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
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 const
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 en
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.
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