Additional control options for the elastic net Peña-Yohai procedure.
enpy_options( max_it = 10, keep_psc_proportion = 0.5, en_algorithm_opts, keep_residuals_measure = c("threshold", "proportion"), keep_residuals_proportion = 0.5, keep_residuals_threshold = 2, retain_best_factor = 2, retain_max = 500 )
| max_it | maximum number of EN-PY iterations. |
|---|---|
| keep_psc_proportion | how many observations should to keep based on the Principal Sensitivity Components. |
| en_algorithm_opts | options for the LS-EN algorithm. See en_algorithm_options for details. |
| keep_residuals_measure | how to determine what observations to keep, based on their residuals.
If |
| keep_residuals_proportion | proportion of observations to kept based on their residuals. |
| keep_residuals_threshold | only observations with (standardized) residuals less than this threshold are kept. |
| retain_best_factor | only keep candidates that are within this factor of the best candidate. If |
| retain_max | maximum number of candidates, i.e., only the best |
options for the ENPY algorithm.
The EN-PY procedure for computing initial estimates iteratively cleans the data of observations with possibly outlying residual or high leverage. Least-squares elastic net (LS-EN) estimates are computed on the possibly clean subsets. At each iteration, the Principal Sensitivity Components are computed to remove observations with potentially high leverage. Among all the LS-EN estimates, the estimate with smallest M-scale of the residuals is selected. Observations with largest residual for the selected estimate are removed and the next iteration is started.