fscRun: Run fastsimcoal
In strataG: Summaries and Population Structure Analyses of Genetic Data

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

Run a fastsimcoal simulation.

fscRun(
  p,
  num.sims = 1,
  dna.to.snp = FALSE,
  max.snps = 0,
  sfs.type = c("maf", "daf"),
  nonpar.boot = NULL,
  all.sites = TRUE,
  inf.sites = FALSE,
  no.arl.output = FALSE,
  num.loops = 20,
  min.num.loops = 20,
  brentol = 0.01,
  trees = FALSE,
  num.cores = 1,
  seed = NULL,
  quiet = TRUE,
  exec = "fsc26"
)

fscCleanup(label, folder = ".")

fscTutorial()

`p`	list of fastsimcoal input parameters and output produced by fscWrite.
`num.sims`	number of simulation replicates to run.
`dna.to.snp`	convert DNA sequences to numerical SNPs?
`max.snps`	maximum number of SNPs to retain.
`sfs.type`	type of site frequency spectrum to compute for each population sample: 'daf' = derived allele frequency (unfolded), 'maf' = minor allele frequency (folded).
`nonpar.boot`	number of bootstraps to perform on polymorphic sites to extract SFS.
`all.sites`	retain all sites? If `FALSE`, only polymorphic DNA sites will be returned. This includes SNP blocks as they are simulated as DNA sequences.
`inf.sites`	use infinite sites model? If `TRUE`, all mutations are retained in the output, thus the number of sites for SNPs or DNA sequences will potentially be greater than what was requested.
`no.arl.output`	do not output arlequin files.
`num.loops`	number of loops (ECM cycles) to be performed when estimating parameters from SFS. Default is 20.
`min.num.loops`	number of loops (ECM cycles) for which the likelihood is computed on both monomorphic and polymorphic sites. Default is 20.
`brentol`	Tolerance level for Brent optimization. Smaller value imply more precise estimations, but require more computation time. Default = 0.01. Value is restricted between 1e-5 and 1e-1.
`trees`	output NEXUS formatted coalescent trees for all replicates?
`num.cores`	number of cores to use. If set to `NULL`, the value will be what is reported by `detectCores - 1`.
`seed`	random number seed for simulation.
`quiet`	logical indicating if fastsimcoal2 should be run in quiet mode.
`exec`	name of fastsimcoal executable.
`label`	character string of file run labels prefixes.
`folder`	character string giving the root working folder where input files and output resides

fscRun: Runs the fastsimcoal2 simulation and returns a list containing run parameters and a data frame used by fscRead to parse the genotypes generated (if Arlequin-formatted output was requested).
fscCleanup: Deletes all files associated with the simulation identified by label.

fastsimcoal2 is not included with 'strataG' and must be downloaded separately. Additionally, it must be installed such that it can be run from the command line in the current working directory. The function fscTutorial() will open a detailed tutorial on the interface in your web browser.

Eric Archer eric.archer@noaa.gov

Excoffier, L. and Foll, M (2011) fastsimcoal: a continuous-time coalescent simulator of genomic diversity under arbitrarily complex evolutionary scenarios Bioinformatics 27: 1332-1334.
Excoffier, L., Dupanloup, I., Huerta-Sánchez, E., Sousa, V.C., and M. Foll (2013) Robust demographic inference from genomic and SNP data. PLOS Genetics, 9(10):e1003905.
http://cmpg.unibe.ch/software/fastsimcoal2/

fsc.input, fscWrite, fscRead

## Not run: 
#' # three demes with optional names
demes <- fscSettingsDemes(
  Large = fscDeme(10000, 10), 
  Small = fscDeme(2500, 10),
  Medium = fscDeme(5000, 3, 1500)
)

# four historic events
events <- fscSettingsEvents(
  fscEvent(event.time = 2000, source = 1, sink = 2, prop.migrants = 0.05),
  fscEvent(2980, 1, 1, 0, 0.04),
  fscEvent(3000, 1, 0),
  fscEvent(15000, 0, 2, new.size = 3)
 )
 
# four genetic blocks of different types on three chromosomes.  
genetics <- fscSettingsGenetics(
  fscBlock_snp(10, 1e-6, chromosome = 1),
  fscBlock_dna(10, 1e-5, chromosome = 1),
  fscBlock_microsat(3, 1e-4, chromosome = 2),
  fscBlock_standard(5, 1e-3, chromosome = 3)
)

params <- fscWrite(demes = demes, events = events, genetics = genetics)

# runs 100 replicates, converting all DNA sequences to 0/1 SNPs
# will also output the MAF site frequency spectra (SFS) for all SNP loci.
params <- fscRun(params, num.sim = 100, dna.to.snp = TRUE, num.cores = 3)

## End(Not run)