coxme.control {coxme} | R Documentation |
Auxillary function which packages the optional parameters of a
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)), refine.df=4, refine.detail=FALSE)
eps |
convergence criteria for the partial likelihood |
toler.chol |
tolerance for the underlying Cholesky decomposition. This is used to detect singularity (redundant variables). |
iter.max |
maximum number of iterations for the final fit |
inner.iter |
number of iterations for the ‘inner loop’ fits, i.e. when the
partial likelihood is the objective function of |
sparse.calc |
choice of method 1 or 2 for a particular portion of the calculation. This can have an effect on run time for problems with thousands of random effects. |
optpar |
parameters passed forward to the |
refine.df |
the degrees of freedom for the t-distribution used to draw random samples for the refine.n option |
refine.detail |
this option is mostly for debugging. If TRUE
then an extra component |
The main flow of 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.
a list of control parameters
Terry Therneau