R: Package options for nltm

nltm.control {nltm}

R Documentation

Package options for nltm

Description

Sets default values for arguments related to calculation of the maximum profile likelihood estimator of the regression parameters, \beta_\theta (and \beta_\eta, and \beta_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, `\beta_\theta`, followed by the scale for the short-term predictor, `\beta_\eta`, if available and the one for the cure parameter, `\beta_c`, last if a cure model. Default is 5.

Value

A list with the same elements as the input.

References