enpy_options | R Documentation |
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 |
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.
options for the ENPY algorithm.
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