roblm.bimboot: Bootstraping for Bounded Influence Generalized M-Estimator...

In abnormally-distributed/cvreg: Cross Validation and Robust Estimation Utilities

Description

Bootstraping for Bounded Influence Generalized M-Estimator with Huber's psi and Schweppe weights

Usage

roblm.bimboot(
  formula,
  data,
  k = 2 * sqrt((ncol(x) + 1)/nrow(x)),
  conf.level = 0.95,
  parallel = TRUE,
  ncores = NULL,
  iter = 1000
)

Arguments

formula	model formula
data	data frame
k	Tuning constant for outlier downweighting. Defaults to 2*sqrt(p+1/n).
conf.level	the confidence level for computing the bootstrap confidence intervals. defaults to 0.95.
parallel	Whether or not to use parallel processing. Defaults to TRUE.
ncores	Number of cores to use. If not supplied, a cluster on the local machine consisting of the number of system cores minus one is created for the duration of the boot call.
iter	The number of bootstrap replicates. Defaults to 1000.

Value

an object of class "boot"

abnormally-distributed/cvreg documentation built on May 3, 2020, 3:45 p.m.