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functions.r
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functions.r
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#-------------------------------------------------------------------------------
#- Conversions
#-------------------------------------------------------------------------------
an <- function(x){return(as.numeric(x))}
ac <- function(x){return(as.character(x))}
#-------------------------------------------------------------------------------
#- simple plot function for checking input
#-------------------------------------------------------------------------------
plquant<-function(quant)
{
library(lattice)
xyplot(data~year|age,groups=iter,data=quant,type="l")
}
#-------------------------------------------------------------------------------
#- Draw random numbers from a log-normal distribution, but with truncation possibility
#-------------------------------------------------------------------------------
rtlnorm <- function (n, mean = 0, sd = 1, lower = -Inf, upper = Inf)
{
ret <- numeric()
if (length(n) > 1)
n <- length(n)
while (length(ret) < n) {
y <- rlnorm(n - length(ret), mean, sd)
y <- y[y >= lower & y <= upper]
ret <- c(ret, y)
}
stopifnot(length(ret) == n)
ret
}
#-------------------------------------------------------------------------------
#- Fit vonBertalanffy curve to weight data and draw new values based on sd
#-------------------------------------------------------------------------------
randomWeightBertalanffy <- function(histWeight,ages,histPeriod,projPeriod,iters,estim=list(Winf,K,t0),givenMeanWeight=NULL){
#- Make object to store new weights in
storeWt <- array(NA,dim=c(length(ages),length(projPeriod),1,1,1,iters))
#- Get historic observed weights
wtsdat <- as.data.frame(histWeight[,histPeriod])
colnames(wtsdat) <- c(colnames(wtsdat)[1:6],"weight")
#- Fit von Bertalanffy to data
wvBertalanffy <- nls(weight ~ (Winf*(1-exp(-K*(age-t0)))^3.038),data=wtsdat,start=list(Winf=estim$Winf,K=estim$K,t0=estim$t0),control=list(minFactor=1/2^16,maxiter=1000,warnOnly=T))
#- Get mean and sd of fit
meanAge <- log(predict(wvBertalanffy,new=data.frame(age=ages)))
if(is.null(givenMeanWeight)==F) meanAge <- log(givenMeanWeight)
sdAge <- apply(log(histWeight),1,sd,na.rm=T)@.Data
#- Determine 2sd bound
bounds <- cbind(exp(meanAge)-2*apply(histWeight,1,sd)@.Data,
exp(meanAge)+2*apply(histWeight,1,sd)@.Data)
bounds[bounds<=0] <- 0.01
#- Draw new weights
for(iAge in 1:length(ages)){
for(iYr in 1:length(projPeriod)){
for(iTer in 1:iters){
set.seed(iAge*iYr*iTer)
if(iAge == 1){ storeWt[iAge,iYr,,,,iTer] <- rtlnorm(1,mean=meanAge[iAge],sd=sdAge[iAge,,,,,],lower=bounds[iAge,1],upper=bounds[iAge,2])
} else { storeWt[iAge,iYr,,,,iTer] <- rtlnorm(1,mean=meanAge[iAge],sd=sdAge[iAge,,,,,],lower=storeWt[(iAge-1),iYr,,,,iTer],upper=bounds[iAge,2])
}
}
}
}
return(storeWt)}
#-------------------------------------------------------------------------------
#- Update stock objects with 1 year of fisheries and biological data
#-------------------------------------------------------------------------------
updateStocks <- function(iStocks,iFishery,yr,iBiol,iCatchResids){
# iStocks <- tmp_stocks
# iFishery <- tmp_fishery
# yr <- yrmin1
# iBiol <- tmp_biol
# iCatchResids <- ctch
#
iStocks@catch.n[,ac(yr)] <- unitSums(catch.n(iFishery)[,ac(yr)]) * iCatchResids[,ac(yr)]
iStocks@landings.n[,ac(yr)] <- unitSums(landings.n(iFishery)[,ac(yr)])* iCatchResids[,ac(yr)]
iStocks@discards.n[,ac(yr)] <- unitSums(discards.n(iFishery)[,ac(yr)])
iStocks@landings.wt[,ac(yr)] <- unitSums(landings.n(iFishery)[,ac(yr)] * landings.wt(iFishery)[,ac(yr)]) / unitSums(landings.n(iFishery)[,ac(yr)])
iStocks@discards.wt[,ac(yr)] <- 0
iStocks@catch.wt[,ac(yr)] <- iStocks@landings.wt[,ac(yr)]
iStocks@catch[,ac(yr)] <- computeCatch(iStocks)[,ac(yr)]
iStocks@landings[,ac(yr)] <- computeLandings(iStocks)[,ac(yr)]
iStocks@discards[,ac(yr)] <- computeDiscards(iStocks)[,ac(yr)]
iStocks@mat[,ac(yr)] <- iBiol@fec[,ac(yr)]
iStocks@stock.wt[,ac(yr)] <- iBiol@wt[,ac(yr)]
iStocks@m[,ac(yr)] <- iBiol@m[,ac(yr)]
return(iStocks)}
#-------------------------------------------------------------------------------
#- Calculate the harvest by fleet given a TAC for a stock
#-------------------------------------------------------------------------------
fleet.harvest <- function(stk,iYr,TACS){
nUnits <- dims(stk)$unit
nIter <- dims(TACS)$iter
res <- matrix(NA,ncol=nIter,nrow=nUnits,dimnames=list(units=dimnames(stk@stock.n)$unit,iter=1:nIter))
for(iTer in 1:nIter) res[,iTer] <- nls.lm(par=rep(1,nUnits),rescaleF,stk=iter(stk,iTer),iYr=iYr,TACS=c(iter(TACS,iTer)),nls.lm.control(ftol = (.Machine$double.eps)),jac=NULL)$par
stk@harvest[,iYr] <- sweep(stk@harvest[,iYr],c(3,6),res,"*")
return(stk@harvest[,iYr])}
#faster version ImY
fleet.harvestF<- function(stk,iYr,TACS){
nUnits <- dims(stk)$unit
nIter <- dims(TACS)$iter
res <- matrix(NA,ncol=nIter,nrow=nUnits,dimnames=list(units=dimnames(stk@stock.n)$unit,iter=1:nIter))
for(iTer in 1:nIter){
Ns = stk@stock.n[,iYr,1,,,iTer]@.Data
Fs = stk@harvest[,iYr,,,,iTer]@.Data
Cwts = stk@catch.wt[,iYr,,,,iTer]@.Data
Ms = stk@m[,iYr,1,,,iTer]@.Data
res[,iTer] <- nls.lm(par=rep(1,nUnits),rescaleFF,Ns=Ns,Fs=Fs,Cwts=Cwts,Ms=Ms,TACS=TACS[,,,,,iTer],nls.lm.control(ftol = (.Machine$double.eps)),jac=NULL)$par
}
stk@harvest[,iYr] <- sweep(stk@harvest[,iYr],c(3,6),res,"*")
return(stk@harvest[,iYr])}
#faster version
fleet.harvestFF<- function(stk,iYrr,TACS){
# stk <- stf ; iYrr<- ImY ; TACS <- iTAC[,ImY]
lin <- substr(R.Version()$os,1,3)== "lin"
nUnits <- dims(stk)$unit
nIter <- dims(TACS)$iter
res <- matrix(NA,ncol=nIter,nrow=nUnits,dimnames=list(units=dimnames(stk@stock.n)$unit,iter=1:nIter))
for(iTer in 1:nIter){
Ns = c(stk@stock.n[,iYrr,1,,,iTer]@.Data)
Fs = stk@harvest[,iYrr,,,,iTer,drop=T]@.Data
Cwts = stk@catch.wt[,iYrr,,,,iTer,drop=T]@.Data
Ms = c(stk@m[,iYrr,1,,,iTer]@.Data)
if(lin) res[,iTer] <- nls.lm(par=rep(1,nUnits),rescaleFFF,Ns=Ns,Fs=Fs,Cwts=Cwts,Ms=Ms,TACS=c(TACS[,,,,,iTer]),jac=NULL,lower=rep(0,nUnits),upper=rep(1e5,nUnits),nls.lm.control(ftol = (.Machine$double.eps)))$par
if(!lin)res[,iTer] <- nls.lm(par=rep(1,nUnits),rescaleFFF,Ns=Ns,Fs=Fs,Cwts=Cwts,Ms=Ms,TACS=c(TACS[,,,,,iTer]),jac=NULL,lower=rep(0,nUnits),upper=rep(1e5,nUnits),nls.lm.control(ftol = (.Machine$double.eps)))$par
}
stk@harvest[,iYrr] <- sweep(stk@harvest[,iYrr],c(3,6),res,"*")
return(stk@harvest[,iYrr])}
#-------------------------------------------------------------------------------
#- Function to scale the F pattern for stock
#-------------------------------------------------------------------------------
rescaleF <- function(mult,stk.=stk,iYr.=iYr,TACS.=TACS){
stk.@harvest[,iYr.] <- sweep(stk.@harvest[,iYr.],3,mult,"*")
stkZ <- unitSums(stk.@harvest[,iYr.]) + stk.@m[,iYr.,1]
res <- sqrt(c((TACS. - c(apply(sweep(stk.@stock.n[,iYr.] * stk.@catch.wt[,iYr.] * sweep(stk.@harvest[,iYr.],c(1:2,4:6),stkZ,"/"),c(1:2,4:6),(1-exp(-stkZ)),"*"),3:6,sum,na.rm=T)))^2))
return(res)}
#Faster version
rescaleFF <- function(mult,Ns=Ns,Fs=Fs,Cwts=Cwts,Ms=Ms,TACS.=TACS){
Fs <- sweep(Fs,3,mult,"*")
stkZ <- apply(Fs,1,sum) + Ms
res <- sqrt(c((TACS. - c(apply(sweep(sweep(Fs,c(1:2,4:6),stkZ,"/")* Cwts,c(1:2,4:6),Ns * (1-exp(-stkZ)),"*") ,3:6,sum))))^2)
return(res)}
#Faster version
rescaleFFF <- function(mult,Ns.=Ns,Fs.=Fs,Cwts.=Cwts,Ms.=Ms,TACS.=TACS){
Fs. <- t(t(Fs.) * mult)
stkZ <- rowSums(Fs.) + Ms.
res <- sqrt(c((TACS. - c(colSums(sweep(sweep(Fs.,1,stkZ,"/")* Cwts.,1,Ns. * (1-exp(-stkZ)),"*")))))^2)
return(res)}
#-------------------------------------------------------------------------------
#- Calculate the catch by fleet
#-------------------------------------------------------------------------------
harvestCatch <- function(stk.,iYr){
stkZ <- unitSums(stk.@harvest[,iYr]) + stk.@m[,iYr,1]
res <- apply(sweep(stk.@stock.n[,iYr] * stk.@catch.wt[,iYr] * sweep(stk.@harvest[,iYr],c(1:2,4:6),stkZ,"/"),c(1:2,4:6),(1-exp(-stkZ)),"*"),3:6,sum,na.rm=T)
return(res)}
#-------------------------------------------------------------------------------
#- Management plan: calculate A and B TAC
#-------------------------------------------------------------------------------
find.FABF <- function(mult,stk.,mpPoints.){
#mult<-1
#stk.<- iter(stf[,FcY],iTer)
#mpPoints.<-mpPoints
Fs <- sweep(stk.@harvest@.Data,3,mult,"*")
Ns <- stk.@stock.n@.Data[,,1,,,]
Ms <- stk.@m@.Data[,,1,,,]
Hspwns <- stk.@harvest.spwn@.Data[,,1,,,]
Mspwns <- stk.@m.spwn@.Data[,,1,,,]
Mats <- stk.@mat@.Data[,,1,,,]
Swghts <- stk.@stock.wt@.Data[,,1,,,]
Cwghts <- stk.@catch.wt@.Data
bigF <- apply(Fs,1,sum)
ssb <- sum(Ns * Swghts * exp(-bigF*Hspwns - Ms*Mspwns) * Mats)
if(ssb < mpPoints.$Btrigger) resA <- mpPoints.$Ftarget* ssb / mpPoints.$Btrigger
if(ssb > mpPoints.$Btrigger) resA <- mpPoints.$Ftarget
fbarA <- mean(bigF[ac(4:8)])
ret <- (fbarA-resA)^2
return(ret)}
#-------------------------------------------------------------------------------
#- Function to calculate the change in effort
#-------------------------------------------------------------------------------
f21t <- function(fmult,Q1,morts,Fsq,landsel,nums,land_wts){
bigF <- Fsq * fmult
return(abs(Q1 - sum( ((bigF)/(bigF + morts))*nums*(1-exp(-bigF-morts))* land_wts)))
}
#-------------------------------------------------------------------------------
#- Function to return the change in effort
#-------------------------------------------------------------------------------
f31t <- function(Q1,iBiol,ImY,iFishery,iFailRuns=NULL){
lan_wt <- landings.wt( iFishery)[,ac(ImY)]
fpattern<- catch.sel(iFishery)[,ac(ImY)]
iters <- dims(Q1)$iter
nUnits <- dims(Q1)$unit
fm <- matrix(NA,nrow=dims(iFishery)$unit,ncol=iters,dimnames=list(dimnames(iFishery@landings.n)$unit,dimnames(iFishery@landings.n)$iter))
if(is.null(iFailRuns)==T){
for(i in 1:iters){
iMort <- iBiol@m[,ac(ImY),,,,i]@.Data
iNum <- iBiol@n[,ac(ImY),,,,i]@.Data
iFsq <- fpattern[,,,,,i]@.Data
iLanwt <- lan_wt[,,,,,i]@.Data
iQ1 <- Q1[,ac(ImY),,,,i]@.Data
fm[,i] <- nls.lm(par=rep(1,nUnits),rescaleFBiol,iFsq.=iFsq,iMort.=iMort,iNum.=iNum,iLanwt.=iLanwt,TACS.=iQ1,nls.lm.control(ftol = (.Machine$double.eps)),jac=NULL,lower=rep(0,nUnits),upper=rep(1e5,nUnits))$par
}
} else {
for(i in (1:iters)[-iFailRuns]){
iMort <- iBiol@m[,ac(ImY),,,,i]@.Data
iNum <- iBiol@n[,ac(ImY),,,,i]@.Data
iFsq <- fpattern[,,,,,i]@.Data
iLanwt <- lan_wt[,,,,,i]@.Data
iQ1 <- Q1[,ac(ImY),,,,i]@.Data
fm[,i] <- nls.lm(par=rep(1,nUnits),rescaleFBiol,iFsq.=iFsq,iMort.=iMort,iNum.=iNum,iLanwt.=iLanwt,TACS.=iQ1,nls.lm.control(ftol = (.Machine$double.eps)),jac=NULL,lower=rep(0,nUnits),upper=rep(1e5,nUnits))$par
}
}
return(fm)
}
f31tF <- function(Q1,iBiol,ImY,iFishery,iFailRuns=NULL){
# Q1=TAC*TACusage ; iBiol=biol ; iFishery =fishery
lin <- substr(R.Version()$os,1,3)== "lin"
lan_wt <- landings.wt( iFishery)[,ac(ImY)]
fpattern<- catch.sel(iFishery)[,ac(ImY)]
iters <- dims(Q1)$iter
nUnits <- dims(Q1)$unit
fm <- matrix(NA,nrow=dims(iFishery)$unit,ncol=iters,dimnames=list(dimnames(iFishery@landings.n)$unit,dimnames(iFishery@landings.n)$iter))
if(is.null(iFailRuns)==T){
for(i in 1:iters){
iMort <- c(iBiol@m[,ac(ImY),,,,i]@.Data)
iNum <- c(iBiol@n[,ac(ImY),,,,i]@.Data)
iNum[1] <- c(iBiol@n[1,ac(ImY-1),,,,i]@.Data)
iFsq <- fpattern[,,,,,i,drop=T]@.Data
iLanwt <- lan_wt[,,,,,i,drop=T]@.Data
iQ1 <- c(Q1[,ac(ImY),,,,i]@.Data)
if(lin) fm[,i] <- nls.lm(par=rep(1,nUnits),rescaleFBiolF,iFsq.=iFsq,iMort.=iMort,iNum.=iNum,iLanwt.=iLanwt,TACS.=iQ1,nls.lm.control(ftol = (.Machine$double.eps)),jac=NULL,lower=rep(0,nUnits),upper=rep(1e5,nUnits))$par
if(!lin)fm[,i] <- nls.lm(par=rep(1,nUnits),rescaleFBiolF,iFsq.=iFsq,iMort.=iMort,iNum.=iNum,iLanwt.=iLanwt,TACS.=iQ1,nls.lm.control(ftol = (.Machine$double.eps)),jac=NULL,lower=rep(0,nUnits),upper=rep(1e5,nUnits))$par
}
} else {
for(i in (1:iters)[-iFailRuns]){
iMort <- c(iBiol@m[,ac(ImY),,,,i]@.Data)
iNum <- c(iBiol@n[,ac(ImY),,,,i]@.Data)
iNum[1] <- c(iBiol@n[1,ac(ImY-1),,,,i]@.Data)
iFsq <- fpattern[,,,,,i,drop=T]@.Data
iLanwt <- lan_wt[,,,,,i,drop=T]@.Data
iQ1 <- c(Q1[,ac(ImY),,,,i]@.Data)
if(lin) fm[,i] <- nls.lm(par=rep(1,nUnits),rescaleFBiolF,iFsq.=iFsq,iMort.=iMort,iNum.=iNum,iLanwt.=iLanwt,TACS.=iQ1,nls.lm.control(ftol = (.Machine$double.eps)),jac=NULL,lower=rep(0,nUnits),upper=rep(1e5,nUnits))$par
if(!lin)fm[,i] <- nls.lm(par=rep(1,nUnits),rescaleFBiolF,iFsq.=iFsq,iMort.=iMort,iNum.=iNum,iLanwt.=iLanwt,TACS.=iQ1,nls.lm.control(ftol = (.Machine$double.eps)),jac=NULL,lower=rep(0,nUnits),upper=rep(1e5,nUnits))$par
}
}
return(fm)
}
#-------------------------------------------------------------------------------
#- Function to scale the F pattern for biol
#-------------------------------------------------------------------------------
rescaleFBiol <- function(mult,iFsq.,iMort.,iNum.,iLanwt.,TACS.){
iF <- sweep(iFsq.,3,mult,"*")
stkZ <- apply(iF,1,sum) + iMort.
res <- sqrt(c((c(TACS.) - c(apply(sweep(sweep(iLanwt. * iF,c(1:2,4:6),stkZ,"/"),c(1:2,4:6),iNum. * (1-exp(-stkZ)),"*"),3:6,sum,na.rm=T)))^2))
return(res)}
rescaleFBiolF <- function(mult,iFsq.,iMort.,iNum.,iLanwt.,TACS.){
iF <- t(t(iFsq.) * mult)
stkZ <- rowSums(iF) + iMort.
res <- sqrt(c((TACS. - c(colSums(sweep(sweep(iF,1,stkZ,"/")* iLanwt.,1,iNum. * (1-exp(-stkZ)),"*")))))^2)
return(res)}
#-------------------------------------------------------------------------------
#- Function to calculate the ssb based on biological numbers at age
#-------------------------------------------------------------------------------
ssbb <- function(iBiol,iHarvest,iStck){
# iBiol= biol[,ac(projPeriod)] ; iHarvest =f[,ac(projPeriod)] ;iStck=stocks[,ac(projPeriod)]
res <- quantSums(iBiol@n * iBiol@wt * exp(-iHarvest * iStck@harvest.spwn - iBiol@m * iStck@m.spwn) * iBiol@fec)
return(res)}
#-------------------------------------------------------------------------------
#- Function to estimate the mean and sd of the lognormal distribution that fits
# the observed recruitment best
#-------------------------------------------------------------------------------
optimRecDistri <- function(pars,recs){
sim <- plnorm(recs,meanlog=pars[1],sdlog=pars[2])
lik <- sum(abs(log(sim) - log(seq(0,1,length.out=length(recs)+2)[-c(1,length(recs)+2)])))
return(lik)}
#-------------------------------------------------------------------------------
#- Function for paralelisation
#-------------------------------------------------------------------------------
startCluster <- function(snow.cores){
#-Start up cluster
library(snow)
library(Rmpi)
num.cores <- snow.cores
snow.cluster <- c(rep("localhost",num.cores))
cl <- makeMPIcluster(count=length(snow.cluster))
options(timeout=14*86400)
clusterEvalQ(cl,library(FLSAM))
clusterEvalQ(cl,library(MASS))
clusterEvalQ(cl,library(msm))
return(cl)}
exportCluster <- function(cl,lst){
clusterExport(cl,lst)
}
retroLB <- function(x){
cat(paste("\n\n\n\ THIS IS ITERATION NUMBER",x,"\n\n\n"))
stck <- window(iter(stocks,x),end=histMaxYr)
tun <- FLIndices()
for(iSurv in names(surveys))
tun[[iSurv]] <- iter(window(surveys[[iSurv]],end=range(NSH.tun[[iSurv]])["maxyear"]),x)
res <- retro(stock=stck,indices=tun,
control=ctrl,retro=10,base.assess=base.assess)
return(res)}
#-------------------------------------------------------------------------------
#- Function to overwrite the updated tmp_stocks to stocks (but do not overwrite
# the elements listed below from tmp to stocks, hence they are taken from
# the stocks object)
#-------------------------------------------------------------------------------
tmp2stocks <- function(stocks2,tmp_stocks2,yr){
TaYtmp_stocks <- tmp_stocks2[,ac(yr)]
TaYstocks <- stocks2[,ac(yr)]
TaYtmp_stocks@stock <- TaYstocks@stock
TaYtmp_stocks@stock.n <- TaYstocks@stock.n
TaYtmp_stocks@harvest <- TaYstocks@harvest
TaYtmp_stocks@name <- TaYstocks@name
TaYtmp_stocks@desc <- TaYstocks@desc
TaYtmp_stocks@range <- TaYstocks@range
stocks2[,ac(yr)] <- TaYtmp_stocks[,ac(yr)]
return(stocks2)}
#-----------------------------------------------------------------------------------------------------------
# Recruitment function => gives the R accoring to the SSB and SR parameters
#-----------------------------------------------------------------------------------------------------------
B2Rsimple<-function (Biom, SRp){
#Biom<- ssb(biol)[,ac(iYr-1),,,,its]
#SRp<- SRmod[its,]
#ite<- its
#yr<- iYr
#recreg<- RecType
#
mod <- SRp$mod
A <- SRp$A
B <- SRp$B
if (mod=="segreg" ) mu <- A*(Biom>=B)+A*Biom*(Biom<B)/B
if (mod=="ricker" ) mu <- A*Biom*exp(-B*Biom)
if (mod=="bevholt") mu <- A*Biom/(B+Biom)
REC<-mu
return(REC)}
B2R<-function (Biom, SRp,ite,yr,recreg){
#Biom<- ssb(biol)[,ac(iYr-1),,,,its]
#SRp<- SRmod[its,]
#ite<- its
#yr<- iYr
#recreg<- RecType
#
if( recreg != "SGRec"){
mod <- SRp$mod
A <- SRp$A
B <- SRp$B
sig <- SRp$sigma # this rescaling should have been done in the Bayesian protocol
if (mod=="segreg" ) mu <- A*(Biom>=B)+A*Biom*(Biom<B)/B
if (mod=="ricker" ) mu <- A*Biom*exp(-B*Biom)
if (mod=="bevholt") mu <- A*Biom/(B+Biom)
REC<-exp(log(mu)+rnorm(1,0,sig))
REC<- max(REC,10)
}
if ( recreg == "SGRec")
REC <- SRp[1,yr]
return(REC)}
B2Rdet<-function (Biom, SRp,ite,yr,recreg){
#Biom<- ssb(biol)[,ac(iYr-1),,,,its]
#SRp<- SRmod[its,]
#ite<- its
#yr<- iYr
#recreg<- RecType
#
if( recreg != "SGRec"){
mod <- SRp$mod
A <- SRp$A
B <- SRp$B
sig <- SRp$sigma # this rescaling should have been done in the Bayesian protocol
if (mod=="segreg" ) mu <- A*(Biom>=B)+A*Biom*(Biom<B)/B
if (mod=="ricker" ) mu <- A*Biom*exp(-B*Biom)
if (mod=="bevholt") mu <- A*Biom/(B+Biom)
REC<-mu
REC<- max(REC,10)
}
if ( recreg == "SGRec")
REC <- SRp[1,yr]
return(REC)}
B2Rbis<-function (Biom, SRp,its,yr,dev){
# Biom <- ssb(biol)[,ac(iYr-1),,,,its]
# SRp <- SRmod[its,]
# ite <- its
# dev <- devR
# yr <- iYr-1
mod <- SRp$mod
A <- SRp$A
B <- SRp$B
sig <- SRp$sigma # this rescaling should have been done in the Bayesian protocol
if (mod=="segreg" ) mu <- A*(Biom>=B)+A*Biom*(Biom<B)/B
if (mod=="ricker" ) mu <- A*Biom*exp(-B*Biom)
if (mod=="bevholt") mu <- A*Biom/(B+Biom)
REC<- mu * exp(dev[,ac(yr),,,,its])
REC<- max(REC,10)
return(REC)}
B2RF<-function (Biom, SRp,yr,dev){
# Biom <- iter(ssbtp,1:3)
# SRp <- SRmod[1:3,]
# dev <- iter(devR,1:3)
# yr <- iYr-1
Biom <- c(Biom@.Data)
mod <- SRp$mod
A <- SRp$A
B <- SRp$B
idxsegreg<- which(mod=="segreg")
idxricker<- which(mod=="ricker")
idxbevholt<- which(mod=="bevholt")
mu <- numeric(length(A))
mu[idxsegreg] <- A[idxsegreg]*(Biom[idxsegreg]>=B[idxsegreg])+A[idxsegreg]*Biom[idxsegreg]*(Biom[idxsegreg]<B[idxsegreg])/B[idxsegreg]
mu[idxricker] <- A[idxricker]*Biom[idxricker]*exp(-B[idxricker]*Biom[idxricker])
mu[idxbevholt] <- A[idxbevholt]*Biom[idxbevholt]/(B[idxbevholt]+Biom[idxbevholt])
REC <- (mu * exp(dev[,ac(yr)]))+10
return(REC)}
#-----------------------------------------------------------------------------------------------------------
# modified mcmc function to resample stock from the SAM assessment.
#-----------------------------------------------------------------------------------------------------------
monteCarloStock2 <-
function (stck, sam, realisations, run.dir = tempdir(),seed_number)
{
require(MASS)
ctrl <- sam@control
mcstck <- propagate(stck, iter = realisations)
mcstck <- window(mcstck, start = range(sam)["minyear"], end = range(sam)["maxyear"])
mcstck@stock.n[] <- NA
mcstck@harvest[] <- NA
set.seed(seed_number)
random.param <- mvrnorm(realisations, sam@params$value, sam@vcov)
save(random.param, file = file.path(run.dir, "random.param.RData"))
n.states <- length(unique(ctrl@states[names(which(ctrl@fleets ==
0)), ]))
yrs <- dims(sam)$minyear:dims(sam)$maxyear
ages <- dims(sam)$age
u <- random.param[, which(colnames(random.param) == "U")]
ca <- random.param[, which(colnames(random.param) == "logCatch")]
idxNs <- c(mapply(seq, from = seq(1, ncol(u), ages + n.states),
to = seq(1, ncol(u), ages + n.states) + ages - 1, by = 1))
idxFs <- c(mapply(seq, from = seq(1, ncol(u), ages + n.states) +
ages, to = seq(1, ncol(u), ages + n.states) + n.states +
ages - 1, by = 1))
mcstck@stock.n[] <- exp(aperm(array(u[, idxNs], dim = c(realisations,
ages, length(yrs))), perm = c(2, 3, 1)))
mcstck@harvest[] <- exp(aperm(array(u[, idxFs], dim = c(realisations,
n.states, length(yrs))), perm = c(2, 3, 1)))[ctrl@states[names(which(ctrl@fleets ==
0)), ], , ]
mcstck@catch[] <- exp(aperm(array(ca, dim = c(realisations,
length(yrs))), perm = c(2, 1)))
return(mcstck)
}
#stk1 <- monteCarloStock2(Mac,Mac.sam,nits,run.dir="tmp",seed_number = 555)
#stk2 <- monteCarloStock2(Mac,Mac.sam,nits,run.dir="tmp",seed_number = 555)
#identical(stk1,stk2)
#