build_jabba: Creates a data list used as input to JAGS
In jabbamodel/JABBA: Just Another Bayesian Biomass Assessment

build_jabba

R Documentation

Creates a data list used as input to JAGS

Description

Creates a data list with JABBA input and settings to be passed to fit_jabba()

Usage

build_jabba(
  catch = NULL,
  cpue = NULL,
  se = NULL,
  auxiliary = NULL,
  auxiliary.se = NULL,
  auxiliary.type = c("z", "effort", "bk", "bbmsy", "ffmsy")[1],
  assessment = "jabba",
  scenario = "jabba",
  model.type = c("Schaefer", "Fox", "Pella", "Pella_m"),
  add.catch.CV = TRUE,
  catch.cv = 0.1,
  catch.error = c("random", "under")[1],
  Plim = 0,
  r.dist = c("lnorm", "range"),
  r.prior = c(0.2, 0.5),
  K.dist = c("lnorm", "range"),
  K.prior = NULL,
  psi.dist = c("lnorm", "beta"),
  psi.prior = c(0.9, 0.25),
  b.prior = c(FALSE, 0.3, NA, c("bk", "bbmsy", "ffmsy")[1]),
  BmsyK = 0.4,
  shape.CV = 0.3,
  sets.q = 1:(ncol(cpue) - 1),
  sigma.est = TRUE,
  sets.var = 1:(ncol(cpue) - 1),
  fixed.obsE = ifelse(is.null(se), 0.1, 0.001),
  auxiliary.obsE = ifelse(is.null(auxiliary.se), 0.1, 0.001),
  auxiliary.sigma = TRUE,
  auxiliary.lag = 1,
  qA.cv = 0.1,
  sets.varA = 1:(ncol(auxiliary) - 1),
  sigma.proc = TRUE,
  proc.dev.all = TRUE,
  igamma = c(4, 0.01),
  projection = FALSE,
  TACs = NULL,
  TACint = NULL,
  imp.yr = NULL,
  pyrs = NULL,
  P_bound = c(0.02, 1.3),
  sigmaobs_bound = 1,
  sigmaproc_bound = 0.2,
  q_bounds = c(10^-30, 1000),
  K_bounds = c(0.01, 10^10),
  qA_bounds = c(10^-30, 1000),
  harvest.label = c("Hmsy", "Fmsy")[2],
  catch.metric = "(t)",
  verbose = TRUE
)

Arguments

`catch`	catch time series, requires data.frame(year, catch)
`cpue`	cpue time series, requires data.frame(year, cpue.1,cpue.2,...,cpue.N)
`se`	optional log standard error (CV) time series,requires data.frame(year, se.1,se.2,...,se.N)
`auxiliary`	additional time series of either c(Z,Effort,B/B0,B/Bmsy,F/Fmsy)
`auxiliary.type`	c("effort","z","bk","bbmsy","ffmsy")
`assessment`	= "example",
`scenario`	= "s1",
`model.type`	= c("Schaefer","Fox","Pella","Pella_m"),
`add.catch.CV`	= c(TRUE,FALSE) option estimate catch with error
`catch.cv`	catch error on log-scale (default = 0.1)
`catch.error`	can be random or directional under reporting "under"
`Plim`	= 0, # Set Plim = Blim/K where recruitment may become impaired (e.g. Plim = 0.25) PRIORS
`r.dist`	= c("lnorm","range"), # prior distribution for the intrinsic rate population increas
`r.prior`	= c(0.2,0.5), # prior(mu, lod.sd) for intrinsic rate of population increase
`K.dist`	= c("lnorm","range"), # prior distribution for unfished biomass K = B0
`K.prior`	= NULL, # prior(mu,CV) for the unfished biomass K = B0
`psi.dist`	= c("lnorm","beta"), # prior distribution for the initial biomass depletion B[1]/K
`psi.prior`	= c(0.9,0.25), # prior(mu, CV) for the initial biomass depletion B[1]/K
`b.prior`	= c(FALSE,0.3,NA,c("bk","bbmy","ffmsy")[1]), # depletion prior set as d.prior = c(mean,cv,yr,type=c("bk","bbmsy"))
`BmsyK`	= 0.4, # Inflection point of the surplus production curve, requires Pella-Tomlinson (model = 3 \| model 4)
`shape.CV`	= 0.3, # CV of the shape m parameters, if estimated with Pella-Tomlinson (Model 4) VARIANCE options
`sets.q`	= 1:(ncol(cpue)-1), # assigns catchability q to different CPUE indices. Default is each index a seperate q
`sigma.est`	= TRUE, # Estimate additional observation variance
`sets.var`	= 1:(ncol(cpue)-1), # estimate individual additional variace
`fixed.obsE`	# Minimum fixed observation error
`auxiliary.obsE`	# Fixed observation error for auxiliary data
`auxiliary.sigma`	# TRUE/FALSE
`auxiliary.lag`	lag option in years (default 1) for effort, z and ffmsy
`qA.cv`	precision on lognormal prior for e.g. qA*Z (not applicable to effort)
`sigma.proc`	= TRUE, # TRUE: Estimate process error, else set to value
`proc.dev.all`	= TRUE, # TRUE: All year, year = starting year
`igamma`	= c(3,0.01), # prior for process error variance, default informative igamma ~ mean 0.07, CV 0.4
`projection`	= FALSE, # Switch on by Projection = TRUE
`TACs`	= NULL
`TACint`	= NULL, # default avg last 3 years
`imp.yr`	= NULL, # default last year plus ONE
`pyrs`	= NULL, # Set number of projections years
`P_bound`	= c(0.02,1.3), # Soft penalty bounds for b/k
`sigmaobs_bound`	= 1, # Adds an upper bound to the observation variance
`sigmaproc_bound`	= 0.2, # Adds an upper bound to the process variance
`qA_bounds`	Defines lower and upper bounds for q of auxiliary data type effort
`harvest.label`	= c("Hmsy","Fmsy")[2], # choose label preference H/Hmsy versus Fmsy
`catch.metric`	"(t)" # Define catch input metric e.g. (tons) "000 t"
`verbose`	option show cat comments
`auxiliary`, `se`	optional input standard errors on auxilary data
`q_bounds=`	c(10^-30,1000), # Defines lower and upper bounds for q
`K_bounds=`	c(0.01,10^10), # Defines lower and upper bounds for K