statsmodels.regression.linear_model.OLSResults.normalized_cov_params OLSResults.normalized_cov_params()

statsmodels.regression.linear_model.OLSResults.get_robustcov_results OLSResults.get_robustcov_results(cov_type='HC1', use_t=None, **kwds) create new results instance with robust covariance as default Parameters: cov_type : string the type of robust sandwich estimator to use. see Notes below use_t : bool If true, then the t distribution is used for inference. If false, then the normal distribution is used. kwds : depends on cov_type Required or optional arguments for robust covariance c

statsmodels.regression.linear_model.OLSResults.get_influence OLSResults.get_influence() [source] get an instance of Influence with influence and outlier measures Returns: infl : Influence instance the instance has methods to calculate the main influence and outlier measures for the OLS regression See also statsmodels.stats.outliers_influence.OLSInfluence

statsmodels.regression.linear_model.OLSResults.f_test OLSResults.f_test(r_matrix, cov_p=None, scale=1.0, invcov=None) Compute the F-test for a joint linear hypothesis. This is a special case of wald_test that always uses the F distribution. Parameters: r_matrix : array-like, str, or tuple array : An r x k array where r is the number of restrictions to test and k is the number of regressors. It is assumed that the linear combination is equal to zero. str : The full hypotheses to test can be

statsmodels.regression.linear_model.OLSResults.el_test OLSResults.el_test(b0_vals, param_nums, return_weights=0, ret_params=0, method='nm', stochastic_exog=1, return_params=0) [source] Tests single or joint hypotheses of the regression parameters using Empirical Likelihood. Parameters: b0_vals : 1darray The hypothesized value of the parameter to be tested param_nums : 1darray The parameter number to be tested print_weights : bool If true, returns the weights that optimize the likelihoo

statsmodels.regression.linear_model.OLSResults.cov_params OLSResults.cov_params(r_matrix=None, column=None, scale=None, cov_p=None, other=None) Returns the variance/covariance matrix. The variance/covariance matrix can be of a linear contrast of the estimates of params or all params multiplied by scale which will usually be an estimate of sigma^2. Scale is assumed to be a scalar. Parameters: r_matrix : array-like Can be 1d, or 2d. Can be used alone or with other. column : array-like, opti

statsmodels.regression.linear_model.OLSResults.conf_int_el OLSResults.conf_int_el(param_num, sig=0.05, upper_bound=None, lower_bound=None, method='nm', stochastic_exog=1) [source] Computes the confidence interval for the parameter given by param_num using Empirical Likelihood Parameters: param_num : float The parameter for which the confidence interval is desired sig : float The significance level. Default is .05 upper_bound : float The maximum value the upper limit can be. Default is

statsmodels.regression.linear_model.OLSResults.compare_lr_test OLSResults.compare_lr_test(restricted, large_sample=False) Likelihood ratio test to test whether restricted model is correct Parameters: restricted : Result instance The restricted model is assumed to be nested in the current model. The result instance of the restricted model is required to have two attributes, residual sum of squares, ssr, residual degrees of freedom, df_resid. large_sample : bool Flag indicating whether to

statsmodels.regression.linear_model.OLSResults.conf_int OLSResults.conf_int(alpha=0.05, cols=None) Returns the confidence interval of the fitted parameters. Parameters: alpha : float, optional The alpha level for the confidence interval. ie., The default alpha = .05 returns a 95% confidence interval. cols : array-like, optional cols specifies which confidence intervals to return Notes The confidence interval is based on Student?s t-distribution.

statsmodels.regression.linear_model.OLSResults.compare_lm_test OLSResults.compare_lm_test(restricted, demean=True, use_lr=False) Use Lagrange Multiplier test to test whether restricted model is correct Parameters: restricted : Result instance The restricted model is assumed to be nested in the current model. The result instance of the restricted model is required to have two attributes, residual sum of squares, ssr, residual degrees of freedom, df_resid. demean : bool Flag indicating whe