regression.linear_model.RegressionResults()

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) [source]

This class summarizes the fit of a linear regression model.

It handles the output of contrasts, estimates of covariance, etc.

Returns:

**Attributes** : aic : Aikake?s information criteria. For a model with a constant . For a model without a constant . bic : Bayes? information criteria For a model with a constant . For a model without a constant bse : The standard errors of the parameter estimates. pinv_wexog : See specific model class docstring centered_tss : The total (weighted) sum of squares centered about the mean. cov_HC0 : Heteroscedasticity robust covariance matrix. See HC0_se below. cov_HC1 : Heteroscedasticity robust covariance matrix. See HC1_se below. cov_HC2 : Heteroscedasticity robust covariance matrix. See HC2_se below. cov_HC3 : Heteroscedasticity robust covariance matrix. See HC3_se below. cov_type : Parameter covariance estimator used for standard errors and t-stats df_model : Model degress 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. ess : Explained sum of squares. If a constant is present, the centered total sum of squares minus the sum of squared residuals. If there is no constant, the uncentered total sum of squares is used. fvalue : F-statistic of the fully specified model. Calculated as the mean squared error of the model divided by the mean squared error of the residuals. f_pvalue : p-value of the F-statistic fittedvalues : The predicted the values for the original (unwhitened) design. 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. HC0_se : White?s (1980) heteroskedasticity robust standard errors. Defined as sqrt(diag(X.T X)^(-1)X.T diag(e_i^(2)) X(X.T X)^(-1) where e_i = resid[i] HC0_se is a cached property. When HC0_se or cov_HC0 is called the RegressionResults instance will then have another attribute het_scale, which is in this case is just resid**2. HC1_se : MacKinnon and White?s (1985) alternative heteroskedasticity robust standard errors. Defined as sqrt(diag(n/(n-p)*HC_0) HC1_see is a cached property. When HC1_se or cov_HC1 is called the RegressionResults instance will then have another attribute het_scale, which is in this case is n/(n-p)*resid**2. HC2_se : MacKinnon and White?s (1985) alternative heteroskedasticity robust standard errors. Defined as (X.T X)^(-1)X.T diag(e_i^(2)/(1-h_ii)) X(X.T X)^(-1) where h_ii = x_i(X.T X)^(-1)x_i.T HC2_see is a cached property. When HC2_se or cov_HC2 is called the RegressionResults instance will then have another attribute het_scale, which is in this case is resid^(2)/(1-h_ii). HC3_se : MacKinnon and White?s (1985) alternative heteroskedasticity robust standard errors. Defined as (X.T X)^(-1)X.T diag(e_i^(2)/(1-h_ii)^(2)) X(X.T X)^(-1) where h_ii = x_i(X.T X)^(-1)x_i.T HC3_see is a cached property. When HC3_se or cov_HC3 is called the RegressionResults instance will then have another attribute het_scale, which is in this case is resid^(2)/(1-h_ii)^(2). model : A pointer to the model instance that called fit() or results. mse_model : Mean squared error the model. This is the explained sum of squares divided by the model degrees of freedom. mse_resid : Mean squared error of the residuals. The sum of squared residuals divided by the residual degrees of freedom. mse_total : Total mean squared error. Defined as the uncentered total sum of squares divided by n the number of observations. nobs : Number of observations n. normalized_cov_params : See specific model class docstring params : The linear coefficients that minimize the least squares criterion. This is usually called Beta for the classical linear model. pvalues : The two-tailed p values for the t-stats of the params. resid : The residuals of the model. resid_pearson : wresid normalized to have unit variance. rsquared : R-squared of a model with an intercept. This is defined here as 1 - ssr/centered_tss if the constant is included in the model and 1 - ssr/uncentered_tss if the constant is omitted. rsquared_adj : Adjusted R-squared. This is defined here as 1 - (nobs-1)/df_resid * (1-rsquared) if a constant is included and 1 - nobs/df_resid * (1-rsquared) if no constant is included. scale : A scale factor for the covariance matrix. Default value is ssr/(n-p). Note that the square root of scale is often called the standard error of the regression. ssr : Sum of squared (whitened) residuals. uncentered_tss : Uncentered sum of squares. Sum of the squared values of the (whitened) endogenous response variable. wresid : The residuals of the transformed/whitened regressand and regressor(s)

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