smoothSurvReg.control {smoothSurv}

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 \lambda 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.

Value

A list with the same elements as the input except dist.range and by.knots is returned.

Author(s)

Arnošt Komárek [e-mail]