coxme.control {kinship}R Documentation

Control parameters for coxme

Description

Set various control parameters for the coxme function.

Usage

coxme.control(eps=0.00001, toler.chol=.Machine$double.eps^0.75, 
toler.ms=.01, inner.iter=4, 
iter.max=10, simplex=0, lower=0, upper=Inf, sparse.calc=NULL )

Arguments

eps convergence criteria for the inner Cox model computations. Iteration ceases when the relative change in the log-likelihood is less than eps.
toler.chol tolerance that is used to detect singularity, i.e., redundant predictor variables in the model, in the underlying Cholesky decompostion routines.
toler.ms convergence criteria for the minimization of the integrated loglikelihood over the variance parameters. Since this ``outer'' iteration uses the Cox iteration as an inner loop, and the Cox iteration in turn uses the cholesky decomposition as an inner look, each of these treating the computations below it as if they were exact, the cholesky tolerance should be tighter than the Cox tolerance, which in turn should be tighter than that for the variance estimates.
inner.iter the number of iterations for the inner iteration loop.
iter.max maximum number of iterations for solution of a Cox partial likelihood, given the values of the random effect variances. Calls with iter=0 are useful to evaluate the likelihood for a prespecified parameter vector, such as in the computation of a profile likelihood.
simplex number of iterations for the Nelder-Mead simplex algorithm. The simplex method is very good at finding the general neighborhood of a minimum without getting lost, but can take a very large number of iterations to narrow in on the final answer; opposite strentghs to the standard minimizer optim. For hard problems, adding 50-100 iterations of the simplex as a starting estimate for the usual method can be very helpful.
lower, upper limits for the variance parameters, used by optim.
sparse.calc style of computation for the inner likelihood code. The results of the two computations are identical, but can differ in total compute time. The optional calculation (calc=1) uses somewhat more memory, but can be substantially faster when the total number of random effects is of order n, the total sample size. The standard calculation (calc=0) is faster when the number of random effects is small. By default, the coxme.fit function chooses the method dynamically. It may not always do so optimally.

Details

The central computation consists of an outer maximization to determine the variances of the random effects, performed by the optim function. Each evaluation for optim, however, itself requires the solution of a minimization problem; this is the inner loop. It is important that the inner loop use a fixed number of iterations, but it is not yet clear what is the minimal sufficient number for that inner loop. Making this number smaller will make the routine faster, but perhaps at the expense of accuracy.

Value

a list containing values for each option.

References

Therneau and Pankratz

See Also

coxme


[Package kinship version 1.1.0-21 Index]