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JABBAv1.5beta.R
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JABBAv1.5beta.R
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##><>><>><>><>><>><>><>><>><>><>><>><>><>><>><>><>><>><>><>><>><>><>><>><>><>><><><
## Stock Assessment execution File for JABBA
## Developed by Henning Winker & Felipe Carvalho (Cape Town/Hawaii)
##><>><>><>><>><>><>><>><>><>><>><>><>><>><>><>><>><>><>><>><>><>><>><>><>><>><><><
cat(paste0("\n","><>><>><>><>><>><>><>><>"))
cat(paste0("\n","><> Run Model ",Mod.names,"<><"))
cat(paste0("\n","><>><>><>><>><>><>><>><>","\n","\n"))
# setwd(paste(File)
dir.create(paste0(File,"/",assessment),showWarnings = FALSE)
dir.create(paste0(File,"/",assessment,"/",Scenario,"_",Mod.names),showWarnings = FALSE)
dir.create(paste0(File,"/",assessment,"/",Scenario,"_",Mod.names,"/Input"),showWarnings = FALSE)
input.dir = paste0(File,"/",assessment,"/",Scenario,"_",Mod.names,"/Input")
#><>><>><>><>><>><>><>><>><>><>><>><>><>><>><>><>><>><>><>><>><>><>><>><>
# Define objects to make sure they exist if not included in Prime file
#><>><>><>><>><>><>><>><>><>><>><>><>><>><>><>><>><>><>><>><>><>><>><>><>
if(exists("igamma")==FALSE) igamma = c(4,0.01) # Generic process error prior
if(exists("BmsyK")==FALSE) BmsyK = 0.4 # JABBA default for Pella model
if(exists("Model")==FALSE){ Model = 1; Mod.names = c("Schaefer")} # Run Schaefer if model is not specified
if(exists("proc.dev.all")==FALSE) proc.dev.all = FALSE # process error deviation if only catch is available
if(exists("Plim")==FALSE) Plim = 0 # Standard non-compound model
if(exists("KOBE.plot")==FALSE) KOBE.plot = TRUE # Produces JABBA Kobe plot
if(exists("KOBE.type")==FALSE) KOBE.type = c("ICCAT","IOTC")[2] # ICCAT uses 3 colors; IOTC 4 (incl. orange)
if(exists("Biplot")==FALSE) Biplot= TRUE # Produces a "post-modern" biplot with buffer and target zones (Quinn & Collie 2005)
if(exists("save.trajectories")==FALSE) save.trajectories =FALSE # saves posteriors of P=B/K, B/Bmsy and H/Hmsy as .RData object
if(exists("harvest.label")==FALSE) harvest.label = c("Hmsy","Fmsy")[2] # choose label preference H/Hmsy versus Fmsy
if(exists("CPUE.plot")==FALSE) CPUE.plot= TRUE # Runs state-tool to produce "alligned" multi-CPUE plot
if(exists("catch.metric")==FALSE) catch.metric = "(t)" # Runs state-tool to produce "alligned" multi-CPUE plot
if(exists("meanCPUE")==FALSE) meanCPUE = FALSE # Uses averaged CPUE from state-space tool instead of individual indices
if(exists("Projection")==FALSE) Projection = FALSE # Use Projections: requires to define TACs vectors
if(exists("save.projections")==FALSE) save.projections = FALSE# saves projection posteriors as .RData object
if(exists("Reproduce.seed")==FALSE) Reproduce.seed = FALSE # If FALSE a random seed assigned to each run (default)
if(exists("TACint")==FALSE) TACint = mean(catch[nrow(catch)-3,2]:catch[nrow(catch),2]) # use mean catch from last years
if(exists("imp.yr")==FALSE) imp.yr = as.numeric(format(Sys.Date(), "%Y"))+1 # use next year from now
if(exists("init.values")==FALSE) init.values =FALSE # Allows to add manual starting values for K, r, q
if(exists("sigmaobs_bound")==FALSE) sigmaobs_bound = 1 # Adds an upper bound to the observation variance
if(exists("sigmaproc_bound")==FALSE) sigmaproc_bound = 0.2 # Adds an upper bound to the process variance
if(exists("P_bound")==FALSE) P_bound = c(0.03,1.) # Soft penalty bounds for P
if(exists("q_bounds")==FALSE) q_bounds= c(10^-30,1000) # Defines lower and upper bounds for q
if(exists("K_bounds")==FALSE) K_bounds= c(0.01,10^10) # Defines lower and upper bounds for q
if(exists("add.catch.CV")==FALSE) add.catch.CV =FALSE # Use estimation as a routine (increases stability)
if(exists("catch.cv")==FALSE) catch.cv = 0.05 # Use estimation as a routine (increases stability)
if(exists("CatchOnly")==FALSE) CatchOnly = FALSE # Use estimation as a routine (increases stability)
if(exists("jabba2FRL")==FALSE) jabba2FRL = FALSE
# Save entire posterior as .RData object
if(exists("save.all")==FALSE) save.all = FALSE #
#><>><>><>><>><>><>><>><>><>><>><>><>><>><>><>><>><>><>><>><>><>><>><>
#-------------------------
# Prepare input data
#-------------------------
cat(paste0("\n","><> Prepare input data <><","\n","\n"))
indices = names(cpue)[2:ncol(cpue)]
n.indices = max(length(indices),1)
catches = names(catch)[2:ncol(catch)]
n.catches = length(catches)
years=catch[,1]
styr = min(years)
endyr = max(years)
n.years = length(years)
styr.catch = min(catch[,1])
styr.C = styr.catch-styr+1
conv.catch = as.numeric(rbind(matrix(rep(NA,(styr.C-1)*n.catches),styr.C-1,n.catches),as.matrix(catch[,-1])))
Catch=matrix(conv.catch,nrow=n.years,ncol=n.catches)
Catch[Catch<0.000001] = 0.000001 # Replace any NA or zero by small constant
if(CatchOnly==FALSE){
styr.cpue = min(cpue[,1])
styr.I = styr.cpue-styr+1
# Convert input data to matrices for JAGS input
conv.cpue = as.numeric(rbind(matrix(rep(NA,(styr.I-1)*n.indices),styr.I-1,n.indices),as.matrix(cpue[,-1])))
CPUE=matrix(conv.cpue,nrow=n.years,ncol=n.indices)
}else{
CPUE.plot =FALSE
CPUE = Catch
CPUE[,1] = NA
CPUE[1,1] = 1
cpue = data.frame(Year=years,CatchOnly=CPUE[,1])
SE.I = FALSE
add.catch.CV =FALSE
sigma.est = FALSE
sets.q =1
sets.var=1
n.indices = 1
catches = "CatchOnly"
n.catches = length(catches)
styr.I=styr.C
}
if(SE.I==FALSE){
se = cpue
conv.se = as.numeric(rbind(matrix(rep(NA,(styr.I-1)*n.indices),styr.I-1,n.indices),as.matrix(cpue[,-1])))
se2 = matrix(ifelse(fixed.obsE>0,fixed.obsE^2,10^-10),n.years,n.indices)#/2
} else{
conv.se = as.numeric(rbind(matrix(rep(NA,(styr.I-1)*n.indices),styr.I-1,n.indices),as.matrix(se[,-1])))
#conv.se = sqrt(conv.se^2+fixed.obsE^2)
se2 = matrix(ifelse(is.na(conv.se),0.3^2,conv.se)^2,n.years,n.indices)+fixed.obsE^2#/2
}
# Total Catch
TC = apply(Catch,1,sum)
# Catch CV option.
if(add.catch.CV==TRUE){
if(length(catch.cv)>1) {CV.C =catch.cv[,2]} else {CV.C = rep(catch.cv,length(TC))}
cat(paste0("\n","><> Run with Catch Estimation CV <><","\n","\n"))
} else {
cat(paste0("\n","><> Run with Catch assumed known <><","\n","\n"))
}
# hindcast option
if(exists("tails")==FALSE) tails = max(years)
if(tails<max(years)){
cpue.raw = read.csv(paste0(File,"/",assessment,"/cpue",assessment,".csv"))
cpue.raw = cpue.raw[which(cpue.raw[,1]%in%years),which(colnames(cpue.raw)%in%colnames(cpue))]
} else {
cpue.raw = cpue
}
#------------
# Plot Catch
#------------
cat(paste0("\n","><> Plot Catch in Input subfolder <><","\n","\n"))
Par = list(mfrow=c(1,1),mar = c(5, 5, 1, 1), mgp =c(3,1,0), tck = -0.02,cex=0.8)
png(file = paste0(input.dir,"/Catches_",assessment,".png"), width = 7, height = 5,
res = 200, units = "in")
par(Par)
plot(catch[,1],catch[,1],ylim=c(0,max(catch[,2:ncol(catch)],na.rm=TRUE)),ylab=paste0("Catch ",catch.metric),xlab="Year",type="n")
for(i in 2:ncol(catch)) lines(catch[,1],catch[,i],lty=(i-1),lwd=2)
legend("topright",paste(names(catch)[2:ncol(catch)]),lty=1:(ncol(catch)-1),bty="n")
dev.off()
#---------------------
# Index color palette
#---------------------
jabba.colors = as.character(c('#e6194b', "#3cb44b", "#ffe119",
"#0082c8","#f58231", "#911eb4",
"#46f0f0", "#f032e6", "#d2f53c",
"#fabebe", "#008080","#e6beff", "#aa6e28",rainbow(10)))
#--------------------
# Set seed
#--------------------
if(Reproduce.seed==FALSE){
get_seed = ceiling(runif(1,min=0,max=1e6)) } else {get_seed = 123}
set.seed(get_seed)
#---------------------------------------------------------------------------
# CPUE run State-Space model for averaging CPUE
#---------------------------------------------------------------------------
if(CPUE.plot==TRUE){
cat(paste0("\n","><> Run State-Space CPUE averaging tool","\n"))
#find first time-series with first CPUE
q1.y = c(1:n.years)[is.na(apply(CPUE,1,mean,na.rm=TRUE))==FALSE][1] #first year with CPUE
q1.I = which.max(CPUE[q1.y,])
qs = c(q1.I,c(1:(ncol(cpue)-1))[-q1.I])
sink("cpueAVG.jags")
cat("
model {
# Prior specifications
eps <- 0.0000000000001 # small constant
iq[1] ~ dgamma(1000,1000)
q[1] <- pow(iq[1],-1)
logq[1] <- log(1)
for(i in 2:nI){
iq[i] ~ dgamma(0.001,0.001)
q[i] <- pow(iq[i],-1)
logq[i] <- log(q[i])
}
")
if(sigma.proc==TRUE){
cat("
# Process variance
isigma2 <- isigma2.est
sigma2 <- pow(isigma2,-1)
sigma <- sqrt(sigma2)
fakesigma.fixed <- sigma.fixed # Prevent unused variable error msg
",append=TRUE)
}else{ cat("
isigma2 <- pow(sigma.fixed+eps,-2)
sigma2 <- pow(isigma2,-1)
sigma <- sqrt(sigma2)
",append=TRUE)}
if(sigma.est==TRUE){
cat("
# Obsevation variance
# Observation error
itau2~ dgamma(0.001,0.001)
tau2 <- 1/itau2
for(i in 1:nI)
{
for(t in 1:N)
{
var.obs[t,i] <- SE2[t,i]+tau2
ivar.obs[t,i] <- 1/var.obs[t,i]
# note total observation error (TOE)
TOE[t,i] <- sqrt(var.obs[t,i])
}}
",append=TRUE)
}else{ cat("
# Obsevation variance
# Observation error
itau2~ dgamma(2,2)
tau2 <- 1/itau2
for(i in 1:nI)
{
for(t in 1:N)
{
var.obs[t,i] <- SE2[t,i] # drop tau2
fake.tau[t,i] <- tau2
ivar.obs[t,i] <- 1/var.obs[t,i]
# note total observation error (TOE)
TOE[t,i] <- sqrt(var.obs[t,i])
}}
",append=TRUE)}
# Run rest of code
cat("
# Process variance prior
isigma2.est ~ dgamma(0.001,0.001)
# Priors and constraints
logY.est[1] ~ dnorm(logY1, 1) # Prior for initial population size
mean.r ~ dnorm(1, 0.001) # Prior for mean growth rate
# Likelihood
# State process
for (t in 1:(N-1)){
r[t] ~ dnorm(mean.r, isigma2)
logY.est[t+1] <- logY.est[t] + r[t] }
# Observation process
for (t in 1:N) {
for(i in 1:nI){
y[t,i] ~ dnorm(logY.est[t]+logq[i], ivar.obs[t,i])
}}
# Population sizes on real scale
for (t in 1:N) {
Y.est[t] <- exp(logY.est[t])
}
}
",fill = TRUE)
sink()
q.init = 1
mCPUE = as.matrix(CPUE[q1.y:n.years,qs])
mSE2 = as.matrix(se2[q1.y:n.years,qs])
if(n.indices>1) for(i in 2:n.indices){q.init[i] = mean(mCPUE[,i],na.rm=TRUE)/mean(mCPUE[,1],na.rm=TRUE)}
# Bundle data
jags.data <- list(y = log(mCPUE),SE2=mSE2, logY1 = log(mCPUE[1,1]), N = length(q1.y:n.years),nI=n.indices,sigma.fixed=ifelse(sigma.proc==TRUE,0,sigma.proc))
# Initial values
inits <- function(){list(isigma2.est=runif(1,20,100), itau2=runif(1,80,200), mean.r = rnorm(1),iq = 1/q.init)}
# Parameters monitored
parameters <- c("mean.r", "sigma","r", "Y.est","q")
# Call JAGS from R (BRT 3 min)
mod.cpue <- jags(jags.data, inits, parameters, "cpueAVG.jags", n.chains = nc, n.thin = max(nt,2), n.iter = max(ni/5,10000), n.burnin = nb/10)
cat(paste0("\n","><> Plot State-Space CPUE fits in Input subfolder <><","\n"))
# get individual trends
fitted <- lower <- upper <- NULL
cpue.yrs = years[q1.y:n.years]
for (t in 1:nrow(mCPUE)){
fitted[t] <- median(mod.cpue$BUGSoutput$sims.list$Y.est[,t])
lower[t] <- quantile(mod.cpue$BUGSoutput$sims.list$Y.est[,t], 0.025)
upper[t] <- quantile(mod.cpue$BUGSoutput$sims.list$Y.est[,t], 0.975)}
q.adj = apply(mod.cpue$BUGSoutput$sims.list$q,2,median)
Par = list(mfrow=c(1,1),mar = c(3.5, 3.5, 0.1, 0.1), mgp =c(2.,0.5,0), tck = -0.02,cex=0.8)
png(file = paste0(input.dir,"/CPUE_",assessment,"_",Scenario,".png"), width = 5, height = 3.5,
res = 200, units = "in")
par(Par)
u.ylim = NULL
for(i in 1:n.indices){ u.ylim = c(u.ylim,exp(log(mCPUE[,i]/q.adj[i])+1.96*sqrt(mSE2[,i])))}
ylim = c(0,max(u.ylim,na.rm=TRUE))
plot(0, 0, ylim = ylim, xlim = range(cpue.yrs), ylab = "Expected CPUE", xlab = "Year", col = "black", type = "n")
legend("topright",paste(indices),lwd=2,col=(jabba.colors)[1:n.indices],bty="n")
polygon(x = c(cpue.yrs,rev(cpue.yrs)), y = c(lower,rev(upper)), col = "gray", border = "gray90")
for(i in 1:n.indices)
{
shift = runif(1,-0.1,0.1)
cols=jabba.colors[qs[i]]
plotCI(cpue.yrs+shift,mCPUE[,i]/q.adj[i],ui=exp(log(mCPUE[,i]/q.adj[i])+1.96*sqrt(mSE2[,i])),li=exp(log(mCPUE[,i]/q.adj[i])-1.96*sqrt(mSE2[,i])),add=TRUE,col= cols,pt.bg = cols,pch=21,gap=0)
lines(cpue.yrs+shift,mCPUE[,i]/q.adj[i], col = cols,lwd=2)
points(cpue.yrs+shift,mCPUE[,i]/q.adj[i], bg = cols,pch=21)
}
lines(cpue.yrs,fitted,lwd=2)
dev.off()
logSE = apply(log(mod.cpue$BUGSoutput$sims.list$Y.est),2,sd)
if(nrow(mCPUE)<n.years) {
fitted = c(rep(NA,q1.y-1),fitted)
logSE = c(rep(0.2,q1.y-1),logSE)
}
avgCPUE = data.frame(Year=years,CPUE= fitted,logSE=logSE)
write.csv(avgCPUE,paste0(input.dir,"/avgCPUE_",assessment,"_",Scenario,".csv"))
if(meanCPUE==TRUE){
cat(paste0("\n","><> Use average CPUE as input for JABBA <><","\n"))
CPUE = as.matrix(avgCPUE[,2])
cpue.check = cpue[,-1]
cpue.check[is.na(cpue[,-1])]=0
CPUE[,1] = ifelse(apply(cpue.check,1,sum)==0,rep(NA,length(CPUE[,1])),CPUE[,1])
se2 = as.matrix(avgCPUE[,3]^2)
n.indices=1
indices = "All"
sets.q =1
sets.var =1
}
}
#--------------------------------------------------------------------------
# END of CPUE State-Space tool
#--------------------------------------------------------------------------
#-----------
# FUNCTIONS
#-----------
cat(paste0("\n","><> Prepare JABBA prior inputs <><","\n"))
#--------------------------------------------------
# Function to get beta prior parameters
#--------------------------------------------------
get_beta <- function(mu,CV,Min=0,Prior="x"){
a = seq(0.0001,1000,0.001)
b= (a-mu*a)/mu
s2 = a*b/((a+b)^2*(a+b+1))
sdev = sqrt(s2)
# find beta )parameter a
CV.check = (sdev/mu-CV)^2
a = a[CV.check==min(CV.check)]
#find beta parameter b
b = (a-mu*a)/mu
x = seq(Min,1,0.001)
pdf = dbeta(x,a,b)
plot(x,pdf,type="l",xlim=range(x[pdf>0.01]),xlab=paste(Prior),ylab="",yaxt="n")
polygon(c(x,rev(x)),c(rep(0,length(x)),rev(ifelse(pdf==Inf,100000,pdf))),col="grey")
return(c(a,b))
}
#--------------------------------------------------
# Function to get gamma prior parameters
#--------------------------------------------------
get_gamma <- function(mu,CV,Prior="x"){
a = seq(0.00001,10000,0.0001)
b = a/mu
s2 = (a/b^2)
sdev = sqrt(s2)
# find beta )parameter a
CV.check = (sdev/mu-CV)^2
a = a[CV.check==min(CV.check)]
#find beta parameter b
b = a/mu
x = sort(rgamma(1000,a,b))
pdf = dgamma(x,a,b)
plot(x,pdf,type="l",xlim=range(x[pdf>0.01]),xlab=paste(Prior),ylab="",yaxt="n")
polygon(c(x,rev(x)),c(rep(0,length(x)),rev(ifelse(pdf==Inf,100000,pdf))),col="grey")
return(c(a,b))
}
#--------------------------------------------------
# Function to get lognormal prior parameters
#--------------------------------------------------
plot_lnorm <- function(mu,CV,Prior="x"){
sdev= sqrt(log(CV^2+1))
rand.pr = rlnorm(1000,log(mu)-0.5*sdev^2,sdev)
x = seq(min(rand.pr),quantile(rand.pr,0.995),max(rand.pr/500))
pdf = dlnorm(x,log(mu),sdev)
plot(x,pdf,type="l",xlim=range(x),xlab=paste(Prior),ylab="",yaxt="n")
polygon(c(x,rev(x)),c(rep(0,length(x)),rev(ifelse(pdf==Inf,100000,pdf))),col="grey")
return(c(mu,sdev))
}
#------------------------------------
# Function kobeJabba for FLR
#------------------------------------
kobeJabba<-function(x,minyear=1){
out=cbind(melt(x[,,2]),c(x[,,3]))
names(out)=c("iter","year","stock","harvest")
out$year=out$year+minyear-1
out}
#-------------------------------------------------
# Function kobeJabbaProj for projections with FLR
#-------------------------------------------------
kobeJabbaProj<-function(x,minyear=1,tac=NULL){
out=cbind(melt(x[,,,2]),c(x[,,,3]))
names(out)=c("iter","year","tac","stock","harvest")
out$year=out$year+minyear-1
out}
#----------------------------------------------------
#----------------------------------------------------
if(r.dist=="range"){
# initial range of r based on resilience (FishBase.org)
if(length(r.prior)>1){ start.r = r.prior} else
if(r.prior == "High") {
start.r <- c(0.6,1.5)} else if(r.prior == "Medium") {
start.r <- c(0.2,0.8)} else if(r.prior == "Low") {
start.r <- c(0.05,0.5)} else { # i.e. res== "Very low"
start.r <- c(0.015,0.1)}
log.r = mean(log(start.r))
sd.r = abs(log.r - log(start.r[1]))/2
r.prior = c(exp(log.r),sd.r)
CV.r = sqrt(exp(sd.r^2)-1)
} else {
log.r = log(r.prior[1])
sd.r = r.prior[2]
CV.r = sqrt(exp(sd.r^2)-1)
}
#----------------------------------------------------
# Prepare K prior
#----------------------------------------------------
if(K.dist=="range"){
log.K = mean(log(K.prior))
sd.K= abs(log.K - log(K.prior[1]))/2
log.K = mean(log(K.prior))+0.5*sd.K^2 # Will be back-bias corrected in lognorm function
CV.K = sqrt(exp(sd.K^2)-1)
} else {
CV.K = K.prior[2]
sd.K=sqrt(log(CV.K^2+1))
log.K = log(K.prior[1])#-0.5*sd.K^2
}
#----------------------------------------------------------
# Get JABBA parameterization and suplus production function
#----------------------------------------------------------
# For Pella-Tomlinson
if(Model==3 | Model==4){
#-----------------------------------------------
# find inflection point
ishape = NULL
# Find shape for SBmsytoK
ishape = seq(0.1,10,0.001)
check.shape =((ishape)^(-1/(ishape-1))-BmsyK)^2
# Set shape (> 0, with 1.001 ~ Fox and 2 = Schaefer)
shape = ishape[check.shape==min(check.shape)]
} else {shape=FALSE}
#------------------------------------------------
# Set shape m for Fox and Schaefer: Fox m ~1; Schaefer m =2
if(shape==FALSE){
if(Model == 1){m=2} else {m = 1.001}}else{m=shape}
cat(paste0("\n","><> Plot Prior distributions in Input subfolder <><","\n"))
Par = list(mfrow=c(1,3),mai=c(0.5,0.1,0,.1),omi = c(0.1,0.2,0.1,0) + 0.1,mgp=c(2,1,0), tck = -0.02,cex=0.8)
png(file = paste0(input.dir,"/Priors_",assessment,"_",Scenario,".png"), width = 9, height = 3,
res = 200, units = "in")
par(Par)
K.pr = plot_lnorm(exp(log.K),CV.K,Prior="K")
r.pr = plot_lnorm(mu=exp(log.r),CV=CV.r,Prior="r")
if(psi.dist=="beta"){
psi.pr = get_beta(mu=psi.prior[1],CV=psi.prior[2],Min=0,Prior=paste0("Prior B(",years[1],")/K"))} else {
psi.pr = plot_lnorm(mu=psi.prior[1],CV=psi.prior[2],Prior=paste0("Prior B(",years[1],")/K"))
}
mtext(paste("Density"), side=2, outer=TRUE, at=0.5,line=1,cex=0.9)
dev.off()
cat(paste0("\n","><> Plot assumed Surplus Production shape in Input subfolder <><","\n"))
# Plot MSY
Par = list(mfrow=c(1,1),mai=c(0.6,0.3,0,.15),omi = c(0.1,0.2,0.2,0) + 0.1,mgp=c(2,1,0), tck = -0.02,cex=0.8)
png(file = paste0(input.dir,"/Production",assessment,"_",Scenario,".png"), width = 6, height = 5,
res = 200, units = "in")
par(Par)
# Get Bmsy/B0 as a fucntion of M
Pmsy=(m)^(-1/(m-1))
P = seq(0.0001,1,0.001)
SP = ifelse(P>Plim,r.pr[1]/(m-1)*P*(1-P^(m-1)),r.pr[1]/(m-1)*P*(1-P^(m-1))*4*P)
#if(is.null(refBmsy)==TRUE) refBmsy = Bmsy
plot(P,SP/max(SP),type="l",ylab="Relative Yield",xlab="B/B0",lwd=2)
mtext(paste("Relative Yield"), side=2, outer=TRUE, at=0.6,line=1,cex=0.9)
legend("topright",c("SPM"),col=c(1),lwd=2,bty="n")
if(Model==4){
# shape density
#dm = dgamma(seq(0.001,5,0.1),5,5)*m
dm = dlnorm((seq(0.001,5,0.1)),log(m),shape.CV)
dm = dm/max(dm)
bmsyk = (seq(0.001,5,0.1))^(-1/(seq(0.001,5,0.1)-1))
polygon(c(bmsyk,rev(bmsyk)),c(dm,rep(0,length(dm))),col="grey",border=0)
}
abline(v=Pmsy,lty=2)
mtext(paste("Relative Yield"), side=2, outer=TRUE, at=0.6,line=1,cex=0.9)
legend("topright",c("SPM"),col=c(1),lwd=2,bty="n")
dev.off()
# Note PRIORS and save input subfolder
Priors =rbind(K.pr,psi.prior,c(r.pr[1],CV.r))
row.names(Priors) = c("K","Psi","r")
colnames(Priors) = c("Mean","CV")
write.csv(Priors,paste0(input.dir,"/Priors",assessment,"_",Scenario,".csv"))
#----------------------------------------------------------
# Set up JABBA
#----------------------------------------------------------
cat(paste0("\n","><> Set up JAGS input <><","\n"))
# Plot MSY
# remove scientific numbers
options(scipen=999)
#----------------------------------------------------------
# starting values
nq = length(unique(sets.q))
nvar = length(unique(sets.var))
#----------------------------------------------------------
# Setup TAC projection
#---------------------------------------------------------
if(Projection==TRUE){
nTAC = length(TACs)
TAC = mat.or.vec(pyrs,nTAC)
yr.last = max(years) # assessment year
for(i in 1:nTAC){
TAC[,i] = c(rep(TACint,imp.yr-yr.last-1),rep(TACs[i],pyrs-(imp.yr-yr.last-1)))
}
}else{
nTAC = 1
TAC = TC[n.years] #
pyrs = 1
}
#---------------------------------------------------------------
# JABBA Schaefer/Fox Models 1-2, Pella 3
#---------------------------------------------------------------
# Slope of hockey-stick
slope.HS = ifelse(Plim==0,1/10^-10,1/Plim)
nSel = 1 # setup for JABBA-SELECT version (in prep)
nI = ncol(CPUE) # number of CPUE series
stI = ifelse(proc.dev.all==TRUE,1, c(1:n.years)[is.na(apply(CPUE,1,mean,na.rm=TRUE))==FALSE][1]) #first year with CPUE
# Initial starting values (new Eq)
inits <- function(){list(K= rlnorm(1,log.K,0.3),r = rlnorm(1,r.pr[1],r.pr[2]) ,q = runif(nq,min(CPUE,na.rm=T)/max(catch[,-1],na.rm=T),max(CPUE,na.rm=T)/max(catch[,-1],na.rm=T)), isigma2.est=runif(1,20,100), itau2=runif(nvar,80,200))}
# starting value option
if(init.values==TRUE){
inits <- function(){list(K= init.K,r=init.r,q = init.q, isigma2.est=runif(1,20,100), itau2=runif(nvar,80,200))}
}
# JABBA input data
surplus.dat = list(N=n.years, TC = TC,I=CPUE,SE2=se2,r.pr=r.pr,psi.pr=psi.pr,K.pr = K.pr,
nq=nq,nI = nI,nvar=nvar,sigma.fixed=ifelse(sigma.proc==TRUE,0,sigma.proc),
sets.var=sets.var, sets.q=sets.q,Plim=Plim,slope.HS=slope.HS,
nTAC=nTAC,pyrs=pyrs,TAC=TAC,igamma = igamma,stI=stI,TACint =TACint,pen.bk = rep(0,n.years),proc.pen=0,K.pen = 0,
obs.pen = rep(0,nvar),P_bound=P_bound,q_bounds=q_bounds,sigmaobs_bound=sigmaobs_bound,sigmaproc_bound=sigmaproc_bound,K_bounds=K_bounds,mu.m=m)
# JAGS model file
JABBA = "JABBA.jags"
# PARAMETERS TO MONITOR
params <- c("K","r", "q", "psi","sigma2", "tau2","m","Hmsy","SBmsy", "MSY", "BtoBmsy","HtoHmsy","CPUE","Proc.Dev","P","SB","H","prP","prBtoBmsy","prHtoHmsy","TOE")
#-----------------------------------------------
# If shape parameter is estimated (Model =4)
if(Model==4){
surplus.dat$m.CV = shape.CV }
#-----------------------------------------------
# If Catch Estimation with CV is used
if(add.catch.CV==TRUE){
surplus.dat$CV.C = CV.C
params = c(params,"estC")
#P_bound[1] = 10^-10 # No Contraint needed
}
#--------------------------
# Capture Settings
#--------------------------
Settings = surplus.dat
Settings$Model.type = Mod.names
Settings$add.catch.CV = add.catch.CV
Settings$catch.cv = catch.cv
Settings$proc.dev.all = proc.dev.all
Settings$Do.Projection = Projection
Settings$TAC.implementation = imp.yr
Settings$catch.metric = catch.metric
Settings$harvest.label = harvest.label
Settings$Run.CPUE.avg.tool = CPUE.plot
Settings$Use.avg.CPUE = meanCPUE
Settings$Specify.init.values =init.values
Settings$save.trajectories = save.trajectories
Settings$save.large.posterior.object = save.all
Settings$Seed = get_seed
Settings$MCMC.ni = ni
Settings$MCMC.saved.steps = nt
Settings$MCMC.burnin = nb
Settings$MCMC.Chains = nc
Settings$MCMC.ni = ni
Settings$MCMC.saved = nsaved
capture.output( Settings, file=paste0(input.dir,"/Settings.txt"))
#-------------------------------------------------------------------
cat(paste0("\n","><> RUN ",Mod.names," model for ",assessment," ",Scenario," in JAGS <><","\n","\n"))
# JAGS MODEL Standard
sink("JABBA.jags")
cat("
model {
# Prior specifications
eps <- 0.0000000000000000000000000000000001 # small constant
#Catchability coefficients
for(i in 1:nq)
{
q[i] ~ dunif(q_bounds[1],q_bounds[2])
}
# Process variance prior
isigma2.est ~ dgamma(igamma[1],igamma[2])
# Carrying Capacity SB0
K ~ dlnorm(log(K.pr[1]),pow(K.pr[2], -2))
# informative priors for Hmsy as a function of r
r ~ dlnorm(log(r.pr[1]),pow(r.pr[2],-2))
")
if(Model==4){
cat("
# Shape m prior
m ~ dlnorm(log(mu.m),pow(m.CV,-2))
",append=TRUE)
}else{ cat("
m <- mu.m
",append=TRUE)}
if(psi.dist =="beta"){
cat("
# Beta Prior for Biomass depletion at the start (deteministic)
psi ~ dbeta(psi.pr[1],psi.pr[2])
",append=TRUE)
} else {
cat("
# Lognormal for Biomass depletion at the start (deteministic)
psi ~ dlnorm(log(psi.pr[1]),pow(psi.pr[2],-2)) #I(0.1,1.1)
",append=TRUE)
}
if(sigma.proc==TRUE){
cat("
# Process variance
isigma2 <- isigma2.est
sigma2 <- pow(isigma2,-1)
sigma <- sqrt(sigma2)
fakesigma.fixed <- sigma.fixed # Prevent unused variable error msg
",append=TRUE)
}else{ cat("
isigma2 <- pow(sigma.fixed+eps,-2)
sigma2 <- pow(isigma2,-1)
sigma <- sqrt(sigma2)
",append=TRUE)}
if(sigma.est==TRUE){
cat("
# Obsevation variance
for(i in 1:nvar)
{
# Observation error
itau2[i]~ dgamma(0.001,0.001)
tau2[i] <- 1/itau2[i]
}
for(i in 1:nI)
{
for(t in 1:N)
{
var.obs[t,i] <- SE2[t,i]+tau2[sets.var[i]]
ivar.obs[t,i] <- 1/var.obs[t,i]
# note total observation error (TOE)
TOE[t,i] <- sqrt(var.obs[t,i]) # Total observation variance
}}
",append=TRUE)
}else{ cat("
# Obsevation variance
for(i in 1:nvar)
{
# Observation error
itau2[i]~ dgamma(4,0.01)
tau2[i] <- 1/itau2[i]
}
for(i in 1:nI)
{
for(t in 1:N)
{
var.obs[t,i] <- SE2[t,i] # drop tau2
fake.tau[t,i] <- tau2[sets.var[i]]
ivar.obs[t,i] <- 1/var.obs[t,i]
# note total observation error (TOE)
TOE[t,i] <- sqrt(var.obs[t,i])
}}
",append=TRUE)}
# Run standard JABBA
if(add.catch.CV==FALSE){
cat("
for(t in 1:N){
estC[t] <- TC[t]
}
",append=TRUE)} else {
cat("
for(t in 1:N){
estC[t] ~ dlnorm(log(TC[t]),pow(CV.C[t],-2))
}
",append=TRUE)}
cat("
#Process equation
Pmean[1] <- log(psi)
iPV[1] <- ifelse(1<(stI),10000,isigma2) # inverse process variance
P[1] ~ dlnorm(Pmean[1],iPV[1]) # set to small noise instead of isigma2
penB[1] <- ifelse(P[1]<P_bound[1],log(K*P[1])-log(K*P_bound[1]),ifelse(P[1]>P_bound[2],log(K*P[1])-log(K*P_bound[2]),0)) # penalty if Pmean is outside viable biomass
# Process equation
for (t in 2:N)
{
Pmean[t] <- ifelse(P[t-1] > Plim,
log(max(P[t-1] + r/(m-1)*P[t-1]*(1-pow(P[t-1],m-1)) - estC[t-1]/K,0.001)),
log(max(P[t-1] + r/(m-1)*P[t-1]*(1-pow(P[t-1],m-1))*P[t-1]*slope.HS - estC[t-1]/K,0.001)))
iPV[t] <- ifelse(t<(stI),10000,isigma2) # inverse process variance
P[t] ~ dlnorm(Pmean[t],iPV[t])
penB[t] <- ifelse(P[t]<(P_bound[1]),log(K*P[t])-log(K*(P_bound[1])),ifelse(P[t]>P_bound[2],log(K*P[t])-log(K*(P_bound[2])),0)) # penalty if Pmean is outside viable biomass
}
# Process error deviation
for(t in 1:N){
Proc.Dev[t] <- log(P[t]*K)-log(exp(Pmean[t])*K)}
# Enforce soft penalties on bounds for P
for(t in 1:N){
pen.bk[t] ~ dnorm(penB[t],1000) # enforce penalty with CV = 0.1
}
Hmsy <- r*pow(m-1,-1)*(1-1/m)
for (t in 1:N)
{
SB[t] <- K*P[t]
H[t] <- TC[t]/SB[t]
}
# Observation equation in related to EB
for(i in 1:nI)
{
for (t in 1:N)
{
Imean[t,i] <- log(q[sets.q[i]]*P[t]*K);
I[t,i] ~ dlnorm(Imean[t,i],(ivar.obs[t,i]));
#CPUE[t,i] ~ dlnorm(Imean[t,i],(ivar.obs[t,i]))#q[sets.q[i]]*P[t]*K
CPUE[t,i] <- q[sets.q[i]]*P[t]*K
}}
#Management quantaties
SBmsy_K <- (m)^(-1/(m-1))
SBmsy <- SBmsy_K*K
MSY <- SBmsy*Hmsy
for (t in 1:N)
{
# use x y to put them towards the end of the alphabetically sorted mcmc object
#SP[t] <- pow(r.pella,-(m-1))*SB[t]*(1-pow(P[t],m-1))
BtoBmsy[t] <- SB[t]/SBmsy
HtoHmsy[t] <- H[t]/(Hmsy)
}
# Enforce soft penalty on K if < K_bounds >
K.pen ~ dnorm(penK,1000) # enforce penalty
penK <- ifelse(K<(K_bounds[1]),log(K)-log(K_bounds[1]),ifelse(K>K_bounds[2],log(K)-log(K_bounds[2]),0)) # penalty if Pmean is outside viable biomass
# Enforce soft penalty on process deviance if sigma.proc > 0.2
proc.pen ~ dnorm(penProc,1000) # enforce penalty
penProc <- ifelse(sigma>sigmaproc_bound,log(sigma)-log(sigmaproc_bound),0)
# Enforce soft penalty on observation error if sigma.obs > sigma_bound
for(i in 1:nvar){
obs.pen[i] ~ dnorm(penObs[i],1000) # enforce penalty
penObs[i] <- ifelse(pow(tau2[i],0.5)>sigmaobs_bound,log(pow(tau2[i],0.5))-log(sigmaobs_bound),0)
}
", append=TRUE)
# PROJECTION
if(Projection==TRUE){
cat("
for(i in 1:nTAC){
# Project first year into the future
prPmean[1,i] <- ifelse(P[N] > Plim,
log(max(P[N] + Hmsy/(1-1/m)*P[N]*(1-pow(P[N],m-1)) - TACint/K,0.005)),
log(max(P[N] + Hmsy/(1-1/m)*P[N]*(1-pow(P[N],m-1))*4*P[N] - TACint/K,0.001)))
prP[1,i] ~ dlnorm(prPmean[1,i],isigma2)
# Project all following years
for(t in 2:pyrs){
prPmean[t,i] <- ifelse(prP[t-1,i] > Plim,
log(max(prP[t-1,i] + Hmsy/(1-1/m)*prP[t-1,i]*(1-pow(prP[t-1,i],m-1)) - TAC[t-1,i]/K,0.001)),
log(max(prP[t-1,i] + Hmsy/(1-1/m)*prP[t-1,i]*(1-pow(prP[t-1,i],m-1))*slope.HS*prP[t-1,i] - TAC[t-1,i]/K,0.001)))
# process error (as monte-carlo simular)
prP[t,i] ~ dlnorm(prPmean[t,i],isigma2)}
for(t in 1:pyrs){
prB[t,i] <- prP[t,i]*K
prH[t,i] <- TAC[t,i]/prB[t,i]
prHtoHmsy[t,i] <- prH[t,i]/Hmsy
prBtoBmsy[t,i] <- prB[t,i]/SBmsy
}}
",append=TRUE)} else {
cat("
#Prevent error for unused input
fakeTAC <- TAC
fakepyrs <- pyrs
fakenTAC <- nTAC
fakeTACint <- TACint
prHtoHmsy <- 1
prP <- 1
prBtoBmsy <- 1
", append=TRUE)}
cat("
} # END OF MODEL
",append=TRUE,fill = TRUE)
sink()
ptm <- proc.time()
mod <- jags(surplus.dat, inits,params,paste(JABBA), n.chains = nc, n.thin = nt, n.iter = ni, n.burnin = nb) # adapt is burn-in
proc.time() - ptm
save.time = proc.time() - ptm
cat(paste0("\n",paste0("><> Scenario ",Scenario,"_",Mod.names," completed in ",as.integer(save.time[3]/60)," min and ",round((save.time[3]/60-as.integer(save.time[3]/60))*100)," sec <><","\n")))
cat(paste0("\n","><> Produce results output of ",Mod.names," model for ",assessment," ",Scenario," <><","\n"))
# if run with library(rjags)
posteriors = mod$BUGSoutput$sims.list
#-----------------------------------------------------------
# <><<><<><<><<><<><<>< Outputs ><>><>><>><>><>><>><>><>><>
#-----------------------------------------------------------
output.dir = paste0(File,"/",assessment,"/",Scenario,"_",Mod.names,"/Output")
dir.create(output.dir, showWarnings = FALSE)
# run some mcmc convergence tests
par.dat= data.frame(posteriors[params[c(1:7)]])
geweke = geweke.diag(data.frame(par.dat))
pvalues <- 2*pnorm(-abs(geweke$z))
pvalues
heidle = heidel.diag(data.frame(par.dat))
# postrior means + 95% BCIs
#Model parameter
apply(par.dat,2,quantile,c(0.025,0.5,0.975))
man.dat = data.frame(posteriors[params[8:10]])
#Management quantaties
apply(man.dat,2,quantile,c(0.025,0.5,0.975))
# Depletion
Depletion = posteriors$P[,c(1,n.years)]
colnames(Depletion) = c(paste0("P",years[1]),paste0("P",years[n.years]))
# Current stock status (Kobe posterior)
H_Hmsy.cur = posteriors$HtoHmsy[,c(n.years)]
B_Bmsy.cur = posteriors$BtoBmsy[,c(n.years)]
# Prepare posterior quantaties
man.dat = data.frame(man.dat,Depletion,B_Bmsy.cur,H_Hmsy.cur)
results = round(t(cbind(apply(par.dat,2,quantile,c(0.025,0.5,0.975)))),3)
results = data.frame(Median = results[,2],LCI=results[,1],UCI=results[,3],Geweke.p=round(pvalues,3),Heidel.p = round(heidle[,3],3))
ref.points = round(t(cbind(apply(man.dat,2,quantile,c(0.025,0.5,0.975)))),3)
ref.points = data.frame(Median = ref.points[,2],LCI=ref.points[,1],UCI=ref.points[,3])
# get number of parameters
npar = length(par.dat)
# number of years
N=n.years
#-------------------------------------------------------------------------
# Save parameters, results table and current status posterior in csv files
#-------------------------------------------------------------------------