coxdual.control: Control Cox model fit
In coxinterval: Cox-Type Models for Interval-Censored Data

Description Usage Arguments Details Value References See Also Examples

Set parameters controlling the model fit returned by coxdual.

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coxdual.control(eps = 1e-07, 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 `coxdual` will eventually exit, even when the convergence criteria are not met.
`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`	a logical value indicating that the sieve rather than the semiparametric maximum likelihood estimator should be fit to the data. The default `TRUE` is recommended to avoid issues with support finding and convergence.
`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 the state 0 to state 1, 0 to 2, and 1 to 2 transition types, respectively.
`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 `coxdual` should contain an element `data` that gives the known support points, corresponding size of the risk set, left and right endpoints of censoring intervals for the progression time, first and last observation times, likelihood contribution type (`0` progression status unknown, `1` positive status, `2` negative status), survival time observed, and type-specific covariates.

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 illness-death process under dual censoring.

coxdual

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 = 1e-5, sieve.rate = 2/5))