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

Attributes:

HC0_se

HC1_se

HC2_se

HC3_se

aic

bic

bse

The standard errors of the parameter estimates.

centered_tss

condition_number

Return condition number of exogenous matrix.

Calculated as ratio of largest to smallest singular value of the exogenous variables. This value is the same as the square root of the ratio of the largest to smallest eigenvalue of the inner-product of the exogenous variables.

cov_HC0

Heteroscedasticity robust covariance matrix. See HC0_se.

cov_HC1

Heteroscedasticity robust covariance matrix. See HC1_se.

cov_HC2

Heteroscedasticity robust covariance matrix. See HC2_se.

cov_HC3

Heteroscedasticity robust covariance matrix. See HC3_se.

eigenvals

Return eigenvalues sorted in decreasing order.

ess

The explained sum of squares.

If a constant is present, the centered total sum of squares minus the sum of squared residuals. If there is no constant, the uncentered total sum of squares is used.

f_pvalue

The p-value of the F-statistic.

fittedvalues

The predicted values for the original (unwhitened) design.

fvalue

F-statistic of the fully specified model.

Calculated as the mean squared error of the model divided by the mean squared error of the residuals if the nonrobust covariance is used. Otherwise computed using a Wald-like quadratic form that tests whether all coefficients (excluding the constant) are zero.

llf

mse

mse_model

mse_resid

Mean squared error of the residuals.

The sum of squared residuals divided by the residual degrees of freedom.

mse_total

nobs

Number of observations n.

prsquared

pvalues

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

resid

The residuals of the model.

resid_pearson

Residuals, normalized to have unit variance.

array_like

The array wresid normalized by the sqrt of the scale to have unit variance.

rsquared

rsquared_adj

ssr

Sum of squared (whitened) residuals.

tvalues

Return the t-statistic for a given parameter estimate.

uncentered_tss

use_t

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

wresid

The residuals of the transformed/whitened regressand and regressor(s).

Methods

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 a set of linear restrictions.
compare_lr_test(restricted[, large_sample])	Likelihood ratio test to test whether restricted model is correct.
conf_int([alpha, cols])	Compute the confidence interval of 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_prediction([exog, transform, weights, ...])	Compute prediction results.
get_robustcov_results([cov_type, use_t])	Create new results instance with robust covariance as default.
info_criteria(crit[, dk_params])	Return an information criterion for the model.
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.
remove_data()	Remove data arrays, all nobs arrays from result and model.
save(fname[, remove_data])	Save a pickle of this instance.
scale()	A scale factor for the covariance matrix.
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, 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

HC0_se
HC1_se
HC2_se
HC3_se
aic
bic
bse	The standard errors of the parameter estimates.
centered_tss
condition_number	Return condition number of exogenous matrix.
cov_HC0	Heteroscedasticity robust covariance matrix.
cov_HC1	Heteroscedasticity robust covariance matrix.
cov_HC2	Heteroscedasticity robust covariance matrix.
cov_HC3	Heteroscedasticity robust covariance matrix.
eigenvals	Return eigenvalues sorted in decreasing order.
ess	The explained sum of squares.
f_pvalue	The p-value of the F-statistic.
fittedvalues	The predicted values for the original (unwhitened) design.
fvalue	F-statistic of the fully specified model.
llf
mse
mse_model
mse_resid	Mean squared error of the residuals.
mse_total
nobs	Number of observations n.
prsquared
pvalues	The two-tailed p values for the t-stats of the params.
resid	The residuals of the model.
resid_pearson	Residuals, normalized to have unit variance.
rsquared
rsquared_adj
ssr	Sum of squared (whitened) residuals.
tvalues	Return the t-statistic for a given parameter estimate.
uncentered_tss
use_t	Flag indicating to use the Student's distribution in inference.
wresid	The residuals of the transformed/whitened regressand and regressor(s).