r_matrix[, cov_p, scale, invcov])	Compute the F-test for a joint linear hypothesis.
fittedvalues()
fvalue()
get_prediction([exog, transform, weights, ...])	compute prediction results
get_robustcov_results([cov_type, use_t])	create new results instance with robust covariance as default
initialize(model, params, **kwd)
llf()
load(fname)	load a pickle, (class method)
mse()
mse_model()
mse_resid()
mse_total()
nobs()
normalized_cov_params()
predict([exog, transform])	Call self.model.predict with self.params as the first argument.
prsquared()
pvalues()
remove_data()	remove data arrays, all nobs arrays from result and model
resid()
resid_pearson()	Residuals, normalized to have unit variance.
rsquared()
rsquared_adj()
save(fname[, remove_data])	save a pickle of this instance
scale()
ssr()
summary([yname, xname, title, alpha])	Summarize the Regression Results
summary2([yname, xname, title, alpha, ...])	Experimental summary function to 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.
uncentered_tss()
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
wresid()

Attributes

use_t