lmrobM.control {RobStatTM}

Tuning parameters for lmrobM

Description

This function sets tuning parameters for the M estimators of regression implemented in lmrobM.

Usage

lmrobM.control(
  bb = 0.5,
  efficiency = 0.99,
  family = "opt",
  tuning.chi,
  tuning.psi,
  max.it = 100,
  rel.tol = 1e-07,
  mscale_tol = 1e-06,
  mscale_maxit = 50,
  trace.lev = 0
)

Arguments

bb

tuning constant (between 0 and 1/2) for the M-scale used to compute the residual scale estimator. Defaults to 0.5.

efficiency

desired asymptotic efficiency of the final regression M-estimator. Defaults to 0.85.

family

string specifying the name of the family of loss function to be used (current valid options are "bisquare", "opt" and "mopt"). Incomplete entries will be matched to the current valid options.

tuning.chi

tuning constant for the function used to compute the M-scale used for the residual scale estimator. If missing, it is computed inside lmrobdet.control to match the value of bb according to the family of rho functions specified in family.

tuning.psi

tuning parameters for the regression M-estimator computed with a rho function as specified with argument family. If missing, it is computed inside lmrobdet.control to match the value of efficiency according to the family of rho functions specified in family. Appropriate values for tuning.psi for a given desired efficiency for Gaussian errors can be constructed using the functions bisquare, mopt and opt.

max.it

maximum number of IRWLS iterations for the M-estimator

rel.tol

relative covergence tolerance for the IRWLS iterations for the M-estimator

mscale_tol

Convergence tolerance for the M-scale algorithm. See mscale.

mscale_maxit

Maximum number of iterations for the M-scale algorithm. See mscale.

trace.lev

positive values (increasingly) provide details on the progress of the M-algorithm

Value

A list with the necessary tuning parameters.

Author(s)

Matias Salibian-Barrera, [e-mail]

Examples

data(coleman, package='robustbase')
m2 <- lmrobM(Y ~ ., data=coleman, control=lmrobM.control())
m2
summary(m2)