hzar.chain.doSeq: Repeatedly run the optimizer in series to tune the optimizer.
In hzar: Hybrid Zone Analysis using R

Description Usage Arguments Details Value Author(s) See Also Examples

hzar.chain.doSeq cyclically calls hzar.doFit and hzar.next.fitRequest in order to optimize the covariance matrix driving mcmc process, MCMCmetrop1R.

hzar.chain.doSeq(hzar.request,
                 count = 3,
                 collapse = FALSE,
                 announce.complete = "Chain Complete")

`hzar.request`	The `hzar.fitRequest` object to use initially.
`count`	How many iterations to perform.
`collapse`	If TRUE, if all iterations succeed return a single `hzar.fitRequest` object with all of the results concatenated instead of a list of `hzar.fitRequest` objects.
`announce.complete`	Diagnostics message to print when the chain has completed.

For each iteration, hzar.doFit is called using hzar.request and the result is added to the results list.

For the second and all subsequent iterations, hzar.request is generated by hzar.next.fitRequest using the results of the previous iteration. If hzar.next.fitRequest fails, the results list is returned.

When count iterations are performed and if collapse is TRUE, the results list is reduced to a single hzar.fitRequest object with the covariance matrix set to the last matrix used. All other fields are set as if the concatenated results were the results of a single call to hzar.doFit.

Either a list of all successful hzar.fitRequest objects, or, if collapse is TRUE, a single hzar.fitRequest object with all of the results concatenated.

Graham Derryberry asterion@alum.mit.edu

hzar.fitRequest hzar.doFit MCMCmetrop1R hzar.next.fitRequest

data(manakinMolecular);
mknAdaA <-
  hzar.doMolecularData1DPops(manakinMolecular$distance,
                             manakinMolecular$ada.A,
                             manakinMolecular$ada.nSamples);
mknAdaAmodel <-
  hzar.makeCline1DFreq(mknAdaA, scaling="fixed",tails="none");
mknAdaAmodel <-
  hzar.model.addBoxReq(mknAdaAmodel, -30, 600)
mknAdaAmodelFit <-
  hzar.first.fitRequest.old.ML(model=mknAdaAmodel ,
                               mknAdaA,
                               verbose=FALSE);
mknAdaAmodelFit$mcmcParam$chainLength <- 1e3;
mknAdaAmodelFit$mcmcParam$burnin <- 50;
mknAdaAmodelFit$mcmcParam$thin <- 10;
str(hzar.chain.doSeq(mknAdaAmodelFit, count=2));