R: Auxiliary for Controlling non linear GAMLSS Fitting
NL.control
R Documentation
Auxiliary for Controlling non linear GAMLSS Fitting
Description
This ia an auxiliary function used to control the iterations for nlgamlss fitting. Typically
only used when calling nlgamlss function with the option control.
Since the nlgamlss uses nlm for fitting all of the NL.control argument are passed to
nlm.
an estimate of the size of log-likelihood at the minimum with default equal 1.
typsize
this argument is passed to nlm and it is an estimate of the size of each
parameter at the minimum. If its value is NULL (the default value)
the typsizeis set within the nlgamlss function to typsize=abs(p0)
where p0 is the vector containing the starting values
of all the parameters to be maximized. p0 is defined within nlgamlss
stepmax
this argument is passed to nlm and it is a positive scalar which
gives the maximum allowable scaled step length.
stepmax is used to prevent steps which would cause the optimization
function to overflow, to prevent the algorithm from leaving the area of interest in parameter
space, or to detect divergence in the algorithm. stepmax would be chosen small enough
to prevent the first two of these occurrences, but should be larger than any anticipated
reasonable step. If its value is NULL (the default value) it is defined within nlgamlss as
stepmax=sqrt(p0 %*% p0)
iterlim
a positive integer specifying the maximum number of
iterations to be performed before the program is terminated. The default is 100
ndigit
the number of significant digits in the log-likelihood function. The default is 10
steptol
A positive scalar providing the minimum allowable relative
step length. The defaults is 1e-05
gradtol
a positive scalar giving the tolerance at which the scaled
gradient is considered close enough to zero to terminate the
algorithm. The scaled gradient is a measure of the relative
change in log-likelihood in each direction 'p[i]' divided by the
relative change in 'p[i]'. The default is 1e-05
print.level
this argument determines the level of printing which is
done during the minimization process. The default value of
'0' means that no printing occurs, a value of '1' means that
initial and final details are printed and a value of 2 means
that full tracing information is printed.
check.analyticals
a logical scalar specifying whether the analytic
gradients and Hessians, if they are supplied, should be
checked against numerical derivatives at the initial
parameter values. This can help detect incorrectly formulated
gradients or Hessians.
hessian
if TRUE, the hessian of the log likelihood at the maximum is returned ,the default is hessian=TRUE
Details
See the R function nlm and the fist two refernces below for details of the algotithm.
Dennis, J. E. and Schnabel, R. B. (1983) Numerical Methods for
Unconstrained Optimization and Nonlinear Equations.
Prentice-Hall, Englewood Cliffs, NJ.
Schnabel, R. B., Koontz, J. E. and Weiss, B. E. (1985) A modular
system of algorithms for unconstrained minimization. ACM Trans.
Math. Software, 11, 419-440.
Rigby, R. A. and Stasinopoulos D. M. (2005). Generalized additive models for location, scale and shape,(with discussion),
Appl. Statist., 54, part 3, pp 507-554.
Stasinopoulos D. M., Rigby R.A. and Akantziliotou C. (2003) Instructions on how to use the GAMLSS package in R.
Accompanying documentation in the current GAMLSS help files, (see also http://www.gamlss.com/).