bam2r: BAM inputs to R object conversion function

In nikolaifish/bamExtras: Functions and Data in Support of Stock Assessments with the Beaufort Assessment Model (BAM)

BAM inputs to R object conversion function

Description

This script reads in BAM dat, tpl, and cxx files, and converts them to R objects (character vectors of each line of the code). It also builds an R list on the tpl init objects, from the tpl and dat files.

Usage

bam2r(
  CommonName = NULL,
  init = NULL,
  dat_file = NULL,
  tpl_file = NULL,
  cxx_file = NULL,
  dat_obj = NULL,
  tpl_obj = NULL,
  cxx_obj = NULL,
  object_labels = list(bounded_point = c("init", "lower", "upper", "phase", "prior_mean",
    "prior_var", "prior_pdf"), bounded_vector = c("lower", "upper", "phase")),
  is_bam = TRUE
)

bam_to_r(
  CommonName = NULL,
  init = NULL,
  dat_file = NULL,
  tpl_file = NULL,
  cxx_file = NULL,
  dat_obj = NULL,
  tpl_obj = NULL,
  cxx_obj = NULL,
  object_labels = list(bounded_point = c("init", "lower", "upper", "phase", "prior_mean",
    "prior_var", "prior_pdf"), bounded_vector = c("lower", "upper", "phase")),
  is_bam = TRUE
)

Arguments

CommonName	Common name of species modeled in BAM files. Only used when accessing dat, tpl, and cxx character vectors named as e.g. dat_CommonName
init	user supplied list of tpl "init" object names defined in tpl Data Section. If missing, the function builds one from values in the dat and tpl files.
dat_file	dat file path
tpl_file	tpl file path
cxx_file	cxx file path
dat_obj	dat file read in as a character vector with readLines(con=dat_file)
tpl_obj	tpl file read in as a character vector with readLines(con=tpl_file)
cxx_obj	cxx file read in as a character vector with readLines(con=cxx_file)
object_labels	list of labels for bounded points and vectors
is_bam	Are you using this function on an actual bam model? If FALSE, code that is very specific to bam will not be run. The function was designed to read bam model files into R, but in many cases it should work to read other dat and tpl files.

Examples

## Not run: 
# Read in any of the current BAM models
bam_AtMe <- bam2r("AtlanticMenhaden")
bam_BlSB <- bam2r("BlackSeaBass")
bam_BlTi <- bam2r("BluelineTilefish")
bam_Cobi <- bam2r("Cobia")
bam_GagG <- bam2r("GagGrouper")
bam_GrTr <- bam2r("GrayTriggerfish")
bam_GrAm <- bam2r("GreaterAmberjack")
bam_ReGr <- bam2r("RedGrouper")
bam_RePo <- bam2r("RedPorgy")
bam_ReSn <- bam2r("RedSnapper")
bam_ScGr <- bam2r("ScampGrouper")
bam_SnGr <- bam2r("SnowyGrouper")
bam_Tile <- bam2r("Tilefish")
bam_VeSn <- bam2r("VermilionSnapper")

# Run a bam model and assign rdat output to object
rdat_RePo <- run_bam(bam=bam_RePo,fileName="RePo")

# Modify data input from a BAM model and incorporate it back into the dat file object
init2 <- bam_RePo$init
init2$set_steep[c(1,5)] <- paste(0.6) # Change steepness
init2$set_steep[4] <- paste(-abs(as.numeric(init2$set_steep[4]))) # Fix steepness
bam_RePo2 <- bam2r("RedPorgy",init=init2)
rdat_RePo2 <- run_bam(bam=bam_RePo2,fileName="RePo2")

# Compare models
plot(rdat_RePo$t.series$year, rdat_RePo$t.series$SSB.msst,type="o", ylim=range(c(rdat_RePo$t.series$SSB.msst,rdat_RePo2$t.series$SSB.msst),na.rm=TRUE), xlab="year",ylab="SSB.msst")
points(rdat_RePo2$t.series$year,rdat_RePo2$t.series$SSB.msst,type="o",col="blue")
legend("topright",legend=c("RePo","RePo2"),pch=1,lty=1,col=c("black","blue"),bty="n")

## End(Not run)

nikolaifish/bamExtras documentation built on April 17, 2025, 9:44 p.m.