lmrobM.control: 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
)

Value

A list with the necessary tuning parameters.

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 scaleM.
mscale_maxit: Maximum number of iterations for the M-scale algorithm. See scaleM.
trace.lev: positive values (increasingly) provide details on the progress of the M-algorithm

Author

Matias Salibian-Barrera, matias@stat.ubc.ca

Examples

Run this code

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

Run the code above in your browser using DataLab