statsmodels.regression.linear_model.RegressionResults¶
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class
statsmodels.regression.linear_model.
RegressionResults
(model, params, normalized_cov_params=None, scale=1.0, cov_type='nonrobust', cov_kwds=None, use_t=None, **kwargs)[source]¶ This class summarizes the fit of a linear regression model.
It handles the output of contrasts, estimates of covariance, etc.
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.
Attributes
pinv_wexog See model class docstring for implementation details. cov_type Parameter covariance estimator used for standard errors and t-stats. df_model Model degrees of freedom. The number of regressors p. Does not include the constant if one is present. df_resid Residual degrees of freedom. n - p - 1, if a constant is present. n - p if a constant is not included. het_scale adjusted squared residuals for heteroscedasticity robust standard errors. Is only available after HC#_se or cov_HC# is called. See HC#_se for more information. history Estimation history for iterative estimators. model A pointer to the model instance that called fit() or results. params The linear coefficients that minimize the least squares criterion. This is usually called Beta for the classical linear model. 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, scale, 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. 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, 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. cov_params
([r_matrix, column, scale, cov_p, …])Compute the variance/covariance matrix. f_test
(r_matrix[, cov_p, scale, 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. 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, 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).