Options for the ENPY Algorithm

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.

Value

options for the ENPY algorithm.

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.

See also

Other functions for initial estimates: enpy_initial_estimates(), prinsens(), starting_point()