smoothSurvReg.control {smoothSurv} | R Documentation |
This function checks and sets the fitting options for smoothSurvReg. Its arguments can be used instead of ... in a call to smoothSurvReg.
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)
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.
|
A list with the same elements as the input except dist.range
and by.knots
is returned.
Arnošt Komárek arnost.komarek[AT]mff.cuni.cz