Usage
garchFitControl(
llh = c("filter", "internal", "testing"),
nlminb.eval.max = 2000,
nlminb.iter.max = 1500,
nlminb.abs.tol = 1.0e-20,
nlminb.rel.tol = 1.0e-14,
nlminb.x.tol = 1.0e-14,
nlminb.step.min = 2.2e-14,
nlminb.scale = 1,
nlminb.fscale = FALSE,
nlminb.xscale = FALSE,
sqp.mit = 200,
sqp.mfv = 500,
sqp.met = 2,
sqp.mec = 2,
sqp.mer = 1,
sqp.mes = 4,
sqp.xmax = 1.0e3,
sqp.tolx = 1.0e-16,
sqp.tolc = 1.0e-6,
sqp.tolg = 1.0e-6,
sqp.told = 1.0e-6,
sqp.tols = 1.0e-4,
sqp.rpf = 1.0e-4,
lbfgsb.REPORT = 10,
lbfgsb.lmm = 20,
lbfgsb.pgtol = 1e-14,
lbfgsb.factr = 1,
lbfgsb.fnscale = FALSE,
lbfgsb.parscale = FALSE,
nm.ndeps = 1e-14,
nm.maxit = 10000,
nm.abstol = 1e-14,
nm.reltol = 1e-14,
nm.alpha = 1.0,
nm.beta = 0.5,
nm.gamma = 2.0,
nm.fnscale = FALSE,
nm.parscale = FALSE)Arguments
llh
llh = c("filter", "internal", "testing")[1],
defaults to "filter".
nlminb.eval.max
Maximum number of evaluations of the objective function
allowed, defaults to 200.
nlminb.iter.max
Maximum number of iterations allowed, defaults to 150.
nlminb.abs.tol
Absolute tolerance, defaults to 1e-20.
nlminb.rel.tol
Relative tolerance, defaults to 1e-10.
nlminb.x.tol
X tolerance, defaults to 1.5e-8.
nlminb.fscale
defaults to FALSE.
nlminb.xscale
defaulkts to FALSE.
nlminb.step.min
Minimum step size, defaults to 2.2e-14.
nlminb.scale
defaults to 1.
sqp.mit
maximum number of iterations, defaults to 200.
sqp.mfv
maximum number of function evaluations, defaults to 500.
sqp.met
specifies scaling strategy:
sqp.met=1 - no scaling
sqp.met=2 - preliminary scaling in 1st iteration (default)
sqp.met=3 - controlled scaling
sqp.met=4 - interval scaling
sqp.met=5 - permanent scaling in all iterations
sqp.mec
correction for negative curvature:
sqp.mec=1 - no correction
sqp.mec=2 - Powell correction (default)
sqp.mer
restarts after unsuccessful variable metric updates:
sqp.mer=0 - no restarts
sqp.mer=1 - standard restart
sqp.mes
interpolation method selection in a line search:
sqp.mes=1 - bisection
sqp.mes=2 - two point quadratic interpolation
sqp.mes=3 - three point quadratic interpolation
sqp.mes=4 - three point cubic interpolation (default)
sqp.xmax
maximum stepsize, defaults to 1.0e+3.
sqp.tolx
tolerance for the change of the coordinate vector,
defaults to 1.0e-16.
sqp.tolc
tolerance for the constraint violation,
defaults to 1.0e-6.
sqp.tolg
tolerance for the Lagrangian function gradient,
defaults to 1.0e-6.
sqp.told
defaults to 1.0e-6.
sqp.tols
defaults to 1.0e-4.
sqp.rpf
value of the penalty coefficient,
default to1.0D-4.
The default velue may be relatively small. Therefore, larger
value, say one, can sometimes be more suitable.
lbfgsb.REPORT
The frequency of reports for the "BFGS" and "L-BFGS-B" methods if
control$trace is positive. Defaults to every 10 iterations.
lbfgsb.lmm
is an integer giving the number of BFGS updates retained in
the "L-BFGS-B" method, It defaults to 5.
lbfgsb.factr
controls the convergence of the "L-BFGS-B" method. Convergence
occurs when the reduction in the objective is within this factor
of the machine tolerance. Default is 1e7, that is a tolerance
of about 1.0e-8.
lbfgsb.pgtol
helps control the convergence of the "L-BFGS-B" method. It is a
tolerance on the projected gradient in the current search
direction. This defaults to zero, when the check is suppressed.
lbfgsb.fnscale
defaults to FALSE.
lbfgsb.parscale
defaults to FALSE.
nm.ndeps
A vector of step sizes for the finite-difference approximation
to the gradient, on par/parscale scale. Defaults to 1e-3.
nm.maxit
The maximum number of iterations. Defaults to 100 for the
derivative-based methods, and 500 for "Nelder-Mead". For "SANN"
maxit gives the total number of function evaluations. There is
no other stopping criterion. Defaults to 10
nm.abstol
The absolute convergence tolerance. Only useful for non-negative
functions, as a tolerance for reaching zero.
nm.reltol
Relative convergence tolerance. The algorithm stops if it is
unable to reduce the value by a factor of
reltol * (abs(val) + reltol) at a step. Defaults to
sqrt(.Machine$double.eps), typically about 1e-8.
nm.alpha, nm.beta, nm.gamma
Scaling parameters for the "Nelder-Mead" method.
alpha is the reflection factor (default 1.0),
beta the contraction factor (0.5), and
gamma the expansion factor (2.0).
nm.fnscale
An overall scaling to be applied to the value of fn and gr
during optimization. If negative, turns the problem into a
maximization problem. Optimization is performed on
fn(par)/fnscale.
nm.parscale
A vector of scaling values for the parameters. Optimization is
performed on par/parscale and these should be comparable in the
sense that a unit change in any element produces about a unit
change in the scaled value.