enpy_options: Options for the ENPY Algorithm
In pense: Penalized Elastic Net S/MM-Estimator of Regression

enpy_options

R Documentation

Options for the ENPY Algorithm

Description

Additional control options for the elastic net Peña-Yohai procedure.

Usage

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
)

Arguments

`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 `proportion`, a fixed number of observations is kept. If `threshold`, only observations with residuals below the threshold are kept.
`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 `<= 1`, only keep candidates from the last iteration.
`retain_max`	maximum number of candidates, i.e., only the best `retain_max` candidates are retained.

Details

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.