Description Usage Arguments Details Author(s) References
A function for assessing whether models optimized well, identifying bad fits, and rerunning them if needed.
1 2 3 
misse.list 
a 
n.tries 
maximum number of retries for a given model. 
remove.bad 
a logical indicating whether models identified as poorly optimized (even after attempting to be refit) should be removed from 
dont.rerun 
a logical indicating whether models identified as poorly optimized should be run. The default is 
save.file 
file to use to save the full model fits before removing the poorly optimized ones. 
n.cores 
how many cores to run this on in parallel. 
sann 
a logical indicating whether a twostep optimization
procedure is to be used. The first includes a simulate annealing
approach, with the second involving a refinement using

sann.its 
a numeric indicating the number of times the simulated annealing algorithm should call the objective function. 
sann.temp 
the starting temperature for the simulated annealing. Higher temperatures results in the chain sampling a much wider space initially. The default of 5320 is based on the default of the GenSA package. For larger trees setting this value higher in conjunction with more sann.its can drastically improve performance. 
bounded.search 
a logical indicating whether or not bounds should
be enforced during optimization. The default is 
starting.vals 
a numeric vector of length 3 with starting values for the model. Position [1] sets turnover, [2] sets extinction fraction, and [3] transition rates between distinct diversification rates. 
turnover.upper 
sets the upper bound for the turnover parameters. 
eps.upper 
sets the upper bound for the eps parameters. 
trans.upper 
sets the upper bound for the transition rate parameters. 
restart.obj 
an object of class that contains everything to restart an optimization. 
This function is used to triage poorly optimized models after a MiSSEGreedy
run.
It is normally invoked within MiSSEGreedy
, but it can also be used as a standalone function,
to simply identify poorly identify models and/or rerun them.
Jeremy M. Beaulieu
Vascancelos, T, B.C. O'Meara, and J.M. Beaulieu. In prep.
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