coxme
fit as a single list.coxme.control(eps = 1e-08, toler.chol = .Machine$double.eps^0.75,
iter.max = 20, inner.iter = Quote(max(4, fit0$iter+1)),
sparse.calc = NULL,
optpar = list(method = "BFGS", control=list(reltol = 1e-5)))
optim
.
The default is to use one more iteration than the baseline coxph
model fit0
. The baseline model conoptim
routine.coxme
is to use the optim
routine to
find the best values for the variance parameters. For any given trial
value of the variance parameters, an inner loop maximizes the partial
likelihood to select the regression coefficients beta (fixed) and b
(random). Within this loop cholesky decomposition is used. It is
critical that the convergence criteria of inner loops be less than
outer ones, thus toler.chol < eps < reltol.coxme