define_BioGeoBEARS_run: Define a maximum likelihood search, perhaps stratified
In BioGeoBEARS: BioGeography with Bayesian (and Likelihood) Evolutionary Analysis in R Scripts

Description Usage Arguments Value Note Author(s) References See Also Examples

Set up the inputs object for an ML search. See parameter descriptions for defaults.

  define_BioGeoBEARS_run(abbr = "default",
    description = "defaults",
    BioGeoBEARS_model_object = define_BioGeoBEARS_model_object(),
    trfn = "Psychotria_5.2.newick",
    geogfn = "Psychotria_geog.data", timesfn = NA,
    distsfn = NA, dispersal_multipliers_fn = NA,
    area_of_areas_fn = NA, areas_allowed_fn = NA,
    detects_fn = NA, controls_fn = NA, max_range_size = NA,
    states_list = NULL, force_sparse = FALSE,
    use_detection_model = FALSE, print_optim = TRUE,
    num_cores_to_use = NA, cluster_already_open = FALSE,
    use_optimx = TRUE, return_condlikes_table = FALSE,
    calc_TTL_loglike_from_condlikes_table = TRUE,
    calc_ancprobs = TRUE, fixnode = NULL, fixlikes = NULL,
    speedup = TRUE, tmpwd = getwd())

`abbr`	Text abbreviation of run, e.g. "default"
`description`	Text description of run, e.g. "defaults"
`BioGeoBEARS_model_object`	Default is `define_BioGeoBEARS_model_object()`
`trfn`	The filename of the phylogenetic tree, in NEWICK format (http://evolution.genetics.washington.edu/phylip/newicktree.html). Tipnames should match the names in geogfn. See `read.tree` in APE for reading in phylogenetic trees. Default "Psychotria_5.2.newick"
`geogfn`	A PHYLIP-style file with geographic range data (see `getranges_from_LagrangePHYLIP`) for each tipname. This is the same format used by C++ LAGRANGE (SmithRee2010_CPPversion). Default "Psychotria_geog.data"
`timesfn`	Filename for the stratified times.
`distsfn`	Filename for the changing distances.
`dispersal_multipliers_fn`	Filename for the changing hard-coded dispersal multipliers
`area_of_areas_fn`	Filename for the area of each area
`areas_allowed_fn`	Filename for the allowed connections between areas for single-species ranges.
`detects_fn`	Filename for the counts of detections of OTUs of interest. See `calc_obs_like`.
`controls_fn`	Filename for the counts of taphonomic controls (which INCLUDE the OTUs of interest). See `calc_obs_like`.
`max_range_size`	The maximum rangesize, in number of areas. Having a smaller maximum range size means that you can have more areas (the size of the state space is greatly reduced; see `numstates_from_numareas`.
`states_list`	A list of the possible states/geographic ranges, in 0-based index form.
`force_sparse`	Should sparse matrix exponentiation be used? Default `FALSE`, which means dense matrix exponentiation is always used. If `NA`, the program will use sparse matrix exponentiation for transition matrices above rank 128 (size 128x128). NOTE: Sparse matrix exponentiation seems to give correlated, but not exact, results, and these errors may accumulate. Presumably the problems become less with larger matrices, but I have not explored this in detail.
`use_detection_model`	If TRUE, use the detection model (with parameters mf, dp, and fdp) and counts of detections and counts of taphonomic controls to calculate the `tip_condlikes_of_data_on_each_state`.
`print_optim`	If TRUE (default), print the optimization steps as ML estimation progresses.
`tmpwd`	The working directory in which the input and output files will be placed. Default is `getwd`. This is stored mostly for future reference; users are responsible for manually navigating to the appropriate directory ahead of time, using `setwd`.
`num_cores_to_use`	If >1, parallel processing will be attempted. Note: parallel processing via `library (parallel)` will work in Mac command-line R, but not in Mac GUI `R.app`.
`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.
`use_optimx`	If TRUE, use `optimx` rather that `optim`.
`return_condlikes_table`	If `TRUE`, return the table of ALL conditional likelihood results, including at branch subsections (only some should be used in calculating the final log-likelihood of the geography range data on the tree!)
`calc_TTL_loglike_from_condlikes_table`	If TRUE, force making of the condlikes table, and use it to calculate the log-likelihood (default=TRUE; matches LAGRANGE).
`calc_ancprobs`	If `TRUE` (default), calculate and return the necessary pieces (uppass and downpass probs) for ancestral states.
`fixnode`	If the state at a particular node is going to be fixed (e.g. for ML marginal ancestral states), give the node number.
`fixlikes`	The state likelihoods to be used at the fixed node. I.e. 1 for the fixed state, and 0 for the others.
`speedup`	If `TRUE` (default), set the maximum number of iterations to `itnmax=50*(number of free parameters)`, instead of the `optimx` default, 250. Also set `optimx` `reltol` parameter to 0.001 (instead of the default, ~1e-8).

inputs Inputs for ML search.

Go BEARS!

Nicholas J. Matzke matzke@berkeley.edu

http://phylo.wikidot.com/matzke-2013-international-biogeography-society-poster

Matzke_2012_IBS

readfiles_BioGeoBEARS_run, define_BioGeoBEARS_model_object, setwd, getwd

test=1

Loading required package: rexpokit
Loading required package: SparseM

Attaching package: 'SparseM'

The following object is masked from 'package:base':

    backsolve

Loading required package: Rcpp
Loading required package: cladoRcpp
Loading required package: ape
Loading required package: phylobase

Attaching package: 'phylobase'

The following object is masked from 'package:ape':

    edges