robregcc_option {robregcc}

Control parameter for model estimation:

Description

The model approach use scaled lasoo approach for model selection.

Usage

robregcc_option(
  maxiter = 10000,
  tol = 1e-10,
  nlam = 100,
  out.tol = 1e-08,
  lminfac = 1e-08,
  lmaxfac = 10,
  mu = 1,
  nu = 1.05,
  sp = 0.3,
  gamma = 2,
  outMiter = 3000,
  inMiter = 500,
  kmaxS = 500,
  tolS = 1e-04,
  nlamx = 20,
  nlamy = 20,
  spb = 0.3,
  spy = 0.3,
  lminfacX = 1e-06,
  lminfacY = 0.01,
  kfold = 10,
  fullpath = 0,
  sigmafac = 2
)

Arguments

maxiter

maximum number of iteration for convergence

tol

tolerance value set for convergence

nlam

number of lambda to be genrated to obtain solution path

out.tol

tolernce value set for convergence of outer loop

lminfac

a multiplier of determing lambda_min as a fraction of lambda_max

lmaxfac

a multiplier of lambda_max

mu

penalty parameter used in enforcing orthogonality

nu

penalty parameter used in enforcing orthogonality (incremental rate of mu)

sp

maximum proportion of nonzero elements in shift parameter

gamma

adaptive penalty weight exponential factor

outMiter

maximum number of outer loop iteration

inMiter

maximum number of inner loop iteration

kmaxS

maximum number of iteration for fast S estimator for convergence

tolS

tolerance value set for convergence in case of fast S estimator

nlamx

number of x lambda

nlamy

number of y lambda

spb

sparsity in beta

spy

sparsity in shift gamma

lminfacX

a multiplier of determing lambda_min as a fraction of lambda_max for sparsity in X

lminfacY

a multiplier of determing lambda_min as a fraction of lambda_max for sparsity in shift parameter

kfold

nummber of folds for crossvalidation

fullpath

1/0 to get full path yes/no

sigmafac

multiplying factor for the range of standard deviation

Value

a list of controling parameter.

Examples

 
# default options
library(robregcc)
control_default = robregcc_option()
# manual options
control_manual <- robregcc_option(maxiter=1000,tol = 1e-4,lminfac = 1e-7)