nltm.control: Package options for nltm

In nltm: Non-Linear Transformation Models

Package options for nltm

Description

Sets default values for arguments related to calculation of the maximum profile likelihood estimator of the regression parameters, β_θ (and β_η, and β_c), and the baseline hazard S_0(t) (see nltm). Optimization is performed using the "L-BFGS-B" method by Byrd et. al. (1995). See optim.

Usage

nltm.control(fnscale=-1, maxit=1000, reltol, factr=1e7, pgtol=0,
             s0.tol=1e-5, bscale=5) 

Arguments

fnscale	An overall scaling to be applied to the profile likelihood function (profileLik) during optimization. If positive, turns the problem into a minimization problem. Optimization is performed on 'profileLik(par)/fnscale'. Default is -1.
maxit	The maximum number of iterations. Default is 1000.
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. Default is sqrt(.Machine\$double.eps), typically about 1e-8.
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 1e-8.
pgtol	Helps control the convergence of the "L-BFGS-B" method. It is a tolerance on the projected gradient in the current search direction. Default is 0.
s0.tol	Convergence tolerance of baseline hazard self-consistency equation. Default is 1e-5.
bscale	The maximum profile likelihood estimator is obtained by maximizing the profile likelihood over a region determined by the magnitude of the observed covariates. These constraints are imposed in order to avoid numerical problems in the calculation of the profile likelihood function. For a given regression parameter, corresponding to a covariate with observed values x, the upper bound is the bscale of the parameter divided by max(abs(x)) if max(abs(x))>1e-10, otherwise 1e-10. The lower bound is minus the upper bound. Different values of bscale are allowed for different parameters. If different values of bscale are provided, the vector needs to have the scale for the regression parameters of the long-term predictor, β_θ, followed by the scale for the short-term predictor, β_η, if available and the one for the cure parameter, β_c, last if a cure model. Default is 5.

Value

A list with the same elements as the input.

References

Byrd, R. H., Lu, P., Nocedal, J. and Zhu, C. (1995) A limited memory algorithm for bound constrained optimization. SIAM J. Scientific Computing, 16, 1190-1208.

See Also

optim, nltm.

nltm documentation built on April 11, 2022, 5:05 p.m.