3.9.2.2.4. statsmodels.regression.linear_model.OLSResults¶
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class
statsmodels.regression.linear_model.OLSResults(model, params, normalized_cov_params=None, scale=1.0, cov_type='nonrobust', cov_kwds=None, use_t=None)[source]¶ Results class for for an OLS model.
Most of the methods and attributes are inherited from RegressionResults. The special methods that are only available for OLS are:
- get_influence
- outlier_test
- el_test
- conf_int_el
See also
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__init__(model, params, normalized_cov_params=None, scale=1.0, cov_type='nonrobust', cov_kwds=None, use_t=None)¶
3.9.2.2.4.1. Methods¶
HC0_se() |
See statsmodels.RegressionResults |
HC1_se() |
See statsmodels.RegressionResults |
HC2_se() |
See statsmodels.RegressionResults |
HC3_se() |
See statsmodels.RegressionResults |
__init__(model, params[, ...]) |
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aic() |
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bic() |
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bse() |
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centered_tss() |
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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. |
conf_int_el(param_num[, sig, upper_bound, ...]) |
Computes the confidence interval for the parameter given by param_num |
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. |
el_test(b0_vals, param_nums[, ...]) |
Tests single or joint hypotheses of the regression parameters using Empirical Likelihood. |
ess() |
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f_pvalue() |
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f_test(r_matrix[, cov_p, scale, invcov]) |
Compute the F-test for a joint linear hypothesis. |
fittedvalues() |
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fvalue() |
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get_influence() |
get an instance of Influence with influence and outlier measures |
get_robustcov_results([cov_type, use_t]) |
create new results instance with robust covariance as default |
initialize(model, params, **kwd) |
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llf() |
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load(fname) |
load a pickle, (class method) |
mse_model() |
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mse_resid() |
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mse_total() |
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nobs() |
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normalized_cov_params() |
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outlier_test([method, alpha]) |
Test observations for outliers according to method |
predict([exog, transform]) |
Call self.model.predict with self.params as the first argument. |
pvalues() |
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remove_data() |
remove data arrays, all nobs arrays from result and model |
resid() |
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resid_pearson() |
Residuals, normalized to have unit variance. |
rsquared() |
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rsquared_adj() |
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save(fname[, remove_data]) |
save a pickle of this instance |
scale() |
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ssr() |
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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() |
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wald_test(r_matrix[, cov_p, scale, invcov, ...]) |
Compute a Wald-test for a joint linear hypothesis. |
wresid() |