statsmodels.regression.linear_model.OLSResults¶
-
class
statsmodels.regression.linear_model.
OLSResults
(model, params, normalized_cov_params=None, scale=1.0, cov_type='nonrobust', cov_kwds=None, use_t=None, **kwargs)[source]¶ Results class for for an OLS model.
Parameters: model : RegressionModel
The regression model instance.
params : ndarray
The estimated parameters.
normalized_cov_params : ndarray
The normalized covariance parameters.
scale : float
The estimated scale of the residuals.
cov_type : str
The covariance estimator used in the results.
cov_kwds : dict
Additional keywords used in the covariance specification.
use_t : bool
Flag indicating to use the Student’s t in inference.
**kwargs
Additional keyword arguments used to initialize the results.
See also
RegressionResults
- Results store for WLS and GLW models.
Notes
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
Attributes
use_t
Flag indicating to use the Student’s distribution in inference. 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. conf_int_el
(param_num[, sig, upper_bound, …])Compute the confidence interval using Empirical Likelihood. cov_params
([r_matrix, column, scale, cov_p, …])Compute the variance/covariance matrix. el_test
(b0_vals, param_nums[, …])Test single or joint hypotheses using Empirical Likelihood. f_test
(r_matrix[, cov_p, scale, invcov])Compute the F-test for a joint linear hypothesis. get_influence
()Calculate influence and outlier measures. 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, **kwargs)Initialize (possibly re-initialize) a Results instance. load
(fname)Load a pickled results instance normalized_cov_params
()See specific model class docstring outlier_test
([method, alpha, labels, order, …])Test observations for outliers according to method. 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, scale, 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, 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. 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. conf_int_el
(param_num[, sig, upper_bound, …])Compute the confidence interval using Empirical Likelihood. cov_params
([r_matrix, column, scale, cov_p, …])Compute the variance/covariance matrix. el_test
(b0_vals, param_nums[, …])Test single or joint hypotheses using Empirical Likelihood. f_test
(r_matrix[, cov_p, scale, invcov])Compute the F-test for a joint linear hypothesis. get_influence
()Calculate influence and outlier measures. 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, **kwargs)Initialize (possibly re-initialize) a Results instance. load
(fname)Load a pickled results instance normalized_cov_params
()See specific model class docstring outlier_test
([method, alpha, labels, order, …])Test observations for outliers according to method. 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, scale, 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, 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. Properties
HC0_se
White’s (1980) heteroskedasticity robust standard errors. HC1_se
MacKinnon and White’s (1985) heteroskedasticity robust standard errors. HC2_se
MacKinnon and White’s (1985) heteroskedasticity robust standard errors. HC3_se
MacKinnon and White’s (1985) heteroskedasticity robust standard errors. aic
Akaike’s information criteria. bic
Bayes’ information criteria. bse
The standard errors of the parameter estimates. centered_tss
The total (weighted) sum of squares centered about the mean. 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
Log-likelihood of model mse_model
Mean squared error the model. mse_resid
Mean squared error of the residuals. mse_total
Total mean squared error. nobs
Number of observations n. 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
R-squared of the model. rsquared_adj
Adjusted R-squared. ssr
Sum of squared (whitened) residuals. tvalues
Return the t-statistic for a given parameter estimate. uncentered_tss
Uncentered sum of squares. use_t
Flag indicating to use the Student’s distribution in inference. wresid
The residuals of the transformed/whitened regressand and regressor(s).