Optimize an R function using phast's numerical optimization procedure
optim.rphast(func, params, lower = NULL, upper = NULL, precision = "HIGH",
logfile = NULL, ...)A function to be maximized. The first argument of the function should be a numeric vector of the parameters to be optimized
A vector of initial values to send as the first argument of func.
A vector of the same length as the vector of parameters to be optimized, giving the lower bounds for each parameter. If NULL, set the lower bounds to -Inf for all parameters.
A vector of the same length as the vector of parameters to be optimized, giving the upper bounds for each parameter. If NULL, set the upper bounds to Inf for all parameters.
The "precision" to use for the optimization, which affects convergence criteria. Choices are "LOW", "MED", "HIGH", or "VERY_HIGH".
If non-NULL, give the name of a file to write an optimization log to
Additional arguments to be passed to func at each function call. These arguments will not be optimized.
A list with three elements: value: the optimized value of the function, par: a vector giving the parameters at the optimized value, and neval: the number of function evaluations used in the optimization.
This function works very much like the optim function in the stats package. In many phast applications, however, I have noticed that this function converges just as well while taking many fewer function evaluations. It uses the same optimization routine as phyloFit. In general it is most efficient to use phyloFit, because some efficiency is lost in passing objects back and forth from R to C (as is necessary when using C code to optimize an R function, whereas phyloFit uses C code to optimize a C function).