SWO_SA_prime_v1.1.R

##><>><>><>><>><>><>><>><>><>><>><>><>><>><>><>><>><>><>><>><>><>><>><>><>><>><><><
## JABBA: Just Another Bayesian Biomass Assessment
## Input File for JABBA
## Developed by Henning Winker & Felipe Carvalho (Cape Town/Hawaii)  
##><>><>><>><>><>><>><>><>><>><>><>><>><>><>><>><>><>><>><>><>><>><>><>><>><>><>

# required packages
library(gplots)
library(coda)
library(rjags)
library(R2jags)
library("fitdistrplus")
library(reshape)

#----------------------------------------------------------------
# Setup working directories and output folder labels 
#-----------------------------------------------------------------
# Set Working directory file, where assessments are stored 
File = "C:/Work/Research/GitHub/JABBA_testruns"
# Set working directory for JABBA R source code
JABBA.file = "C:/Work/Research/GitHub/JABBAbeta"
# JABBA version
version = "v1.1beta"
# Set Assessment file: assement folder within File that includes .csv input files
assessment = "SWO_SA" 
# add specifier for assessment (File names of outputs)


#><>><>><>><>><>><>><>><>><>><>><>><>><>><>><>><>><>><>><>><>><>><>
# Graphic, Output, Saving (.RData) settings 
#><>><>><>><>><>><>><>><>><>><>><>><>><>><>><>><>><>><>><>><>><>><>
KOBE.plot = TRUE # Produces JABBA Kobe plot 
KOBE.type = c("ICCAT","IOTC")[2] # ICCAT uses 3 colors; IOTC 4 (incl. orange) 
Biplot= TRUE # Produces a "post-modern" biplot with buffer and target zones (Quinn & Collie 2005)
SP.plot = c("standard","phase")[2] # Produces standard or 'Kobe phase' SP plot  
save.trajectories =TRUE # saves posteriors of P=B/K, B/Bmsy and H/Hmsy as .RData object 
harvest.label = c("Hmsy","Fmsy")[2] # choose label preference H/Hmsy versus Fmsy
CPUE.plot= TRUE # Runs state-tool to produce "alligned" multi-CPUE plot  
meanCPUE = FALSE # Uses averaged CPUE from state-space tool instead of individual indices  
Projection = TRUE # Use Projections: requires to define TACs vectors 
save.projections = TRUE # saves projection posteriors as .RData object 
catch.metric = "(t)" # Define catch input metric e.g. (tons) "000 t" etc 
Reproduce.seed = FALSE # If FALSE a random seed assigned to each run, if TRUE set.seed(123)
# Save entire posterior as .RData object
save.all = FALSE # (if TRUE, a very large R object of entire posterior is saved)  
#><>><>><>><>><>><>><>><>><>><>><>><>><>><>><>><>><>><>><>><>><>><>


#><>><>><>><>><>><>><>><>><>><>><>><>><>><>><>><>><>><>
# Optional: Note Scenarios
#><>><>><>><>><>><>><>><>><>><>><>><>><>><>><>><>><>><>
# S1: Model including Brazil1 
# S2: Model excluding Brazil1
# S3: Base-case Model with time blocks on ESP and JPN 
# S4: Add scenario as example for using average CPUE
#><>><>><>><>><>><>><>><>><>><>><>><>><>><>><>><>><>><>
# Specify Scenario name for output file names
Scenarios = c(paste0("Scenario",1:4)) 

# Execute multiple JABBA runs in loop 
for(s in 1:4){
  Scenario = Scenarios[s] 
  
  #><>><>><>><>><>><>><>><>><>><>><>><>><>><>><>><>><>><>><>><>><>><>
  # Suplus Production model specifications
  #><>><>><>><>><>><>><>><>><>><>><>><>><>><>><>><>><>><>><>><>><>><>
  
  # Choose model type: 
  # 1: Schaefer
  # 2: Fox  
  # 3: Pella-Tomlinsson  
  
  Model = c(3,3,3,3,3)[s] 
  Mod.names = c("Schaefer","Fox","Pella")[Model]
  
  # Depensation opiton:
  # Set Plim = Blim/K where recruitment may become impaired (e.g. Plim = 0.25) 
  # Choose Plim = 0 to reduce to conventional Schaefer, Fox, Pella models 
  Plim = 0
  
  # Required specification for Pella-Tomlinson (Model = 3)
  BmsyK = 0.4 # Set Surplus Production curve inflection point
  #><>><>><>><>><>><>><>><>><>><>><>><>><>><>><>><>><>><>><>><>><>><>  
  
  #--------------------------------------------------
  # Read csv files
  #--------------------------------------------------
  
  # Use SEs from csv file for abudance indices (TRUE/FALSE)
  SE.I = TRUE
  
  # Load assessment data
  catch = read.csv(paste0(File,"/",assessment,"/catch",assessment,".csv"))
  cpue = read.csv(paste0(File,"/",assessment,"/cpue",assessment,".csv"))#
  
  if(SE.I ==TRUE){
    se =  read.csv(paste0(File,"/",assessment,"/se",assessment,".csv"))
  }
  
  names(cpue)
  names(catch)
  
  #--------------------------------------------------
  # option to exclude CPUE time series or catch year
  #--------------------------------------------------
  
  # Set up Base-Case for SWO
  
  
  if(s<3){ # Combine SPA and JAPAN
    cpue[,4] = apply(cpue[,4:5],1,mean,na.rm=TRUE)
    cpue[,6] = apply(cpue[,6:7],1,mean,na.rm=TRUE)
    
    cpue = cpue[,-c(5,7)] 
    se[,4] = apply(se[,4:5],1,mean,na.rm=TRUE)
    se[,6] = apply(se[,6:7],1,mean,na.rm=TRUE)
    
    se = se[,-c(5,7)]
    cpue[!is.finite(cpue[,4]),4]=NA
    cpue[!is.finite(cpue[,5]),5]=NA
    se[!is.finite(se[,4]),4]=NA
    se[!is.finite(se[,5]),5]=NA
    
  }
  
  # Remove BrazilI
  if(s>1){
    cpue = cpue[,-c(2)]
    se = se[,-c(2)]
  }
  
  
  names(cpue)
  ncol(catch)
  ncol(cpue)
  
  #------------------------------------------------------
  # Option use mean CPUE from state-space cpue averaging
  #-----------------------------------------------------
  meanCPUE = FALSE
  if(s==4) meanCPUE = TRUE
  
  #------------------------------------------------
  # Prior for unfished biomass K
  #------------------------------------------------
  # The option are: 
  # a) Specify as a lognormal prior with mean and CV 
  # b) Specify as range to be converted into lognormal prior
  
  K.dist = c("lnorm","range")[1]
  
  # if lnorm use mean and CV; if range use lower,upper bound
  K.prior = c(200000,1) 
  
  #-----------------------------------------------------------
  # mean and CV and sd for Initial depletion level P1= SB/SB0
  #-----------------------------------------------------------
  # Set the initial depletion prior B1/K 
  # To be converted into a lognormal prior (with upper bound at 1.1)
  
  psi.dist= c("lnorm","beta")[1]
  # specify as mean and CV 
  psi.prior = c(1,0.25) 
  
  #--------------------------------------------------------------
  # Determine estimation for catchability q and observation error 
  #--------------------------------------------------------------
  # Assign q to CPUE
  sets.q = 1:(ncol(cpue)-1) 
  
  
  #----------------------------------------------------
  # Determine r prior
  #----------------------------------------------------
  # The option are: 
  # a) Specifying a lognormal prior 
  # b) Specifying a resiliance category after Froese et al. (2017; CMSY)
  # Resilience: "Very low", "Low", "Medium", High" (requires r.range = TRUE)
  
  # use [1] lognormal(mean,stdev) or [2] range (min,max) or
  r.dist = c("lnorm","range")[1] 
  
  r.prior = c(0.42,0.37) 
  
  #><>><>><>><>><>><>><>><>><>><>><>><>><>><>><>><>><>><>><>><>>
  # Observation Error
  #><>><>><>><>><>><>><>><>><>><>><>><>><>><>><>><>><>><>><>><>>
  
  #To Estimate additional observation variance set sigma.add = TRUE
  sigma.est = TRUE
  
  # Series
  sets.var = 1:(ncol(cpue)-1) # estimate individual additional variace
  
  # As option for data-weighing
  # minimum fixed observation error for each variance set (optional choose 1 value for both)
  fixed.obsE = c(0.2) # Important if SE.I is not availble
  
  # Total observation error: TOE = sqrt(SE^2+sigma.est^2+fixed.obsE^2)
  
  #><>><>><>><>><>><>><>><>><>><>><>><>><>><>><>><>><>><>><>><>>
  # Process Error
  #><>><>><>><>><>><>><>><>><>><>><>><>><>><>><>><>><>><>><>><>>
  #Estimate set sigma.proc == True
  sigma.proc = TRUE
  # Determines if process error deviation are estimated for all years (TRUE)  
  # or only from the point the first abundance index becomes available (FALSE)
  proc.dev.all = FALSE 
  #------------------------------------------
  if(sigma.proc == TRUE){
    igamma = c(4,0.01) #specify inv-gamma parameters
    
    # Process error check
    gamma.check = 1/rgamma(1000,igamma[1],igamma[2])
    # check mean process error + CV
    mu.proc = sqrt(mean(gamma.check)); CV.proc = sd(sqrt(gamma.check))/mean(sqrt(gamma.check))
    
    # check CV
    round(c(mu.proc,CV.proc),3)
    quantile(sqrt(gamma.check),c(0.1,0.9))
  }else{
    sigma.proc = 0.07 #IF Fixed: typicallly 0.05-0.15 (see Ono et al. 2012)
  }
  #--------------------------------------------
  
  #><>><>><>><>><>><>><>><>><>><>><>><>><>><>><>><>><>><>><>><>>
  # Optional: Do TAC Projections
  #><>><>><>><>><>><>><>><>><>><>><>><>><>><>><>><>><>><>><>><>>
  Projection = TRUE # Switch on by Projection = TRUE 
  
  # Check final year catch 
  catch[nrow(catch),]
  
  # Set range for alternative TAC projections
  TACs = seq(10000,18000,1000) #example
  
  # Intermitted TAC to get to TAC implementation year
  #TACint = mean(catch[nrow(catch)-3,2]:catch[nrow(catch),2]) # avg last 3 years
  TACint = 10058 # Catch for 2016
  # Set year of first TAC implementation
  imp.yr = 2018
  # Set number of projections years
  pyrs = 10
  
  
  #><>><>><>><>><>><>><>><>><>><>><>><>><>><>><>><>><>><>><>><>><>
  # Execute model and produce output
  #><>><>><>><>><>><>><>><>><>><>><>><>><>><>><>><>><>><>><>><>><>
  
  # MCMC settings
  ni <- 30000 # Number of iterations
  nt <- 5 # Steps saved
  nb <- 5000 # Burn-in
  nc <- 2 # number of chains
  nsaved = (ni-nb)/nt*nc
  
  # Run model (BSPSPexe file must be in the same working directory)
  source(paste0(JABBA.file,"/JABBA",version,".R")) 
  
}# THE END