smoothSurvReg.control: More Options for 'smoothSurvReg'
In smoothSurv: Survival Regression with Smoothed Error Distribution

smoothSurvReg.control

R Documentation

More Options for 'smoothSurvReg'

Description

This function checks and sets the fitting options for smoothSurvReg. Its arguments can be used instead of ... in a call to smoothSurvReg.

Usage

smoothSurvReg.control(est.c = TRUE, est.scale = TRUE,
   maxiter = 200, firstiter = 0, rel.tolerance = 5e-5,
   toler.chol = 1e-15, toler.eigen = 1e-3,
   maxhalf = 10, debug = 0, info = TRUE, lambda.use = 1.0, sdspline = NULL,
   difforder = 3, dist.range = c(-6, 6), by.knots = 0.3,
   knots = NULL, nsplines = NULL, last.three = NULL)

Arguments

`est.c`	If TRUE the G-spline coefficients are estimated. Otherwise, they are fixed to the values given by `init.c` parameter of `smoothSurvReg`.
`est.scale`	If TRUE the scale parameter sigma is estimated. Otherwise, it is fixed to the value given by `init.scale` parameter of `smoothSurvReg`.
`maxiter`	Maximum number of Newton-Raphson iterations.
`firstiter`	The index of the first iteration. This option comes from older versions of this function.
`rel.tolerance`	(Relative) tolerance to declare the convergence. In this version of the function, the convergence is declared if the relative difference between two consecutive values of the penalized log-likelihood are smaller than rel.tolerance.
`toler.chol`	Tolerance to declare Cholesky decomposition singular.
`toler.eigen`	Tolerance to declare an eigen value of a matrix to be zero.
`maxhalf`	Maximum number of step-halving steps if updated estimate leads to a decrease of the objective function.
`debug`	If non-zero print debugging information.
`info`	If TRUE information concerning the iteration process is printed during the computation to the standard output.
`lambda.use`	The value of the tuning (penalty) parameter λ used in a current fit by the `smoothSurvReg.fit` function. Value of this option is not interesting for the user. The parameter `lambda` of the function `smoothSurvReg` is more important for the user.
`sdspline`	Standard deviation of the basis G-spline. If not given it is determined as 2/3 times the maximal distance between the two knots. If `est.c` = TRUE and `sdspline` >= 1 it is changed to 0.9 to be able to satisfy the constraints imposed to the fitted error distribution.
`difforder`	The order of the finite difference used in the penalty term.
`dist.range`	Approximate minimal and maximal knot. If not given by `knots` the knots are determined as `c(seq(0, dist.range[2], by = by.knots), seq(0, dist.range[1], by = -by.knots))`. The sequence of knots is sorted and multiple entries are removed.
`by.knots`	The distance between the two knots used when building a vector of knots if these are not given by `knots`. This option is ignored if `nsplines` is not `NULL`.
`knots`	A vector of knots.
`nsplines`	This option is ignored at this moment. It is used to give the number of G-splines to the function `smoothSurvReg.fit`.
`last.three`	A vector of length 3 with indeces of reference knots. The 'a' coefficient of the `knot[last.three[1]]` is then equal to zero, 'a' coefficients with indeces `last.three[2:3]` are expressed as a function of remaining 'a' coefficients such that resulting error distribution has zero mean and unit variance. If `maxiter > 0` `last.three` is determined after the convergence is reached. If `maxiter == 0` `last.three` is used to compute variance matrices.