# statsmodels.stats.outliers_influence.OLSInfluence¶

class statsmodels.stats.outliers_influence.OLSInfluence(results)[source]

class to calculate outlier and influence measures for OLS result

Parameters
resultsRegressionResults

currently assumes the results are from an OLS regression

Notes

One part of the results can be calculated without any auxiliary regression (some of which have the _internal postfix in the name. Other statistics require leave-one-observation-out (LOOO) auxiliary regression, and will be slower (mainly results with _external postfix in the name). The auxiliary LOOO regression only the required results are stored.

Using the LOO measures is currently only recommended if the data set is not too large. One possible approach for LOOO measures would be to identify possible problem observations with the _internal measures, and then run the leave-one-observation-out only with observations that are possible outliers. (However, this is not yet available in an automized way.)

This should be extended to general least squares.

The leave-one-variable-out (LOVO) auxiliary regression are currently not used.

Attributes
det_cov_params_not_obsi

determinant of cov_params of all LOOO regressions

params_not_obsi

parameter estimates for all LOOO regressions

Methods

 Cooks distance covariance ratio between LOOO and original dfbetas uses results from leave-one-observation-out loop dffits measure for influence of an observation dffits measure for influence of an observation Error sum of squares of PRESS residuals calculate studentized residuals Factor of diagonal of hat_matrix used in influence Diagonal of the hat_matrix for OLS Influence measure plot_index([y_var, threshold, title, ax, idx]) index plot for influence attributes plot_influence([external, alpha, criterion, …]) Plot of influence in regression. PRESS residuals estimate of standard deviation of the residuals Studentized residuals using variance from OLS Studentized residuals using LOOO variance Studentized residuals using variance from OLS estimate of variance of the residuals error variance for all LOOO regressions Creates a DataFrame with all available influence results. summary_table([float_fmt]) create a summary table with all influence and outlier measures