This is used to set various numeric parameters controlling a Cox model fit.
Typically it would only be used in a call to coxph.
coxph.control(eps = 1e-09, toler.chol = .Machine$double.eps^0.75,
iter.max = 20, toler.inf = sqrt(eps), outer.max = 10, timefix=TRUE)Iteration continues until the relative change in the log partial likelihood is less than eps. Must be positive.
Tolerance for detection of singularity during a Cholesky decomposition of the variance matrix, i.e., for detecting a redundant predictor variable.
Maximum number of iterations to attempt for convergence.
Tolerance criteria for the warning message about a possible infinite coefficient value.
For a penalized coxph model, e.g. with pspline terms, there is an outer loop of iteration to determine the penalty parameters; maximum number of iterations for this outer loop.
Resolve any near ties in the time variables.
a list containing the values of each of the above constants
See the vignette "Roundoff error and tied times" for a more
detailed explanation of the timefix option. In short, when
time intervals are created via subtraction then two time intervals that are
actually identical can appear to be different due to floating point
round off error, which in turn can make coxph and
survfit results dependent
on things such as the order in which operations were done or the
particular computer that they were run on.
Such cases are unfortunatedly not rare in practice.
The timefix=TRUE option adds
logic similar to all.equal to ensure reliable results.
In analysis of simulated data sets, however, where often by defintion there
can be no duplicates, the option will often need to be set to
FALSE to avoid spurious merging of close numeric values.