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statsmodels.regression.quantile_regression.QuantRegResults

class statsmodels.regression.quantile_regression.QuantRegResults(model, params, normalized_cov_params=None, scale=1.0)[source]

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_lr_test(restricted) Likelihood ratio test to test whether restricted model is correct
conf_int([alpha, cols]) Returns the confidence interval of the fitted parameters.
cov_params([r_matrix, column, scale, cov_p, ...]) Returns the variance/covariance matrix.
df_model()
df_resid()
ess()
f_pvalue()
f_test(r_matrix[, q_matrix, cov_p, scale, ...]) Compute an F-test for a joint linear hypothesis.
fittedvalues()
fvalue()
initialize(model, params, **kwd)
llf()
load(fname) load a pickle, (class method)
mse()
mse_model()
mse_resid()
mse_total()
nobs()
norm_resid() Residuals, normalized to have unit length and unit variance.
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()
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[, q_matrix, cov_p, scale]) Compute a t-test for a joint linear hypothesis of the form Rb = q
tvalues() Return the t-statistic for a given parameter estimate.
uncentered_tss()
wresid()

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