Usage
calc_loglike_for_optim(params, BioGeoBEARS_run_object, phy, tip_condlikes_of_data_on_each_state, print_optim = TRUE, areas_list = areas_list, states_list = states_list, force_sparse = force_sparse, cluster_already_open = cluster_already_open, return_what = "loglike", calc_ancprobs = FALSE)
Arguments
params
A vector of parameters for optimization.
BioGeoBEARS_run_object
Object containing the run
parameters and the model.
tip_condlikes_of_data_on_each_state
A numeric
matrix with rows representing tips, and columns
representing states/geographic ranges. The cells give
the likelihood of the observation data under the
assumption that the tip has that state; typically this
means that the known geographic range gets a '1' and all
other states get a 0.
force_sparse
Should sparse matrix exponentiation
be used?
print_optim
If TRUE (default), print the
optimization steps as ML estimation progresses.
areas_list
A list of the desired area
names/abbreviations/letters (?).
states_list
A list of the possible
states/geographic ranges, in 0-based index form.
cluster_already_open
If the user wants to
distribute the matrix exponentiation calculations from
all the branches across a number of processors/nodes on a
cluster, specify the cluster here. E.g.
cluster_already_open =
makeCluster(rep("localhost",num_cores_to_use), type =
"SOCK"). Note: this will work on most platforms,
including Macs running R from command line, but will NOT
work on Macs running the R GUI R.app, because
parallel processing functions like MakeCluster
from e.g. library(parallel) for some reason crash
R.app. The program runs a check for R.app and will just
run on 1 node if found.
return_what
What should be returned to the user?
Options are "loglike" (the log-likelihood of the data
under the tree, model, and model parameters), "nodelikes"
(the scaled conditional likelihoods at the nodes),
"rootprobs" (the relative probability of the geographic
ranges/states at the root), or "all" (all of the above in
a list). Typically the user will only want to return
"loglike" while doing ML optimization, but then return
"all" once the ML parameter values have been found.
calc_ancprobs
Just use this function once, return
the anc probs of states.