statsmodels.regression.linear_model.RegressionResults

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)

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).