coxinterval (version 1.0)

coxic.control: Control Cox model fit

Description

Set parameters controlling the model fit returned by coxic.

Usage

coxic.control(eps = 1e-07, iter.max = 50000, coef.typ = 1,
              coef.max = 10, sieve.const = 1, sieve.rate = 1/3)

Arguments

eps

maximum threshold for the relative change in the model fit required to meet the iterative estimation routine's stopping rule.

iter.max

maximum number of iterations to attempt. This ensures that coxic will eventually exit, even when there are convergence issues.

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 coef.typ arguments tune variance estimation via the curvature in the profile log-likelihood.

sieve.const

a constant factor that, in part, determines the sieve size. The factor can be made specific to the transition type with sieve.const a vector of length three. Indexing the states from zero, this vector's components correspond to

sieve.rate

a scalar in (1/8, 1/2) determining the rate at which the sieve increases with the sample size.

Value

• A list of the above arguments with their final values.

Details

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, 2014b).

References

Boruvka, A. and Cook, R. J. (2014b) Sieve estimation in a Markov illness-death process under dual censoring.

See Also

coxic

Examples

coxic(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 = coxic.control(eps = 1e-5, sieve.rate = 2/5))