This class summarizes the fit of a linear regression model.
It handles the output of contrasts, estimates of covariance, etc.
Returns: | **Attributes** : aic :
bic :
bse :
pinv_wexog :
centered_tss :
cov_HC0 :
cov_HC1 :
cov_HC2 :
cov_HC3 :
cov_type :
df_model :
df_resid :
ess :
fvalue :
f_pvalue :
fittedvalues :
het_scale :
HC0_se :
HC1_se :
HC2_se :
HC3_se :
model :
mse_model :
mse_resid :
mse_total :
nobs :
normalized_cov_params :
params :
pvalues :
resid :
resid_pearson :
rsquared :
rsquared_adj :
scale :
ssr :
uncentered_tss :
wresid :
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Methods
HC0_se() | See statsmodels.RegressionResults |
HC1_se() | See statsmodels.RegressionResults |
HC2_se() | See statsmodels.RegressionResults |
HC3_se() | See statsmodels.RegressionResults |
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_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_model() | |
mse_resid() | |
mse_total() | |
nobs() | |
normalized_cov_params() | |
predict([exog, transform]) | Call self.model.predict with self.params as the first argument. |
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. |
wresid() |
Attributes
use_t | bool(x) -> bool |