coxdual.control {coxinterval}  R Documentation 
Set parameters controlling the model fit returned by
coxdual
.
coxdual.control(eps = 1e07, iter.max = 50000, coef.typ = 1, coef.max = 10, sieve = TRUE, sieve.const = 1, sieve.rate = 1/3, risk.min = 1, data = FALSE)
eps 
threshold value for the norm used to measure convergence in the parameter estimates. 
iter.max 
maximum number of iterations to attempt. This ensures that

coef.typ 
a scalar or vector of typical (absolute) values for the regression coefficient. 
coef.max 
a scalar or vector of probable upper bounds for the regression
coefficient. This and the 
sieve 
a logical value indicating that the sieve rather than the
semiparametric maximum likelihood estimator should be fit to the
data. The default 
sieve.const 
a constant factor that, in part, determines the sieve size. The
factor can be made specific to the transition type with

sieve.rate 
a scalar in (1/8, 1/2) determining the rate at which the sieve increases with the sample size. 
risk.min 
a positive integer giving the minimum size of risk set for support points defining the sieve. 
data 
a logical value indicating that the object returned by

For a given sample size n, the resulting sieve has size
at most sieve.const*
n^sieve.rate
. Any reduction
in size from this value is applied to ensure that each subinterval in
the sieve's time partition captures at least one support point from
the semiparametric maximum likelihood estimator based on the subsample
with known progression status (Boruvka and Cook, 2014).
A list of the above arguments with their final values.
Boruvka, A. and Cook, R. J. (2014) Sieve estimation in a Markov illnessdeath process under dual censoring.
coxdual(Surv(start, stop, status) ~ cluster(id) + trans(from, to) + I(z * (to == 1)) + I(z * (from %in% 0 & to == 2)) + I(z * (from %in% c(NA, 1) & to == 2)), data = dualrc, control = coxdual.control(eps = 1e5, sieve.rate = 2/5))