gCubeSystem
/
ecological-engine-smart-executor
15
0
Fork 0
Code Issues Pull Requests Releases Wiki Activity

git-svn-id: https://svn.d4science.research-infrastructures.eu/gcube/trunk/data-analysis/EcologicalEngineSmartExecutor@113793 82a268e6-3cf1-43bd-a215-b396298e98cf

This commit is contained in:
Gianpaolo Coro 2015-03-30 15:30:41 +00:00
parent e28c508309
commit 2a58553145
37 changed files with 801 additions and 1362 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

696
PARALLEL_PROCESSING/CMSY_22_noplot.R
View File

@ -1,696 +0,0 @@
##--------------------------------------------------------
## CMSY analysis with estimation of total biomass, including Bayesian Schaefer
## written by Rainer Froese with support from Gianpaolo Coro in 2013-2014
## This version adjusts biomass to average biomass over the year
## It also contains the FutureCrash option to improve prediction of final biomass
## Version 21 adds the purple point to indicate the 25th percentile of final biomass
## Version 22 accepts that no biomass or CPUE area available
##--------------------------------------------------------
library(R2jags) # Interface with JAGS
library(coda)
#-----------------------------------------
# Some general settings
#-----------------------------------------
# set.seed(999) # use for comparing results between runs
rm(list=ls(all=TRUE)) # clear previous variables etc
options(digits=3) # displays all numbers with three significant digits as default
graphics.off() # close graphics windows from previous sessions
#-----------------------------------------
# General settings for the analysis
#-----------------------------------------
sigR <- 0.02 # overall process error; 0.05 works reasonable for simulations, 0.02 for real data; 0 if deterministic model
n <- 10000 # initial number of r-k pairs
batch.mode <- T # set to TRUE to suppress graphs
write.output <- T # set to true if table of output is wanted
FutureCrash <- "No"
#-----------------------------------------
# Start output to screen
#-----------------------------------------
cat("-------------------------------------------\n")
cat("Catch-MSY Analysis,", date(),"\n")
cat("-------------------------------------------\n")
#------------------------------------------
# Read data and assign to vectors
#------------------------------------------
# filename_1 <- "AllStocks_Catch4.csv"
# filename_2 <- "AllStocks_ID4.csv"
# filename_1 <- "SimCatch.csv"
# filename_2 <- "SimSpec.csv"
# filename_2 <- "SimSpecWrongS.csv"
# filename_2 <- "SimSpecWrongI.csv"
# filename_2 <- "SimSpecWrongF.csv"
# filename_2 <- "SimSpecWrongH.csv"
# filename_2 <- "SimSpecWrongL.csv"
# filename_1 <- "FishDataLim.csv"
# filename_2 <- "FishDataLimSpec.csv"
filename_1 <- "WKLIFE4Stocks.csv"
filename_2 <- "WKLIFE4ID.csv"
outfile<-"outfile"
outfile.txt <- "outputfile.txt"
cdat <- read.csv(filename_1, header=T, dec=".", stringsAsFactors = FALSE)
cinfo <- read.csv(filename_2, header=T, dec=".", stringsAsFactors = FALSE)
cat("Files", filename_1, ",", filename_2, "read successfully","\n")
# Stocks with total biomass data and catch data from StartYear to EndYear
# stocks <- sort(as.character(cinfo$stock)) # All stocks
stocks<-"HLH_M07"
# select one stock after the other
for(stock in stocks) {
# assign data from cinfo to vectors
res <- as.character(cinfo$Resilience[cinfo$stock==stock])
StartYear <- as.numeric(cinfo$StartYear[cinfo$stock==stock])
EndYear <- as.numeric(cinfo$EndYear[cinfo$stock==stock])
r_low <- as.numeric(cinfo$r_low[cinfo$stock==stock])
r_hi <- as.numeric(cinfo$r_hi[cinfo$stock==stock])
stb_low <- as.numeric(cinfo$stb_low[cinfo$stock==stock])
stb_hi <- as.numeric(cinfo$stb_hi[cinfo$stock==stock])
intyr <- as.numeric(cinfo$intyr[cinfo$stock==stock])
intbio_low <- as.numeric(cinfo$intbio_low[cinfo$stock==stock])
intbio_hi <- as.numeric(cinfo$intbio_hi[cinfo$stock==stock])
endbio_low <- as.numeric(cinfo$endbio_low[cinfo$stock==stock])
endbio_hi <- as.numeric(cinfo$endbio_hi[cinfo$stock==stock])
Btype <- as.character(cinfo$Btype[cinfo$stock==stock])
FutureCrash <- as.character(cinfo$FutureCrash[cinfo$stock==stock])
comment <- as.character(cinfo$comment[cinfo$stock==stock])
# extract data on stock
yr <- as.numeric(cdat$yr[cdat$stock==stock & cdat$yr >= StartYear & cdat$yr <= EndYear])
ct <- as.numeric(cdat$ct[cdat$stock==stock & cdat$yr >= StartYear & cdat$yr <= EndYear])/1000 ## assumes that catch is given in tonnes, transforms to '000 tonnes
if(Btype=="observed" | Btype=="CPUE" | Btype=="simulated") {
bt <- as.numeric(cdat$TB[cdat$stock==stock & cdat$yr >= StartYear & cdat$yr <= EndYear])/1000 ## assumes that biomass is in tonnes, transforms to '000 tonnes
} else {bt <- NA}
nyr <- length(yr) # number of years in the time series
if(Btype!="observed") {bio <- bt}
# change biomass to moving average as assumed by Schaefer (but not for simulations or CPUE)
# for last year use reported bio
if(Btype=="observed") {
ma <- function(x){filter(x,rep(1/2,2),sides=2)}
bio <- ma(bt)
bio[length(bio)] <- bt[length(bt)] }
# initialize vectors for viable r, k, bt
rv.all <- vector()
kv.all <- vector()
btv.all <- matrix(data=vector(),ncol=nyr+1)
#----------------------------------------------------
# Determine initial ranges for parameters and biomass
#----------------------------------------------------
# initial range of r from input file
if(is.na(r_low)==F & is.na(r_hi)==F) {
start_r <- c(r_low,r_hi)
} else {
# initial range of r and CatchMult values based on resilience
if(res == "High") {
start_r <- c(0.6,1.5)} else if(res == "Medium") {
start_r <- c(0.2,0.8)} else if(res == "Low") {
start_r <- c(0.05,0.5)} else { # i.e. res== "Very low"
start_r <- c(0.015,0.1)}
}
# initial range of k values, assuming k will always be larger than max catch
# and max catch will never be smaller than a quarter of MSY
start_k <- c(max(ct),16*max(ct)/start_r[1])
# initial biomass range from input file
if(is.na(stb_low)==F & is.na(stb_hi)==F) {
startbio <- c(stb_low,stb_hi)
} else {
# us low biomass at start as default
startbio <- c(0.1,0.5)
}
MinYear <- yr[which.min(ct)]
MaxYear <- yr[which.max(ct)]
# use year and biomass range for intermediate biomass from input file
if(is.na(intbio_low)==F & is.na(intbio_hi)==F) {
intyr <- intyr
intbio <- c(intbio_low,intbio_hi)
# else if year of minimum catch is at least 3 years away from StartYear and EndYear of series, use min catch
} else if((MinYear - StartYear) > 3 & (EndYear - MinYear) > 3 ) {
# assume that biomass range in year before minimum catch was 0.01 - 0.4
intyr <- MinYear-1
intbio <- c(0.01,0.4)
# else if year of max catch is at least 3 years away from StartYear and EndYear of series, use max catch
} else if((MaxYear - StartYear) > 3 & (EndYear - MaxYear) > 3 ) {
# assume that biomass range in year before maximum catch was 0.3 - 0.9
intyr <- MaxYear-1
intbio <- c(0.3,0.9)
} else {
# assume uninformative range 0-1 in mid-year
intyr <- as.integer(mean(c(StartYear, EndYear)))
intbio <- c(0,1) }
# end of intbio setting
# final biomass range from input file
if(is.na(endbio_low)==F & is.na(endbio_hi)==F) {
endbio <- c(endbio_low,endbio_hi)
} else {
# else use Catch/maxCatch to estimate final biomass
endbio <- if(ct[nyr]/max(ct) > 0.5) {c(0.4,0.8)} else {c(0.01,0.4)}
} # end of final biomass setting
#----------------------------------------------
# MC with Schaefer Function filtering
#----------------------------------------------
Schaefer <- function(ri, ki, startbio, intyr, intbio, endbio, sigR, pt) {
# if stock is not expected to crash within 3 years if last catch continues
if(FutureCrash == "No") {
yr.s <- c(yr,EndYear+1,EndYear+2,EndYear+3)
ct.s <- c(ct,ct[yr==EndYear],ct[yr==EndYear],ct[yr==EndYear])
nyr.s <- length(yr.s)
} else{
yr.s <- yr
ct.s <- ct
nyr.s <- nyr
}
# create vector for initial biomasses
startbt <-seq(from =startbio[1], to=startbio[2], by = (startbio[2]-startbio[1])/10)
# create vectors for viable r, k and bt
rv <- array(-1:-1,dim=c(length(ri)*length(startbt))) #initialize array with -1. The -1 remaining after the process will be removed
kv <- array(-1:-1,dim=c(length(ri)*length(startbt)))
btv <- matrix(data=NA, nrow = (length(ri)*length(startbt)), ncol = nyr+1)
intyr.i <- which(yr.s==intyr) # get index of intermediate year
#loop through r-k pairs
npoints = length(ri)
nstartb = length(startbt)
for(i in 1 : npoints) {
if (i%%1000==0)
cat(".")
# create empty vector for annual biomasses
bt <- vector()
# loop through range of relative start biomasses
for(j in startbt) {
# set initial biomass, including process error
bt[1]=j*ki[i]*exp(rnorm(1,0, sigR)) ## set biomass in first year
#loop through years in catch time series
for(t in 1:nyr.s) { # for all years in the time series
xt=rnorm(1,0, sigR) # set new random process error for every year
# calculate biomass as function of previous year's biomass plus surplus production minus catch
bt[t+1]=(bt[t]+ri[i]*bt[t]*(1-bt[t]/ki[i])-ct.s[t])*exp(xt)
# if biomass < 0.01 k or > 1.1 k, discard r-k pair
if(bt[t+1] < 0.01*ki[i] || bt[t+1] > 1.1*ki[i]) { break } # stop looping through years, go to next upper level
if ((t+1)==intyr.i && (bt[t+1]>(intbio[2]*ki[i]) || bt[t+1]<(intbio[1]*ki[i]))) { break } #intermediate year check
} # end of loop of years
# if last biomass falls without expected ranges goto next r-k pair
if(t < nyr.s || bt[yr.s==EndYear] > (endbio[2]*ki[i]) || bt[yr.s==EndYear] < (endbio[1]*ki[i])) {
next } else {
# store r, k, and bt, plot point, then go to next startbt
rv[((i-1)*nstartb)+j] <- ri[i]
kv[((i-1)*nstartb)+j] <- ki[i]
btv[((i-1)*nstartb)+j,] <- bt[1:(nyr+1)]/ki[i] #substitute a row into the matrix, exclude FutureCrash years
if(pt==T) {points(x=ri[i], y=ki[i], pch=".", cex=2, col="black")
next }
}
} # end of loop of initial biomasses
} # end of loop of r-k pairs
rv=rv[rv!=-1]
kv=kv[kv!=-1]
btv=na.omit(btv) #delete first line
cat("\n")
return(list(rv, kv,btv))
} # end of Schaefer function
#------------------------------------------------------------------
# Uniform sampling of the r-k space
#------------------------------------------------------------------
# get random set of r and k from log space distribution
ri1 = exp(runif(n, log(start_r[1]), log(start_r[2])))
ki1 = exp(runif(n, log(start_k[1]), log(start_k[2])))
#-----------------------------------------------------------------
# Plot data and progress
#-----------------------------------------------------------------
#windows(14,9)
par(mfcol=c(2,3))
# plot catch
plot(x=yr, y=ct, ylim=c(0,1.2*max(ct)), type ="l", bty="l", main=paste(stock,"catch"), xlab="Year",
ylab="Catch", lwd=2)
points(x=yr[which.max(ct)], y=max(ct), col="red", lwd=2)
points(x=yr[which.min(ct)], y=min(ct), col="red", lwd=2)
# plot r-k graph
plot(ri1, ki1, xlim = start_r, ylim = start_k, log="xy", xlab="r", ylab="k", main="Finding viable r-k", pch=".", cex=2, bty="l", col="lightgrey")
#1 - Call MC-Schaefer function to preliminary explore the space without prior information
cat(stock, ": First Monte Carlo filtering of r-k space with ",n," points\n")
MCA <- Schaefer(ri=ri1, ki=ki1, startbio=startbio, intyr=intyr, intbio=intbio, endbio=endbio, sigR=sigR, pt=T)
rv.all <- append(rv.all,MCA[[1]])
kv.all <- append(kv.all,MCA[[2]])
btv.all <- rbind(btv.all,MCA[[3]])
#take viable r and k values
nviablepoints = length(rv.all)
cat("* Found ",nviablepoints," viable points from ",n," samples\n");
#if few points were found then resample and shrink the k log space
if (nviablepoints<=1000){
log.start_k.new <- log(start_k)
max_attempts = 3
current_attempts = 1
while (nviablepoints<=1000 && current_attempts<=max_attempts){
if(nviablepoints > 0) {
log.start_k.new[1] <- mean(c(log.start_k.new[1], min(log(kv.all))))
log.start_k.new[2] <- mean(c(log.start_k.new[2], max(log(kv.all)))) }
n.new=n*current_attempts #add more points
ri1 = exp(runif(n.new, log(start_r[1]), log(start_r[2])))
ki1 = exp(runif(n.new, log.start_k.new[1], log.start_k.new[2]))
cat("Shrinking k space: repeating Monte Carlo in the interval [",exp(log.start_k.new[1]),",",exp(log.start_k.new[2]),"]\n")
cat("Attempt ",current_attempts," of ",max_attempts," with ",n.new," points","\n")
MCA <- Schaefer(ri=ri1, ki=ki1, startbio=startbio, intyr=intyr, intbio=intbio, endbio=endbio, sigR=sigR, pt=T)
rv.all <- append(rv.all,MCA[[1]])
kv.all <- append(kv.all,MCA[[2]])
btv.all <- rbind(btv.all,MCA[[3]])
nviablepoints = length(rv.all) #recalculate viable points
cat("* Found altogether",nviablepoints," viable points \n");
current_attempts=current_attempts+1 #increment the number of attempts
}
}
# If tip of viable r-k pairs is 'thin', do extra sampling there
gm.rv = exp(mean(log(rv.all)))
if(length(rv.all[rv.all > 0.9*start_r[2]]) < 10) {
l.sample.r <- (gm.rv + max(rv.all))/2
cat("Final sampling in the tip area above r =",l.sample.r,"\n")
log.start_k.new <- c(log(0.8*min(kv.all)),log(max(kv.all[rv.all > l.sample.r])))
ri1 = exp(runif(50000, log(l.sample.r), log(start_r[2])))
ki1 = exp(runif(50000, log.start_k.new[1], log.start_k.new[2]))
MCA <- Schaefer(ri=ri1, ki=ki1, startbio=startbio, intyr=intyr, intbio=intbio, endbio=endbio, sigR=sigR, pt=T)
rv.all <- append(rv.all,MCA[[1]])
kv.all <- append(kv.all,MCA[[2]])
btv.all <- rbind(btv.all,MCA[[3]])
nviablepoints = length(rv.all) #recalculate viable points
cat("Found altogether", length(rv.all), "unique viable r-k pairs and biomass trajectories\n")
}
# ------------------------------------------------------------
# Bayesian analysis of catch & biomass with Schaefer model
# ------------------------------------------------------------
if(Btype == "observed" | Btype=="simulated") {
cat("Running Schaefer MCMC analysis....\n")
mcmc.burn <- as.integer(30000)
mcmc.chainLength <- as.integer(60000) # burn-in plus post-burn
mcmc.thin = 10 # to reduce autocorrelation
mcmc.chains = 3 # needs to be at least 2 for DIC
# Parameters to be returned by JAGS
jags.save.params=c('r','k','sigma.b', 'alpha', 'sigma.r') #
# JAGS model
Model = "model{
# to avoid crash due to 0 values
eps<-0.01
# set a quite narrow variation from the expected value
sigma.b <- 1/16
tau.b <- pow(sigma.b,-2)
Bm[1] <- log(alpha*k)
bio[1] ~ dlnorm(Bm[1],tau.b)
for (t in 2:nyr){
bio[t] ~ dlnorm(Bm[t],tau.b)
Bm[t] <- log(max(bio[t-1] + r*bio[t-1]*(1 - (bio[t-1])/k) - ct[t-1], eps))
}
# priors
alpha ~ dunif(0.01,1) # needed for fit of first biomass
#inverse cubic root relationship between the range of viable r and the size of the search space
inverseRangeFactor <- 1/((start_r[2]-start_r[1])^1/3)
# give sigma some variability in the inverse relationship
sigma.r ~ dunif(0.001*inverseRangeFactor,0.02*inverseRangeFactor)
tau.r <- pow(sigma.r,-2)
rm <- log((start_r[1]+start_r[2])/2)
r ~ dlnorm(rm,tau.r)
# search in the k space from the center of the range. Allow high variability
km <- log((start_k[1]+start_k[2])/2)
tau.k <- pow(km,-2)
k ~ dlnorm(km,tau.k)
#end model
}"
# Write JAGS model to file
cat(Model, file="r2jags.bug")
### random seed
set.seed(runif(1,1,500)) # needed in JAGS
### run model
jags_outputs <- jags(data=c('ct','bio','nyr', 'start_r', 'start_k'),
working.directory=NULL, inits=NULL,
parameters.to.save= jags.save.params,
model.file="r2jags.bug", n.chains = mcmc.chains,
n.burnin = mcmc.burn, n.thin = mcmc.thin, n.iter = mcmc.chainLength,
refresh=mcmc.burn/20, )
# ------------------------------------------------------
# Results from JAGS Schaefer
# ------------------------------------------------------
r_out <- as.numeric(mcmc(jags_outputs$BUGSoutput$sims.list$r))
k_out <- as.numeric(mcmc(jags_outputs$BUGSoutput$sims.list$k))
## sigma_out <- as.numeric(mcmc(jags_outputs$BUGSoutput$sims.list$sigma.b))
alpha_out <- as.numeric(mcmc(jags_outputs$BUGSoutput$sims.list$alpha))
## sigma.r_out <- as.numeric(mcmc(jags_outputs$BUGSoutput$sims.list$sigma.r))
mean.log.r.jags <- mean(log(r_out))
SD.log.r.jags <- sd(log(r_out))
lcl.log.r.jags <- mean.log.r.jags-1.96*SD.log.r.jags
ucl.log.r.jags <- mean.log.r.jags+1.96*SD.log.r.jags
gm.r.jags <- exp(mean.log.r.jags)
lcl.r.jags <- exp(lcl.log.r.jags)
ucl.r.jags <- exp(ucl.log.r.jags)
mean.log.k.jags <- mean(log(k_out))
SD.log.k.jags <- sd(log(k_out))
lcl.log.k.jags <- mean.log.k.jags-1.96*SD.log.k.jags
ucl.log.k.jags <- mean.log.k.jags+1.96*SD.log.k.jags
gm.k.jags <- exp(mean.log.k.jags)
lcl.k.jags <- exp(lcl.log.k.jags)
ucl.k.jags <- exp(ucl.log.k.jags)
mean.log.MSY.jags<- mean(log(r_out)+log(k_out)-log(4))
SD.log.MSY.jags <- sd(log(r_out)+log(k_out)-log(4))
gm.MSY.jags <- exp(mean.log.MSY.jags)
lcl.MSY.jags <- exp(mean.log.MSY.jags-1.96*SD.log.MSY.jags)
ucl.MSY.jags <- exp(mean.log.MSY.jags+1.96*SD.log.MSY.jags)
} # end of MCMC Schaefer loop
#------------------------------------
# get results from CMSY
#------------------------------------
# get estimate of most probable r as median of mid log.r-classes above cut-off
# get remaining viable log.r and log.k
rem.log.r <- log(rv.all[rv.all > gm.rv])
rem.log.k <- log(kv.all[rv.all>gm.rv])
# get vectors with numbers of r and mid values in about 25 classes
hist.log.r <- hist(x=rem.log.r, breaks=25, plot=F)
log.r.counts <- hist.log.r$counts
log.r.mids <- hist.log.r$mids
# get most probable log.r as mean of mids with counts > 0
log.r.est <- median(log.r.mids[which(log.r.counts > 0)])
lcl.log.r <- as.numeric(quantile(x=log.r.mids[which(log.r.counts > 0)], 0.025))
ucl.log.r <- as.numeric(quantile(x=log.r.mids[which(log.r.counts > 0)], 0.975))
r.est <- exp(log.r.est)
lcl.r.est <- exp(lcl.log.r)
ucl.r.est <- exp(ucl.log.r)
# do linear regression of log k ~ log r with slope fixed to -1 (from Schaefer)
reg <- lm(rem.log.k ~ 1 + offset(-1*rem.log.r))
int.reg <- as.numeric(reg[1])
sd.reg <- sd(resid(reg))
se.reg <- summary(reg)$coefficients[2]
# get estimate of log(k) from y where x = log.r.est
log.k.est <- int.reg + (-1) * log.r.est
# get estimates of CL of log.k.est from y +/- SD where x = lcl.log r or ucl.log.r
lcl.log.k <- int.reg + (-1) * ucl.log.r - sd.reg
ucl.log.k <- int.reg + (-1) * lcl.log.r + sd.reg
k.est <- exp(log.k.est)
lcl.k.est <- exp(lcl.log.k)
ucl.k.est <- exp(ucl.log.k)
# get MSY from remaining log r-k pairs
log.MSY.est <- mean(rem.log.r + rem.log.k - log(4))
sd.log.MSY.est <- sd(rem.log.r + rem.log.k - log(4))
lcl.log.MSY.est <- log.MSY.est - 1.96*sd.log.MSY.est
ucl.log.MSY.est <- log.MSY.est + 1.96*sd.log.MSY.est
MSY.est <- exp(log.MSY.est)
lcl.MSY.est <- exp(lcl.log.MSY.est)
ucl.MSY.est <- exp(ucl.log.MSY.est)
# get predicted biomass vectors as median and quantiles of trajectories
median.btv <- apply(btv.all,2, median)
lastyr.bio <- median.btv[length(median.btv)-1]
nextyr.bio <- median.btv[length(median.btv)]
lcl.btv <- apply(btv.all,2, quantile, probs=0.025)
q.btv <- apply(btv.all,2, quantile, probs=0.25)
ucl.btv <- apply(btv.all,2, quantile, probs=0.975)
lcl.lastyr.bio <- lcl.btv[length(lcl.btv)-1]
ucl.lastyr.bio <- ucl.btv[length(lcl.btv)-1]
lcl.nextyr.bio <- lcl.btv[length(lcl.btv)]
ucl.nextyr.bio <- ucl.btv[length(lcl.btv)]
# -----------------------------------------
# Plot results
# -----------------------------------------
# Analysis of viable r-k pairs
plot(x=rv.all, y=kv.all, xlim=start_r,
ylim=c(0.9*min(kv.all, ifelse(Btype == "observed",k_out,NA), na.rm=T), 1.1*max(kv.all)),
pch=16, col="grey",log="xy", bty="l",
xlab="r", ylab="k", main="Analysis of viable r-k")
abline(v=gm.rv, lty="dashed")
# plot points and best estimate from full Schaefer analysis
if(Btype == "observed"|Btype=="simulated") {
# plot r-k pairs from MCMC
points(x=r_out, y=k_out, pch=16,cex=0.5)
# plot best r-k pair from MCMC
points(x=gm.r.jags, y=gm.k.jags, pch=19, col="green")
lines(x=c(lcl.r.jags, ucl.r.jags),y=c(gm.k.jags,gm.k.jags), col="green")
lines(x=c(gm.r.jags,gm.r.jags),y=c(lcl.k.jags, ucl.k.jags), col="green")
}
# if data are from simulation, plot true r and k
if(Btype=="simulated") {
l.stock <- nchar(stock) # get length of sim stock name
r.char <- substr(stock,l.stock-1,l.stock) # get last character of sim stock name
r.sim <- NA # initialize vector for r used in simulation
if(r.char=="_H") {r.sim=1; lcl.r.sim=0.8; ucl.r.sim=1.25} else
if(r.char=="_M") {r.sim=0.5;lcl.r.sim=0.4;ucl.r.sim=0.62} else
if(r.char=="_L") {r.sim=0.25;lcl.r.sim=0.2;ucl.r.sim=0.31} else {r.sim=0.05;lcl.r.sim=0.04;ucl.r.sim=0.062}
# plot true r-k point with error bars
points(x=r.sim, y=1000, pch=19, col="red")
# add +/- 20% error bars
lines(x=c(lcl.r.sim,ucl.r.sim), y=c(1000,1000), col="red")
lines(x=c(r.sim,r.sim), y=c(800,1250), col="red")
}
# plot blue dot for proposed r-k, with 95% CL lines
points(x=r.est, y=k.est, pch=19, col="blue")
lines(x=c(lcl.r.est, ucl.r.est),y=c(k.est,k.est), col="blue")
lines(x=c(r.est,r.est),y=c(lcl.k.est, ucl.k.est), col="blue")
# plot biomass graph
# determine k to use for red line in b/k plot
if(Btype=="simulated") {k2use <- 1000} else
if(Btype == "observed") {k2use <- gm.k.jags} else {k2use <- k.est}
# determine hight of y-axis in plot
max.y <- max(c(bio/k2use,ucl.btv,0.6,startbio[2], intbio[2],endbio[2]),na.rm=T)
plot(x=yr,y=median.btv[1:nyr], lwd=2, xlab="Year", ylab="Relative biomass b/k", type="l",
ylim=c(0,max.y), bty="l", main=paste("Pred. biomass vs ", Btype,sep=""))
lines(x=yr, y=lcl.btv[1:nyr],type="l")
lines(x=yr, y=ucl.btv[1:nyr],type="l")
points(x=EndYear,y=q.btv[yr==EndYear], col="purple", cex=1.5, lwd=2)
abline(h=0.5, lty="dashed")
abline(h=0.25, lty="dotted")
lines(x=c(yr[1],yr[1]), y=startbio, col="blue")
lines(x=c(intyr,intyr), y=intbio, col="blue")
lines(x=c(max(yr),max(yr)), y=endbio, col="blue")
# if observed biomass is available, plot red biomass line
if(Btype == "observed"|Btype=="simulated") {
lines(x=yr, y=bio/k2use,type="l", col="red", lwd=1)
}
# if CPUE data are available, scale to predicted biomass range, plot red biomass line
if(Btype == "CPUE") {
par(new=T) # prepares for new plot on top of previous
plot(x=yr, y=bio, type="l", col="red", lwd=1,
ann=F,axes=F,ylim=c(0,1.2*max(bio, na.rm=T))) # forces this plot on top of previous one
axis(4, col="red", col.axis="red")
}
# plot yield and biomass against equilibrium surplus parabola
max.y <-max(c(ct/MSY.est,ifelse(Btype=="observed"|Btype=="simulated",ct/gm.MSY.jags,NA),1.2),na.rm=T)
# plot parabola
x=seq(from=0,to=2,by=0.001)
y=4*x-(2*x)^2
plot(x=x, y=y, xlim=c(0,1), ylim=c(0,max.y), type="l", bty="l",xlab="Relative biomass b/k",
ylab="Catch / MSY", main="Equilibrium curve")
# plot catch against CMSY biomass estimates
points(x=median.btv[1:nyr], y=ct/MSY.est, pch=16, col="grey")
points(x=q.btv[yr==EndYear],y=ct[yr==EndYear]/MSY.est, col="purple", cex=1.5, lwd=2)
# plot catch against observed biomass or CPUE
if(Btype == "observed"|Btype=="simulated") {
points(x=bio/k2use, y=ct/gm.MSY.jags, pch=16, cex=0.5)
}
# plot exploitation rate u against u.msy
# get u derived from predicted CMSY biomass
u.CMSY <- ct/(median.btv[1:nyr]*k.est)
u.msy.CMSY <- 1-exp(-r.est/2) # # Fmsy from CMSY expressed as exploitation rate
# get u from observed or simulated biomass
if(Btype == "observed"|Btype=="simulated") {
u.bio <- ct/bio
u.msy.bio <- 1-exp(-gm.r.jags/2)
}
# get u from CPUE
if(Btype == "CPUE") {
q=max(median.btv[1:nyr][is.na(bio)==F],na.rm=T)*k.est/max(bio,na.rm=T)
u.CPUE <- ct/(q*bio)
}
# determine upper bound of Y-axis
max.y <- max(c(1.5, 1.2*u.CMSY/u.msy.CMSY,ct[yr==EndYear]/(q.btv[yr==EndYear]*k.est)/u.msy.CMSY,
ifelse(Btype=="observed"|Btype=="simulated",max(u.bio[is.na(u.bio)==F]/u.msy.bio),0),
na.rm=T))
# plot u from CMSY
plot(x=yr,y=u.CMSY/u.msy.CMSY, type="l", bty="l", ylim=c(0,max.y), xlab="Year",
ylab="u / u_msy", main="Exploitation rate")
abline(h=1, lty="dashed")
points(x=EndYear,y=ct[yr==EndYear]/(q.btv[yr==EndYear]*k.est)/u.msy.CMSY, col="purple", cex=1.5, lwd=2)
# plot u from biomass
if(Btype == "observed"|Btype=="simulated") lines(x=yr, y=u.bio/u.msy.bio, col="red")
# plot u from CPUE
if(Btype == "CPUE") {
par(new=T) # prepares for new plot on top of previous
plot(x=yr, y=u.CPUE, type="l", col="red", ylim=c(0, 1.2*max(u.CPUE,na.rm=T)),ann=F,axes=F)
axis(4, col="red", col.axis="red")
}
if(batch.mode == TRUE) {dev.off()} # close plot window
# ------------------------------------------
# print input and results to screen
cat("---------------------------------------\n")
cat("Species:", cinfo$ScientificName[cinfo$stock==stock], "\n")
cat("Name and region:", cinfo$EnglishName[cinfo$stock==stock], ",", cinfo$Name[cinfo$stock==stock], "\n")
cat("Stock:",stock,"\n")
cat("Catch data used from years", min(yr),"-", max(yr), "\n")
cat("Prior initial relative biomass =", startbio[1], "-", startbio[2], "\n")
cat("Prior intermediate rel. biomass=", intbio[1], "-", intbio[2], "in year", intyr, "\n")
cat("Prior final relative biomass =", endbio[1], "-", endbio[2], "\n")
cat("If current catches continue, is the stock likely to crash within 3 years?",FutureCrash,"\n")
cat("Prior range for r =", format(start_r[1],digits=2), "-", format(start_r[2],digits=2),
", prior range for k =", start_k[1], "-", start_k[2],"\n")
# if data are simulated, print true r-k
if(filename_1=="SimCatch.csv") {
cat("True r =", r.sim, "(because input data were simulated with Schaefer model)\n")
cat("True k = 1000 \n")
cat("True MSY =", 1000*r.sim/4,"\n")
cat("True biomass in last year =",bio[length(bio)],"or",bio[length(bio)]/1000,"k \n")
cat("True mean catch / MSY ratio =", mean(ct)/(1000*r.sim/4),"\n")
}
# print results from full Schaefer if available
if(Btype == "observed"|Btype=="simulated") {
cat("Results from Bayesian Schaefer model using catch & biomass (",Btype,")\n")
cat("MSY =", gm.MSY.jags,", 95% CL =", lcl.MSY.jags, "-", ucl.MSY.jags,"\n")
cat("Mean catch / MSY =", mean(ct)/gm.MSY.jags,"\n")
if(Btype != "CPUE") {
cat("r =", gm.r.jags,", 95% CL =", lcl.r.jags, "-", ucl.r.jags,"\n")
cat("k =", gm.k.jags,", 95% CL =", lcl.k.jags, "-", ucl.k.jags,"\n")
}
}
# results of CMSY analysis
cat("Results of CMSY analysis \n")
cat("Altogether", nviablepoints,"unique viable r-k pairs were found \n")
cat(nviablepoints-length(rem.log.r),"r-k pairs above the initial geometric mean of r =", gm.rv, "were analysed\n")
cat("r =", r.est,", 95% CL =", lcl.r.est, "-", ucl.r.est,"\n")
cat("k =", k.est,", 95% CL =", lcl.k.est, "-", ucl.k.est,"\n")
cat("MSY =", MSY.est,", 95% CL =", lcl.MSY.est, "-", ucl.MSY.est,"\n")
cat("Predicted biomass in last year =", lastyr.bio, "2.5th perc =", lcl.lastyr.bio,
"97.5th perc =", ucl.lastyr.bio,"\n")
cat("Predicted biomass in next year =", nextyr.bio, "2.5th perc =", lcl.nextyr.bio,
"97.5th perc =", ucl.nextyr.bio,"\n")
cat("----------------------------------------------------------\n")
## Write some results into outfile
if(write.output == TRUE) {
# write data into csv file
output = data.frame(cinfo$ScientificName[cinfo$stock==stock], stock, StartYear, EndYear, mean(ct)*1000,
ifelse(Btype=="observed"|Btype=="simulate",bio[length(bio)],NA), # last biomass on record
ifelse(Btype == "observed"|Btype=="simulated",gm.MSY.jags,NA), # full Schaefer
ifelse(Btype == "observed"|Btype=="simulated",lcl.MSY.jags,NA),
ifelse(Btype == "observed"|Btype=="simulated",ucl.MSY.jags,NA),
ifelse(Btype == "observed"|Btype=="simulated",gm.r.jags,NA),
ifelse(Btype == "observed"|Btype=="simulated",lcl.r.jags,NA),
ifelse(Btype == "observed"|Btype=="simulated",ucl.r.jags,NA),
ifelse(Btype == "observed"|Btype=="simulated",gm.k.jags,NA),
ifelse(Btype == "observed"|Btype=="simulated",lcl.k.jags,NA),
ifelse(Btype == "observed"|Btype=="simulated",ucl.k.jags,NA),
r.est, lcl.r.est, ucl.r.est, # CMSY r
k.est, lcl.k.est, ucl.k.est, # CMSY k
MSY.est, lcl.MSY.est, ucl.MSY.est, # CMSY r
lastyr.bio, lcl.lastyr.bio, ucl.lastyr.bio, # last year bio
nextyr.bio, lcl.nextyr.bio, ucl.nextyr.bio)# last year + 1 bio
write.table(output, file=outfile, append = T, sep = ",",
dec = ".", row.names = FALSE, col.names = FALSE)
# write some text into text outfile.txt
cat("Species:", cinfo$ScientificName[cinfo$stock==stock], "\n",
"Name:", cinfo$EnglishName[cinfo$stock==stock], "\n",
"Region:", cinfo$Name[cinfo$stock==stock], "\n",
"Stock:",stock,"\n",
"Catch data used from years", min(yr),"-", max(yr),", biomass =", Btype, "\n",
"Prior initial relative biomass =", startbio[1], "-", startbio[2], "\n",
"Prior intermediate rel. biomass=", intbio[1], "-", intbio[2], "in year", intyr, "\n",
"Prior final relative biomass =", endbio[1], "-", endbio[2], "\n",
"Future crash with current catches?", FutureCrash, "\n",
"Prior range for r =", format(start_r[1],digits=2), "-", format(start_r[2],digits=2),
", prior range for k =", start_k[1], "-", start_k[2],"\n",
file=outfile.txt,append=T)
if(filename_1=="SimCatch.csv") {
cat(" True r =", r.sim, "(because input data were simulated with Schaefer model)\n",
"True k = 1000, true MSY =", 1000*r.sim/4,"\n",
"True biomass in last year =",bio[length(bio)],"or",bio[length(bio)]/1000,"k \n",
"True mean catch / MSY ratio =", mean(ct)/(1000*r.sim/4),"\n",
file=outfile.txt,append=T)
}
if(Btype == "observed"|Btype=="simulated") {
cat(" Results from Bayesian Schaefer model using catch & biomass \n",
"r =", gm.r.jags,", 95% CL =", lcl.r.jags, "-", ucl.r.jags,"\n",
"k =", gm.k.jags,", 95% CL =", lcl.k.jags, "-", ucl.k.jags,"\n",
"MSY =", gm.MSY.jags,", 95% CL =", lcl.MSY.jags, "-", ucl.MSY.jags,"\n",
"Mean catch / MSY =", mean(ct)/gm.MSY.jags,"\n",
file=outfile.txt,append=T)
}
cat(" Results of CMSY analysis with altogether", nviablepoints,"unique viable r-k pairs \n",
nviablepoints-length(rem.log.r),"r-k pairs above the initial geometric mean of r =", gm.rv, "were analysed\n",
"r =", r.est,", 95% CL =", lcl.r.est, "-", ucl.r.est,"\n",
"k =", k.est,", 95% CL =", lcl.k.est, "-", ucl.k.est,"\n",
"MSY =", MSY.est,", 95% CL =", lcl.MSY.est, "-", ucl.MSY.est,"\n",
"Predicted biomass last year b/k =", lastyr.bio, "2.5th perc b/k =", lcl.lastyr.bio,
"97.5th perc b/k =", ucl.lastyr.bio,"\n",
"Precautionary 25th percentile b/k =",q.btv[yr==EndYear],"\n",
"----------------------------------------------------------\n",
file=outfile.txt,append=T)
}
} # end of stocks loop

119
PARALLEL_PROCESSING/CMSY_22_noplot.R.test
View File

@ -1,119 +0,0 @@
##--------------------------------------------------------
## CMSY analysis with estimation of total biomass, including Bayesian Schaefer
## written by Rainer Froese with support from Gianpaolo Coro in 2013-2014
## This version adjusts biomass to average biomass over the year
## It also contains the FutureCrash option to improve prediction of final biomass
## Version 21 adds the purple point to indicate the 25th percentile of final biomass
## Version 22 accepts that no biomass or CPUE area available
##--------------------------------------------------------
library(R2jags) # Interface with JAGS
library(coda)
#-----------------------------------------
# Some general settings
#-----------------------------------------
# set.seed(999) # use for comparing results between runs
rm(list=ls(all=TRUE)) # clear previous variables etc
options(digits=3) # displays all numbers with three significant digits as default
graphics.off() # close graphics windows from previous sessions
#-----------------------------------------
# General settings for the analysis
#-----------------------------------------
sigR <- 0.02 # overall process error; 0.05 works reasonable for simulations, 0.02 for real data; 0 if deterministic model
n <- 10000 # initial number of r-k pairs
batch.mode <- T # set to TRUE to suppress graphs
write.output <- T # set to true if table of output is wanted
FutureCrash <- "No"
#-----------------------------------------
# Start output to screen
#-----------------------------------------
cat("-------------------------------------------\n")
cat("Catch-MSY Analysis,", date(),"\n")
cat("-------------------------------------------\n")
#------------------------------------------
# Read data and assign to vectors
#------------------------------------------
# filename_1 <- "AllStocks_Catch4.csv"
# filename_2 <- "AllStocks_ID4.csv"
# filename_1 <- "SimCatch.csv"
# filename_2 <- "SimSpec.csv"
# filename_2 <- "SimSpecWrongS.csv"
# filename_2 <- "SimSpecWrongI.csv"
# filename_2 <- "SimSpecWrongF.csv"
# filename_2 <- "SimSpecWrongH.csv"
# filename_2 <- "SimSpecWrongL.csv"
# filename_1 <- "FishDataLim.csv"
# filename_2 <- "FishDataLimSpec.csv"
filename_1 <- "WKLIFE4Stocks.csv"
filename_2 <- "WKLIFE4ID.csv"
outfile<-"outfile"
outfile.txt <- "outputfile.txt"
cdat <- read.csv(filename_1, header=T, dec=".", stringsAsFactors = FALSE)
cinfo <- read.csv(filename_2, header=T, dec=".", stringsAsFactors = FALSE)
cat("Files", filename_1, ",", filename_2, "read successfully","\n")
# Stocks with total biomass data and catch data from StartYear to EndYear
# stocks <- sort(as.character(cinfo$stock)) # All stocks
stocks<-"HLH_M07"
# select one stock after the other
for(stock in stocks) {
# assign data from cinfo to vectors
res <- as.character(cinfo$Resilience[cinfo$stock==stock])
StartYear <- as.numeric(cinfo$StartYear[cinfo$stock==stock])
EndYear <- as.numeric(cinfo$EndYear[cinfo$stock==stock])
r_low <- as.numeric(cinfo$r_low[cinfo$stock==stock])
r_hi <- as.numeric(cinfo$r_hi[cinfo$stock==stock])
stb_low <- as.numeric(cinfo$stb_low[cinfo$stock==stock])
stb_hi <- as.numeric(cinfo$stb_hi[cinfo$stock==stock])
intyr <- as.numeric(cinfo$intyr[cinfo$stock==stock])
intbio_low <- as.numeric(cinfo$intbio_low[cinfo$stock==stock])
intbio_hi <- as.numeric(cinfo$intbio_hi[cinfo$stock==stock])
endbio_low <- as.numeric(cinfo$endbio_low[cinfo$stock==stock])
endbio_hi <- as.numeric(cinfo$endbio_hi[cinfo$stock==stock])
Btype <- as.character(cinfo$Btype[cinfo$stock==stock])
FutureCrash <- as.character(cinfo$FutureCrash[cinfo$stock==stock])
comment <- as.character(cinfo$comment[cinfo$stock==stock])
# extract data on stock
yr <- as.numeric(cdat$yr[cdat$stock==stock & cdat$yr >= StartYear & cdat$yr <= EndYear])
ct <- as.numeric(cdat$ct[cdat$stock==stock & cdat$yr >= StartYear & cdat$yr <= EndYear])/1000 ## assumes that catch is given in tonnes, transforms to '000 tonnes
if(Btype=="observed" | Btype=="CPUE" | Btype=="simulated") {
bt <- as.numeric(cdat$TB[cdat$stock==stock & cdat$yr >= StartYear & cdat$yr <= EndYear])/1000 ## assumes that biomass is in tonnes, transforms to '000 tonnes
} else {bt <- NA}
nyr <- length(yr) # number of years in the time series
cat("->---------------------------------------
Species: NA
Name and region: NA , NA
Stock: HLH_M07
Catch data used from years 1 - 50
Prior initial relative biomass = 0.5 - 0.9
Prior intermediate rel. biomass= 0.01 - 0.4 in year 25
Prior final relative biomass = 0.4 - 0.8
If current catches continue, is the stock likely to crash within 3 years? No
Prior range for r = 0.2 - 0.8 , prior range for k = 125 - 9965
Results from Bayesian Schaefer model using catch & biomass ( simulated )
MSY = 91.7 , 95% CL = 83.9 - 100
Mean catch / MSY = 0.882
r = 0.425 , 95% CL = 0.374 - 0.483
k = 863 , 95% CL = 783 - 951
Results of CMSY analysis
Altogether 2055 unique viable r-k pairs were found
1142 r-k pairs above the initial geometric mean of r = 0.343 were analysed
r = 0.522 , 95% CL = 0.349 - 0.782
k = 683 , 95% CL = 438 - 1067
MSY = 89.2 , 95% CL = 82.2 - 96.7
Predicted biomass in last year = 0.676 2.5th perc = 0.435 97.5th perc = 0.768
Predicted biomass in next year = 0.673 2.5th perc = 0.433 97.5th perc = 0.758
----------------------------------------------------------
",file=outfile.txt,append=T)
}

178
PARALLEL_PROCESSING/CatchMSY_Dec2014.R Normal file
View File

@ -0,0 +1,178 @@
set.seed(999) ## for same random sequence
#require(hacks)
#setwd("C:/Users/Ye/Documents/Data poor fisheries/Martell Froese Method/")
## Read Data for stock, year=yr, catch=ct, and resilience=res. Expects space delimited file with header yr ct and years in integer and catch in real with decimal point
## For example
## stock res yr ct
## cap-icel Medium 1984 1234.32
## filename <- "RAM_MSY.csv"
##filename <- "ICESct2.csv"
cat("Step 1","\n")
TestRUN <- F # if it is true, just run on the test samples, false will go for a formal run!
filename <- "D20.csv"
outfile <- "CatchMSY_Output.csv"
outfile2 <- paste("NonProcessedSpecies.csv",sep="")
#cdat <- read.csv2(filename, header=T, dec=".")
cdat1 <- read.csv(filename)
cat("\n", "File", filename, "read successfully","\n")
cat("Step 2","\n")
if(file.exists("cdat.RData"))
{load("cdat.RData")} else
{
dim(cdat1)
yrs=1950:2012
# to set NA as 0
cdat1[is.na(cdat1)] <- 0
nrow <- length(cdat1[,1])
ndatColn <- length(cdat1[1,c(-1:-12)])
rownames(cdat1) <- NULL
cdat <- NULL
for(i in 1:nrow)
{#i=1
#a <- ctotal3[i,-1]
tmp=data.frame(stock=rep(as.character(cdat1[i,"Stock_ID"]),ndatColn),
species=rep(as.character(cdat1[i,"Scientific_name"]),ndatColn),
yr=yrs,ct=unlist(c(cdat1[i,c(-1:-12)])),
res=rep(cdat1[i,"ResilienceIndex"],ndatColn))
cdat <- rbind(cdat,tmp)
#edit(cdat)
}
}
StockList=unique(as.character(cdat$stock))
colnames(cdat)
#stock_id <- unique(as.character(cdat$stock))
#??
# stock_id <- "cod-2224" ## for selecting individual stocks
# stock=stock_id
#??
cat("Step 3","\n")
## FUNCTIONS are going to be used subsequently
.schaefer <- function(theta)
{
with(as.list(theta), { ## for all combinations of ri & ki
bt=vector()
ell = 0 ## initialize ell
J=0 #Ye
for (j in startbt)
{
if(ell == 0)
{
bt[1]=j*k*exp(rnorm(1,0, sigR)) ## set biomass in first year
for(i in 1:nyr) ## for all years in the time series
{
xt=rnorm(1,0, sigR)
bt[i+1]=(bt[i]+r*bt[i]*(1-bt[i]/k)-ct[i])*exp(xt)
## calculate biomass as function of previous year's biomass plus net production minus catch
}
#Bernoulli likelihood, assign 0 or 1 to each combination of r and k
ell = 0
if(bt[nyr+1]/k>=lam1 && bt[nyr+1]/k <=lam2 && min(bt) > 0 && max(bt) <=k && bt[which(yr==interyr)]/k>=interbio[1] && bt[which(yr==interyr)]/k<=interbio[2])
ell = 1
J=j # Ye
}
}
return(list(ell=ell,J=J)) # Ye adding J=J
})
}
sraMSY <-function(theta, N)
{
#This function conducts the stock reduction
#analysis for N trials
#args:
# theta - a list object containing:
# r (lower and upper bounds for r)
# k (lower and upper bounds for k)
# lambda (limits for current depletion)
with(as.list(theta),
{
ri = exp(runif(N, log(r[1]), log(r[2]))) ## get N values between r[1] and r[2], assign to ri
ki = exp(runif(N, log(k[1]), log(k[2]))) ## get N values between k[1] and k[2], assing to ki
itheta=cbind(r=ri,k=ki, lam1=lambda[1],lam2=lambda[2], sigR=sigR)
## assign ri, ki, and final biomass range to itheta
M = apply(itheta,1,.schaefer) ## call Schaefer function with parameters in itheta
i=1:N
## prototype objective function
get.ell=function(i) M[[i]]$ell
ell = sapply(i, get.ell)
get.J=function(i) M[[i]]$J # Ye
J=sapply(i,get.J) # Ye
return(list(r=ri,k=ki, ell=ell, J=J)) # Ye adding J=J
})
}
getBiomass <- function(r, k, j)
{
BT <- NULL
bt=vector()
for (v in 1:length(r))
{
bt[1]=j[v]*k[v]*exp(rnorm(1,0, sigR)) ## set biomass in first year
for(i in 1:nyr) ## for all years in the time series
{
xt=rnorm(1,0, sigR)
bt[i+1]=(bt[i]+r[v]*bt[i]*(1-bt[i]/k[v])-ct[i])*exp(xt)
## calculate biomass as function of previous year's biomass plus net production minus catch
}
BT=rbind(BT, t(t(bt)))
}
return(BT)
}
## The End of Functions section
cat("Step 4","\n")
stockLoop <- StockList
# randomly select stocks from randomly selected 5 area codes first
if(TestRUN)
{
set.seed(999)
AreaCodeList <- unique(cdat1$AREA_Code)
sampledAC <- sample(AreaCodeList,size=5,replace=F)
stockLoop <- cdat1[cdat1$AREA_Code %in% sampledAC,c("Stock_ID")]
}
#setup counters
counter1 <- 0
counter2 <- 0
cat("Step 4","\n")
## Loop through stocks
#write.table("x",file=outfile,append = FALSE, row.names = FALSE,col.names=FALSE,sep=",")
write.table("x",file=outfile2,append = FALSE, row.names = FALSE,col.names=FALSE,sep=",")
for(stock in stockLoop)
{
t0<-Sys.time()
xr <- runif(1, 1.0, 10000)
x1<-c(paste("processed",xr,sep=","))
xr <- runif(1, 1.0, 10000)
x2<-c(paste("non processed",xr,sep=","))
#write.table(x1,file=outfile,append = T, row.names = FALSE,col.names=FALSE,sep=",")
write.table(x2,file=outfile2,append = T, row.names = FALSE,col.names=FALSE,sep=",")
cat("Elapsed: ",Sys.time()-t0," \n")
}

440
PARALLEL_PROCESSING/CatchMSY_Dec2014_orig.R Normal file
View File

@ -0,0 +1,440 @@
set.seed(999) ## for same random sequence
#require(hacks)
#setwd("C:/Users/Ye/Documents/Data poor fisheries/Martell Froese Method/")
## Read Data for stock, year=yr, catch=ct, and resilience=res. Expects space delimited file with header yr ct and years in integer and catch in real with decimal point
## For example
## stock res yr ct
## cap-icel Medium 1984 1234.32
## filename <- "RAM_MSY.csv"
##filename <- "ICESct2.csv"
cat("Step 1","\n")
TestRUN <- F # if it is true, just run on the test samples, false will go for a formal run!
filename <- "D20.csv"
outfile <- "CatchMSY_Output.csv"
outfile2 <- paste("NonProcessedSpecies.csv",sep="")
#cdat <- read.csv2(filename, header=T, dec=".")
cdat1 <- read.csv(filename)
cat("\n", "File", filename, "read successfully","\n")
cat("Step 2","\n")
if(file.exists("cdat.RData"))
{load("cdat.RData")} else
{
dim(cdat1)
yrs=1950:2012
# to set NA as 0
cdat1[is.na(cdat1)] <- 0
nrow <- length(cdat1[,1])
ndatColn <- length(cdat1[1,c(-1:-12)])
rownames(cdat1) <- NULL
cdat <- NULL
for(i in 1:nrow)
{#i=1
#a <- ctotal3[i,-1]
tmp=data.frame(stock=rep(as.character(cdat1[i,"Stock_ID"]),ndatColn),
species=rep(as.character(cdat1[i,"Scientific_name"]),ndatColn),
yr=yrs,ct=unlist(c(cdat1[i,c(-1:-12)])),
res=rep(cdat1[i,"ResilienceIndex"],ndatColn))
cdat <- rbind(cdat,tmp)
#edit(cdat)
}
}
StockList=unique(as.character(cdat$stock))
colnames(cdat)
#stock_id <- unique(as.character(cdat$stock))
#??
# stock_id <- "cod-2224" ## for selecting individual stocks
# stock=stock_id
#??
cat("Step 3","\n")
## FUNCTIONS are going to be used subsequently
.schaefer <- function(theta)
{
with(as.list(theta), { ## for all combinations of ri & ki
bt=vector()
ell = 0 ## initialize ell
J=0 #Ye
for (j in startbt)
{
if(ell == 0)
{
bt[1]=j*k*exp(rnorm(1,0, sigR)) ## set biomass in first year
for(i in 1:nyr) ## for all years in the time series
{
xt=rnorm(1,0, sigR)
bt[i+1]=(bt[i]+r*bt[i]*(1-bt[i]/k)-ct[i])*exp(xt)
## calculate biomass as function of previous year's biomass plus net production minus catch
}
#Bernoulli likelihood, assign 0 or 1 to each combination of r and k
ell = 0
if(bt[nyr+1]/k>=lam1 && bt[nyr+1]/k <=lam2 && min(bt) > 0 && max(bt) <=k && bt[which(yr==interyr)]/k>=interbio[1] && bt[which(yr==interyr)]/k<=interbio[2])
ell = 1
J=j # Ye
}
}
return(list(ell=ell,J=J)) # Ye adding J=J
})
}
sraMSY <-function(theta, N)
{
#This function conducts the stock reduction
#analysis for N trials
#args:
# theta - a list object containing:
# r (lower and upper bounds for r)
# k (lower and upper bounds for k)
# lambda (limits for current depletion)
with(as.list(theta),
{
ri = exp(runif(N, log(r[1]), log(r[2]))) ## get N values between r[1] and r[2], assign to ri
ki = exp(runif(N, log(k[1]), log(k[2]))) ## get N values between k[1] and k[2], assing to ki
itheta=cbind(r=ri,k=ki, lam1=lambda[1],lam2=lambda[2], sigR=sigR)
## assign ri, ki, and final biomass range to itheta
M = apply(itheta,1,.schaefer) ## call Schaefer function with parameters in itheta
i=1:N
## prototype objective function
get.ell=function(i) M[[i]]$ell
ell = sapply(i, get.ell)
get.J=function(i) M[[i]]$J # Ye
J=sapply(i,get.J) # Ye
return(list(r=ri,k=ki, ell=ell, J=J)) # Ye adding J=J
})
}
getBiomass <- function(r, k, j)
{
BT <- NULL
bt=vector()
for (v in 1:length(r))
{
bt[1]=j[v]*k[v]*exp(rnorm(1,0, sigR)) ## set biomass in first year
for(i in 1:nyr) ## for all years in the time series
{
xt=rnorm(1,0, sigR)
bt[i+1]=(bt[i]+r[v]*bt[i]*(1-bt[i]/k[v])-ct[i])*exp(xt)
## calculate biomass as function of previous year's biomass plus net production minus catch
}
BT=rbind(BT, t(t(bt)))
}
return(BT)
}
## The End of Functions section
cat("Step 4","\n")
stockLoop <- StockList
# randomly select stocks from randomly selected 5 area codes first
if(TestRUN)
{
set.seed(999)
AreaCodeList <- unique(cdat1$AREA_Code)
sampledAC <- sample(AreaCodeList,size=5,replace=F)
stockLoop <- cdat1[cdat1$AREA_Code %in% sampledAC,c("Stock_ID")]
}
#setup counters
counter1 <- 0
counter2 <- 0
cat("Step 4","\n")
## Loop through stocks
for(stock in stockLoop)
{
t0<-Sys.time()
##stock = "3845" # NB only for test single loop!
## make graph file names:
b <- with(cdat1,cdat1[Stock_ID == stock,c(1,3,5,12)]) # Stock_ID,AREA_Names,Country,"Species"
bb <- do.call(paste,b)
yr <- cdat$yr[as.character(cdat$stock)==stock]
ct <- as.numeric(cdat$ct[as.character(cdat$stock)==stock])/1000 ## assumes that catch is given in tonnes, transforms to '000 tonnes
res <- unique(as.character(cdat$res[as.character(cdat$stock)==stock])) ## resilience from FishBase, if needed, enable in PARAMETER SECTION
nyr <- length(yr) ## number of years in the time series
cat("\n","Stock",stock,"\n")
flush.console()
## PARAMETER SECTION
mvlen=3
ma=function(x,n=mvlen){filter(x,rep(1/n,n),sides=1)}
## If resilience is to be used, delete ## in rows 1-4 below and set ## in row 5 below
start_r <- if(res == "Very low"){c(0.015, 0.1)}else{
if(res == "Low") {c(0.05,0.5)}else {
if(res == "High") {c(0.6,1.5)}else {c(0.2,1)}
}
}
## Medium, or default if no res is found
##start_r <- c(0.5,1.5) ## disable this line if you use resilience
start_k <- c(max(ct),50*max(ct)) ## default for upper k e.g. 100 * max catch
## startbio <- c(0.8,1) ## assumed biomass range at start of time series, as fraction of k
##startbio <- if(ct[1]/max(ct) < 0.5) {c(0.5,0.9)} else {c(0.3,0.6)} ## use for batch processing
## NB: Yimin's new idea on 20Jan14
startbio<- if(mean(ct[1:5])/max(ct) < 0.3) {c(0.6,0.95)} else {
if(mean(ct[1:5])/max(ct)>0.3&mean(ct[1:5])/max(ct)<0.6) {c(0.3,0.7)} else {
c(0.2,0.6)}}
interyr <- yr[2] ## interim year within time series for which biomass estimate is available; set to yr[2] if no estimates are available
interbio <- c(0, 1) ## biomass range for interim year, as fraction of k; set to 0 and 1 if not available
## finalbio <- c(0.8, 0.9) ## biomass range after last catches, as fraction of k
## finalbio <- if(ct[nyr]/max(ct) > 0.5) {c(0.3,0.7)} else {c(0.01,0.4)} ## use for batch processing
## Yimin's new stuff on 10Mar14
#######> pre-classification
pre.clas=ct
pre.clas[pre.clas==0]=0.1
tx=ma(as.numeric(pre.clas),n=mvlen)
Myr=which.max(tx)
Maxc=pre.clas[which.max(tx)]
if(Myr==1)startbio=c(0.05,0.6)else
{
if (ct[1]/Maxc>=0.5) startbio=c(0.4,0.85)
else startbio=c(0.65,0.95)
}
if (Myr==length(yr))finalbio=c(.4,.95) else # ie from fully to overexploited
{
if (tx[length(ct)]/Maxc>=0.5) finalbio=c(.4,.85)
else finalbio=c(.05,.6)
}
# if (Myr==length(yr))finalbio=c(.5,.9)
# #if (Myr<length(yr)){
# # if ((tx[length(ct)]/Maxc)>=0.8) finalbio=c(.4,.8) else
# # if (tx[length(ct)]/Maxc>0.5) finalbio=c(.3,.7) else finalbio=c(.05,.6)}
# # below is the last used (20 Feb)
# if (Myr<length(yr))
# {
# if (tx[length(ct)]/Maxc>0.5) finalbio=c(.2,.8)
# else finalbio=c(.05,.6)
# }
##############<
n <- 30000 ## number of iterations, e.g. 100000
sigR <- 0.0 ## process error; 0 if deterministic model; 0.05 reasonable value? 0.2 is too high
startbt <- seq(startbio[1], startbio[2], by = 0.05) ## apply range of start biomass in steps of 0.05
parbound <- list(r = start_r, k = start_k, lambda = finalbio, sigR)
cat("Last year =",max(yr),", last catch =",1000*ct[nyr],"\n")
cat("Resilience =",res,"\n")
cat("Process error =", sigR,"\n")
cat("Assumed initial biomass (B/k) =", startbio[1],"-", startbio[2], " k","\n")
cat("Assumed intermediate biomass (B/k) in", interyr, " =", interbio[1],"-",interbio[2]," k","\n")
cat("Assumed final biomass (B/k) =", parbound$lambda[1],"-",parbound$lambda[2]," k","\n")
cat("Initial bounds for r =", parbound$r[1], "-", parbound$r[2],"\n")
cat("Initial bounds for k =", format(1000*parbound$k[1], digits=3), "-", format(1000*parbound$k[2],digits=3),"\n")
flush.console()
## MAIN
R1 = sraMSY(parbound, n)
## Get statistics on r, k, MSY and determine new bounds for r and k
r1 <- R1$r[R1$ell==1]
k1 <- R1$k[R1$ell==1]
j1 <- R1$J[R1$ell==1] # Ye
msy1 <- r1*k1/4
mean_msy1 <- exp(mean(log(msy1)))
max_k1a <- min(k1[r1<1.1*parbound$r[1]]) ## smallest k1 near initial lower bound of r
max_k1b <- max(k1[r1*k1/4<mean_msy1]) ## largest k1 that gives mean MSY
max_k1 <- if(max_k1a < max_k1b) {max_k1a} else {max_k1b}
if(length(r1)<10)
{
cat("Too few (", length(r1), ") possible r-k combinations,
check input parameters","\n")
appendPar <- ifelse(counter1==0,F,T)
colnamePar <- ifelse(counter1==0,T,F)
NoModellingSpe <- as.data.frame(cbind(stock,length(r1),b))
names(NoModellingSpe) <- c("Stock","No_of_r1",names(b))
write.table(NoModellingSpe,file=outfile2,
append = appendPar, row.names = FALSE,
col.names=colnamePar,sep=",")
flush.console()
counter1 <- counter1 + 1
}
if(length(r1)>=10)
{
## set new upper bound of r to 1.2 max r1
parbound$r[2] <- 1.2*max(r1)
## set new lower bound for k to 0.9 min k1 and upper bound to max_k1
parbound$k <- c(0.9 * min(k1), max_k1)
cat("First MSY =", format(1000*mean_msy1, digits=3),"\n")
cat("First r =", format(exp(mean(log(r1))), digits=3),"\n")
cat("New upper bound for r =", format(parbound$r[2],digits=2),"\n")
cat("New range for k =", format(1000*parbound$k[1], digits=3), "-", format(1000*parbound$k[2],digits=3),"\n")
## Repeat analysis with new r-k bounds
R1 = sraMSY(parbound, n)
## Get statistics on r, k and msy
r = R1$r[R1$ell==1]
k = R1$k[R1$ell==1]
j = R1$J[R1$ell==1] # Ye
msy = r * k / 4
mean_ln_msy = mean(log(msy))
##############################################################
##> Ye
# BT=0
##
R2<-getBiomass(r, k, j)
#R2<-R2[-1,]
runs<-rep(1:length(r), each=nyr+1)
years=rep(yr[1]:(yr[length(yr)]+1),length=length(r)*(length(yr)+1))
runs=t(runs)
years=t(years)
stock_id=rep(stock,length(runs))
R3<-cbind(as.numeric(runs), as.numeric(years), stock_id, as.numeric(R2) )
## changed this, as otherwise biomass is the level of the factor below
R4<-data.frame(R3, stringsAsFactors=FALSE)
names(R4)<-c("Run", "Year", "Stock","Biomass")
Bmsy_x<-k*0.5
Run<-c(1:length(r))
BMSY<-cbind(Run, Bmsy_x)
R5<-merge(R4, BMSY, by="Run", all.x=T, all.y=F)
R5$B_Bmsy<-as.numeric(paste(R5$Biomass))/R5$Bmsy_x
### B/Bmsy calculated for all feasible combinations of r,K,B0
R6<-aggregate(log(B_Bmsy)~as.numeric(Year)+Stock, data=R5,
FUN=function(z){c(mean=mean(z),sd=sd(z),upr=exp(quantile(z, p=0.975)),
lwr=exp(quantile(z, p=0.025)), lwrQ=exp(quantile(z, p=0.25)),
uprQ=exp(quantile(z, p=0.75)))}) # from directly calculated from R5 becasue B_Bmsy has a lognormal dist
R6<-data.frame(cbind(R6[,1:2],R6[,3][,1],R6[,3][,2],R6[,3][,3],R6[,3][,4],R6[,3][,5], R6[,3][,6]))
names(R6)<-c("Year", "Stock", "BoverBmsy", "BoverBmsySD","BoverBmsyUpper","BoverBmsyLower","BoverBmsylwrQ","BoverBmsyuprQ")
##remove last entry as it is 1 greater than number of years
## removed final year here for ease of dataframe output below
R6<-R6[-length(R6),]
## geometric mean
GM_B_Bmsy<-exp(R6$BoverBmsy)
GM_B_BmsySD=R6$BoverBmsySD #add
## arithmetic mean
M_B_Bmsy<-exp(R6$BoverBmsy+R6$BoverBmsySD^2/2)
### r,k, and MSY
#del GM_B_Bmsy=c(rep(0,(min(yr)-1940)),GM_B_Bmsy)
#del GM_B_BmsySD=c(rep(0,(min(yr)-1940)),GM_B_BmsySD) ######
#del M_B_Bmsy=c(rep(0,(min(yr)-1940)),M_B_Bmsy)
#del yr1=seq(1940,max(yr))
yr1=yr #add
stockInfo <- with(cdat1,cdat1[Stock_ID==stock,1:12])
temp=c(startbio[1],startbio[2],finalbio[1],finalbio[2],res,
mean(log(r)),sd(log(r)),mean(log(k)),sd(log(k)),mean(log(msy)),
sd(log(msy)),sigR,min(yr),max(yr),max(ct),length(r),GM_B_Bmsy,GM_B_BmsySD,M_B_Bmsy)
#add, adding "GM_B_BmsySD" in the line above
output=as.data.frame(matrix(temp,nrow=1))
output <- cbind(stockInfo,output)
names(output) <- c(names(cdat1)[1:12],"startbio[1]","startbio[2]","finalbio[1]","finalbio[2]",
"res","mean(log(r))","sd(log(r))","mean(log(k))","sd(log(k))",
"mean(log(msy))","sd(log(msy))","sigR","min(yr)","max(yr)","max(ct)",
"length(r)",paste("GM_B_msy",yr1,sep="_"),paste("GM_B_msySD",yr1,sep="_"),paste("M_B_Bmsy",yr1,sep="_"))
#add, adding "paste("GM_B_msySD",yr1,sep="_")"in the line above
######< Ye
########################################################
## plot MSY over catch data
pdf(paste(bb,"graph.pdf",sep="_"))
par(mfcol=c(2,3))
plot(yr, ct, type="l", ylim = c(0, max(ct)), xlab = "Year",
ylab = "Catch (1000 t)",main = paste("StockID",stock,sep=":"))
abline(h=exp(mean(log(msy))),col="red", lwd=2)
abline(h=exp(mean_ln_msy - 2 * sd(log(msy))),col="red")
abline(h=exp(mean_ln_msy + 2 * sd(log(msy))),col="red")
hist(r, freq=F, xlim=c(0, 1.2 * max(r)), main = "")
abline(v=exp(mean(log(r))),col="red",lwd=2)
abline(v=exp(mean(log(r))-2*sd(log(r))),col="red")
abline(v=exp(mean(log(r))+2*sd(log(r))),col="red")
plot(r1, k1, xlim = start_r, ylim = start_k, xlab="r", ylab="k (1000t)")
hist(k, freq=F, xlim=c(0, 1.2 * max(k)), xlab="k (1000t)", main = "")
abline(v=exp(mean(log(k))),col="red", lwd=2)
abline(v=exp(mean(log(k))-2*sd(log(k))),col="red")
abline(v=exp(mean(log(k))+2*sd(log(k))),col="red")
plot(log(r), log(k),xlab="ln(r)",ylab="ln(k)")
abline(v=mean(log(r)))
abline(h=mean(log(k)))
abline(mean(log(msy))+log(4),-1, col="red",lwd=2)
abline(mean(log(msy))-2*sd(log(msy))+log(4),-1, col="red")
abline(mean(log(msy))+2*sd(log(msy))+log(4),-1, col="red")
hist(msy, freq=F, xlim=c(0, 1.2 * max(msy)), xlab="MSY (1000t)",main = "")
abline(v=exp(mean(log(msy))),col="red", lwd=2)
abline(v=exp(mean_ln_msy - 2 * sd(log(msy))),col="red")
abline(v=exp(mean_ln_msy + 2 * sd(log(msy))),col="red")
graphics.off()
cat("Possible combinations = ", length(r),"\n")
cat("geom. mean r =", format(exp(mean(log(r))),digits=3), "\n")
cat("r +/- 2 SD =", format(exp(mean(log(r))-2*sd(log(r))),digits=3),"-",format(exp(mean(log(r))+2*sd(log(r))),digits=3), "\n")
cat("geom. mean k =", format(1000*exp(mean(log(k))),digits=3), "\n")
cat("k +/- 2 SD =", format(1000*exp(mean(log(k))-2*sd(log(k))),digits=3),"-",format(1000*exp(mean(log(k))+2*sd(log(k))),digits=3), "\n")
cat("geom. mean MSY =", format(1000*exp(mean(log(msy))),digits=3),"\n")
cat("MSY +/- 2 SD =", format(1000*exp(mean_ln_msy - 2 * sd(log(msy))),digits=3), "-", format(1000*exp(mean_ln_msy + 2 * sd(log(msy))),digits=3), "\n")
## Write results into outfile, in append mode (no header in file, existing files will be continued)
## output = data.frame(stock, sigR, startbio[1], startbio[2], interbio[1], interbio[2], finalbio[1], finalbio[2], min(yr), max(yr), res, max(ct), ct[1], ct[nyr], length(r), exp(mean(log(r))), sd(log(r)), min(r), quantile(r,0.05), quantile(r,0.25), median(r), quantile(r,0.75), quantile(r,0.95), max(r), exp(mean(log(k))), sd(log(k)), min(k), quantile(k, 0.05), quantile(k, 0.25), median(k), quantile(k, 0.75), quantile(k, 0.95), max(k), exp(mean(log(msy))), sd(log(msy)), min(msy), quantile(msy, 0.05), quantile(msy, 0.25), median(msy), quantile(msy, 0.75), quantile(msy, 0.95), max(msy))
#write.table(output, file = outfile, append = TRUE, sep = ";", dec = ".", row.names = FALSE, col.names = FALSE)
appendPar <- ifelse(counter2==0,F,T)
colnamePar <- ifelse(counter2==0,T,F)
write.table(output, file = outfile, append = appendPar, sep = ",", dec = ".",
row.names = FALSE, col.names = colnamePar)
counter2 <- counter2 + 1
}
cat("Elapsed: ",Sys.time()-t0," \n")
} ## End of stock loop, get next stock or exit

178
PARALLEL_PROCESSING/CatchMSY_Dec2014_test2.R Normal file
View File

@ -0,0 +1,178 @@
set.seed(999) ## for same random sequence
#require(hacks)
#setwd("C:/Users/Ye/Documents/Data poor fisheries/Martell Froese Method/")
## Read Data for stock, year=yr, catch=ct, and resilience=res. Expects space delimited file with header yr ct and years in integer and catch in real with decimal point
## For example
## stock res yr ct
## cap-icel Medium 1984 1234.32
## filename <- "RAM_MSY.csv"
##filename <- "ICESct2.csv"
cat("Step 1","\n")
TestRUN <- F # if it is true, just run on the test samples, false will go for a formal run!
filename <- "D20.csv"
outfile <- "CatchMSY_Output1.csv"
outfile2 <- paste("NonProcessedSpecies.csv",sep="")
#cdat <- read.csv2(filename, header=T, dec=".")
cdat1 <- read.csv(filename)
cat("\n", "File", filename, "read successfully","\n")
cat("Step 2","\n")
if(file.exists("cdat.RData"))
{load("cdat.RData")} else
{
dim(cdat1)
yrs=1950:2012
# to set NA as 0
cdat1[is.na(cdat1)] <- 0
nrow <- length(cdat1[,1])
ndatColn <- length(cdat1[1,c(-1:-12)])
rownames(cdat1) <- NULL
cdat <- NULL
for(i in 1:nrow)
{#i=1
#a <- ctotal3[i,-1]
tmp=data.frame(stock=rep(as.character(cdat1[i,"Stock_ID"]),ndatColn),
species=rep(as.character(cdat1[i,"Scientific_name"]),ndatColn),
yr=yrs,ct=unlist(c(cdat1[i,c(-1:-12)])),
res=rep(cdat1[i,"ResilienceIndex"],ndatColn))
cdat <- rbind(cdat,tmp)
#edit(cdat)
}
}
StockList=unique(as.character(cdat$stock))
colnames(cdat)
#stock_id <- unique(as.character(cdat$stock))
#??
# stock_id <- "cod-2224" ## for selecting individual stocks
# stock=stock_id
#??
cat("Step 3","\n")
## FUNCTIONS are going to be used subsequently
.schaefer <- function(theta)
{
with(as.list(theta), { ## for all combinations of ri & ki
bt=vector()
ell = 0 ## initialize ell
J=0 #Ye
for (j in startbt)
{
if(ell == 0)
{
bt[1]=j*k*exp(rnorm(1,0, sigR)) ## set biomass in first year
for(i in 1:nyr) ## for all years in the time series
{
xt=rnorm(1,0, sigR)
bt[i+1]=(bt[i]+r*bt[i]*(1-bt[i]/k)-ct[i])*exp(xt)
## calculate biomass as function of previous year's biomass plus net production minus catch
}
#Bernoulli likelihood, assign 0 or 1 to each combination of r and k
ell = 0
if(bt[nyr+1]/k>=lam1 && bt[nyr+1]/k <=lam2 && min(bt) > 0 && max(bt) <=k && bt[which(yr==interyr)]/k>=interbio[1] && bt[which(yr==interyr)]/k<=interbio[2])
ell = 1
J=j # Ye
}
}
return(list(ell=ell,J=J)) # Ye adding J=J
})
}
sraMSY <-function(theta, N)
{
#This function conducts the stock reduction
#analysis for N trials
#args:
# theta - a list object containing:
# r (lower and upper bounds for r)
# k (lower and upper bounds for k)
# lambda (limits for current depletion)
with(as.list(theta),
{
ri = exp(runif(N, log(r[1]), log(r[2]))) ## get N values between r[1] and r[2], assign to ri
ki = exp(runif(N, log(k[1]), log(k[2]))) ## get N values between k[1] and k[2], assing to ki
itheta=cbind(r=ri,k=ki, lam1=lambda[1],lam2=lambda[2], sigR=sigR)
## assign ri, ki, and final biomass range to itheta
M = apply(itheta,1,.schaefer) ## call Schaefer function with parameters in itheta
i=1:N
## prototype objective function
get.ell=function(i) M[[i]]$ell
ell = sapply(i, get.ell)
get.J=function(i) M[[i]]$J # Ye
J=sapply(i,get.J) # Ye
return(list(r=ri,k=ki, ell=ell, J=J)) # Ye adding J=J
})
}
getBiomass <- function(r, k, j)
{
BT <- NULL
bt=vector()
for (v in 1:length(r))
{
bt[1]=j[v]*k[v]*exp(rnorm(1,0, sigR)) ## set biomass in first year
for(i in 1:nyr) ## for all years in the time series
{
xt=rnorm(1,0, sigR)
bt[i+1]=(bt[i]+r[v]*bt[i]*(1-bt[i]/k[v])-ct[i])*exp(xt)
## calculate biomass as function of previous year's biomass plus net production minus catch
}
BT=rbind(BT, t(t(bt)))
}
return(BT)
}
## The End of Functions section
cat("Step 4","\n")
stockLoop <- StockList
# randomly select stocks from randomly selected 5 area codes first
if(TestRUN)
{
set.seed(999)
AreaCodeList <- unique(cdat1$AREA_Code)
sampledAC <- sample(AreaCodeList,size=5,replace=F)
stockLoop <- cdat1[cdat1$AREA_Code %in% sampledAC,c("Stock_ID")]
}
#setup counters
counter1 <- 0
counter2 <- 0
cat("Step 4","\n")
## Loop through stocks
write.table("x",file=outfile,append = FALSE, row.names = FALSE,col.names=FALSE,sep=",")
write.table("x",file=outfile2,append = FALSE, row.names = FALSE,col.names=FALSE,sep=",")
for(stock in stockLoop)
{
t0<-Sys.time()
xr <- runif(1, 1.0, 10000)
x1<-c(paste("processed",xr,sep=","))
xr <- runif(1, 1.0, 10000)
x2<-c(paste("non processed",xr,sep=","))
write.table(x1,file=outfile,append = T, row.names = FALSE,col.names=FALSE,sep=",")
write.table(x2,file=outfile2,append = T, row.names = FALSE,col.names=FALSE,sep=",")
cat("Elapsed: ",Sys.time()-t0," \n")
}

17
PARALLEL_PROCESSING/DestinationDBHibernate.cfg.xml
View File

@ -1,17 +0,0 @@
<?xml version='1.0' encoding='UTF-8'?>
<hibernate-configuration>
<session-factory>
<property name="connection.driver_class">org.postgresql.Driver</property>
<property name="connection.provider_class">org.hibernate.connection.C3P0ConnectionProvider</property>
<property name="connection.url">jdbc:postgresql://localhost/testdb</property>
<property name="connection.username">gcube</property>
<property name="connection.password">d4science2</property>
<property name="dialect">org.hibernate.dialect.PostgreSQLDialect</property>
<property name="transaction.factory_class">org.hibernate.transaction.JDBCTransactionFactory</property>
<property name="c3p0.timeout">0</property>
<property name="c3p0.max_size">1</property>
<property name="c3p0.max_statements">0</property>
<property name="c3p0.min_size">1</property>
<property name="current_session_context_class">thread</property>
</session-factory>
</hibernate-configuration>

BIN
PARALLEL_PROCESSING/EcologicalEngineExecutor-1.6.4-SNAPSHOT.jar
View File

Binary file not shown.

530
PARALLEL_PROCESSING/UpdateLWR_4.R
View File

@ -1,530 +0,0 @@
#### R and JAGS code for estimating LWR-parameters from previous studies
#### Meant for updating the ESTIMATE table in FishBase
#### Created by Rainer Froese in March 2013, including JAGS models by James Thorston
#### Modified in June 2013 to include subfamilies
rm(list=ls(all=TRUE)) # remove previous variables and data
options(digits=3) # 3 significant digits as default
library(R2jags) # Interface with JAGS
runif(1) # sets random seed
#### Read in data
DataFile = "RF_LWR2.csv" # RF_LWR4 was extracted from FishBase in June 2013
Data = read.csv(DataFile, header=TRUE)
cat("Start", date(), "\n")
cat("Data file =", DataFile, "\n")
# Get unique, sorted list of Families
Fam.All <- sort(unique(as.character(Data$Family)))
Families <- Fam.All[Fam.All== "Acanthuridae" | Fam.All == "Achiridae"]
OutFile = "LWR_Test1.csv"
JAGSFILE = "dmnorm_0.bug"
# Get unique, sorted list of body shapes
Bshape <- sort(unique(as.character(Data$BodyShapeI)))
#------------------------------------------
# Functions
#------------------------------------------
#---------------------------------------------------------
# Function to get the priors for the respective body shape
#---------------------------------------------------------
Get.BS.pr <- function(BS) {
### Assignment of priors based on available body shape information
# priors derived from 5150 LWR studies in FishBase 02/2013
if (BS == "eel-like") { # eel-like prior for log(a) and b
prior_mean_log10a = -2.99
prior_sd_log10a = 0.175
prior_tau_log10a = 1/prior_sd_log10a^2
prior_mean_b = 3.06
prior_sd_b = 0.0896
prior_tau_b = 1/prior_sd_b^2
} else
if (BS == "elongated") { # elongate prior for log(a) and b
prior_mean_log10a = -2.41
prior_sd_log10a = 0.171
prior_tau_log10a = 1/prior_sd_log10a^2
prior_mean_b = 3.12
prior_sd_b = 0.09
prior_tau_b = 1/prior_sd_b^2
} else
if (BS == "fusiform / normal") { # fusiform / normal prior for log(a) and b
prior_mean_log10a = -1.95
prior_sd_log10a = 0.173
prior_tau_log10a = 1/prior_sd_log10a^2
prior_mean_b = 3.04
prior_sd_b = 0.0857
prior_tau_b = 1/prior_sd_b^2
} else
if (BS == "short and / or deep") { # short and / or deep prior for log(a) and b
prior_mean_log10a = -1.7
prior_sd_log10a = 0.175
prior_tau_log10a = 1/prior_sd_log10a^2
prior_mean_b = 3.01
prior_sd_b = 0.0905
prior_tau_b = 1/prior_sd_b^2
} else
# priors across all shapes, used for missing or other BS
{
prior_mean_log10a = -2.0
prior_sd_log10a = 0.313
prior_tau_log10a = 1/prior_sd_log10a^2
prior_mean_b = 3.04
prior_sd_b = 0.119
prior_tau_b = 1/prior_sd_b^2
}
# Priors for measurement error (= sigma) based on 5150 studies
# given here as shape mu and rate r, for gamma distribution
SD_rObs_log10a = 6520
SD_muObs_log10a = 25076
SD_rObs_b = 6808
SD_muObs_b = 37001
# Priors for between species variability (= sigma) based on 5150 studies for 1821 species
SD_rGS_log10a = 1372
SD_muGS_log10a = 7933
SD_rGS_b = 572
SD_muGS_b = 6498
prior.list <- list(mean_log10a=prior_mean_log10a, sd_log10a=prior_sd_log10a,
tau_log10a=prior_tau_log10a, mean_b=prior_mean_b, sd_b=prior_sd_b,
tau_b=prior_tau_b, SD_rObs_log10a=SD_rObs_log10a, SD_muObs_log10a=SD_muObs_log10a,
SD_rObs_b=SD_rObs_b, SD_muObs_b=SD_muObs_b, SD_rGS_log10a=SD_rGS_log10a,
SD_muGS_log10a=SD_muGS_log10a, SD_rGS_b=SD_rGS_b, SD_muGS_b=SD_muGS_b)
return(prior.list)
}
#--------------------------------------------------------------------
# Function to do a Bayesian analysis including LWR from relatives
#--------------------------------------------------------------------
SpecRelLWR <- function(a, b, wts, GenusSpecies, Nspecies, prior_mean_b, prior_tau_b,
prior_mean_log10a, prior_tau_log10a, SD_rObs_log10a, SD_muObs_log10a,
SD_rObs_b, SD_muObs_b, SD_rGS_log10a, SD_muGS_log10a,
SD_rGS_b, SD_muGS_b){
### Define JAGS model
Model = "
model {
#### Process model -- effects of taxonomy
# given the likelihood distributions and the priors,
# create normal posterior distributions for log10a, b,
# and for the process error (=between species variability sigmaGS)
abTrue[1] ~ dnorm(prior_mean_log10a,prior_tau_log10a)
abTrue[2] ~ dnorm(prior_mean_b,prior_tau_b)
sigmaGSlog10a ~ dgamma( SD_rGS_log10a, SD_muGS_log10a)
sigmaGSb ~ dgamma( SD_rGS_b, SD_muGS_b)
# given the posterior distributions and the process errors,
# establish for every species the expected witin-species
# parameter distributions; no correlation roGS between species
roGS <- 0
tauGenusSpecies[1] <- pow(sigmaGSlog10a,-2)
tauGenusSpecies[2] <- pow(sigmaGSb,-2)
for(k in 1:Nspecies){
abGenusSpecies[k,1] ~ dnorm(abTrue[1],tauGenusSpecies[1])
abGenusSpecies[k,2] ~ dnorm(abTrue[2],tauGenusSpecies[2])
}
### Observation model
## Errors
# given the data and the priors, establish distributions
# for the observation errors sigmaObs
sigmaObslog10a ~ dgamma( SD_rObs_log10a, SD_muObs_log10a)
sigmaObsb ~ dgamma( SD_rObs_b, SD_muObs_b)
# create inverse covariance matrix, with negative parameter correlation roObs
roObs ~ dunif(-0.99,0)
CovObs[1,1] <- pow(sigmaObslog10a,2)
CovObs[2,2] <- pow(sigmaObsb,2)
CovObs[1,2] <- roObs * sigmaObslog10a * sigmaObsb
CovObs[2,1] <- CovObs[1,2]
TauObs[1:2,1:2] <- inverse(CovObs[1:2,1:2])
## likelihood
# given the data, the priors and the covariance,
# create multivariate likelihood distributions for log10(a) and b
for(i in 1:N){
TauObsI[i,1:2,1:2] <- TauObs[1:2,1:2] * pow(Weights[i],2) # weighted precision
ab[i,1:2] ~ dmnorm(abGenusSpecies[GenusSpecies[i],1:2],TauObsI[i,1:2,1:2])
}
}
"
# Write JAGS model
cat(Model, file=JAGSFILE)
# JAGS settings
Nchains = 3 # number of MCMC chains to be used in JAGS
Nburnin = 1e4 # number of burn-in iterations, to be discarded; 1e4 = 10000 iterations for burn-in
Niter = 3e4 # number of iterations after burn-in; 3e4 = 30000 iterations
Nthin = 1e1 # subset of iterations to be used for analysis; 1e1 = every 10th iteration
# Run JAGS: define data to be passed on in DataJags;
# determine parameters to be returned in Param2Save;
# call JAGS with function Jags()
DataJags = list(ab=cbind(log10(a),b), N=length(a), Weights=wts, Nspecies=Nspecies, GenusSpecies=GenusSpecies,
prior_mean_b=prior_mean_b, prior_tau_b=prior_tau_b,
prior_mean_log10a=prior_mean_log10a, prior_tau_log10a=prior_tau_log10a,
SD_rObs_log10a=SD_rObs_log10a, SD_muObs_log10a=SD_muObs_log10a,
SD_rObs_b=SD_rObs_b, SD_muObs_b=SD_muObs_b,
SD_rGS_log10a=SD_rGS_log10a, SD_muGS_log10a=SD_muGS_log10a,
SD_rGS_b=SD_rGS_b, SD_muGS_b=SD_muGS_b)
Params2Save = c("abTrue","abGenusSpecies","sigmaGSlog10a","sigmaGSb","sigmaObslog10a","sigmaObsb","roObs")
Jags <- jags(inits=NULL, model.file=JAGSFILE, working.directory=NULL, data=DataJags,
parameters.to.save=Params2Save, n.chains=Nchains, n.thin=Nthin, n.iter=Niter, n.burnin=Nburnin)
Jags$BUGSoutput # contains the results from the JAGS run
# Analyze output for the relatives
abTrue <- Jags$BUGSoutput$sims.list$abTrue
R_mean_log10a <- mean(abTrue[,1]) # true mean of log10(a)
R_sd_log10a <- sd(abTrue[,1]) # true SE of log10(a)
R_mean_b <- mean(abTrue[,2]) # true mean of b
R_sd_b <- sd(abTrue[,2]) # true SE of b
# Analyze output for the target species
abGenusSpecies <- Jags$BUGSoutput$sims.list$abGenusSpecies
mean_log10a <- mean(abGenusSpecies[,1,1]) # true mean of log10(a) for the first species= target species
sd_log10a <- sd(abGenusSpecies[,1,1]) # true SE of log10(a)
mean_b <- mean(abGenusSpecies[,1,2]) # true mean of b
sd_b <- sd(abGenusSpecies[,1,2]) # true SE of b
mean_sigma_log10a <- mean(Jags$BUGSoutput$sims.list$sigmaObslog10a) # measurement error of log10(a)
sd_sigma_log10a <- apply(as.matrix(Jags$BUGSoutput$sims.list$sigmaObslog10a), 2, sd)
mean_sigma_b <- mean(Jags$BUGSoutput$sims.list$sigmaObsb) # measurement error of b
sd_sigma_b <- apply(as.matrix(Jags$BUGSoutput$sims.list$sigmaObsb), 2, sd)
ro_ab <- mean(Jags$BUGSoutput$sims.list$roObs) # measurement correlation of log10(a),b
out.list <- list(N=length(a), mean_log10a=mean_log10a, sd_log10a=sd_log10a, mean_b=mean_b, sd_b=sd_b,
R_mean_log10a=R_mean_log10a, R_sd_log10a=R_sd_log10a, R_mean_b=R_mean_b, R_sd_b=R_sd_b)
return(out.list)
}
#-----------------------------------------------------------------------------
# Function to do a Bayesian LWR analysis with studies for target species only
#-----------------------------------------------------------------------------
SpecLWR <- function(a, b, wts, prior_mean_b, prior_tau_b,
prior_mean_log10a, prior_tau_log10a, SD_rObs_log10a, SD_muObs_log10a,
SD_rObs_b, SD_muObs_b, SD_rGS_log10a, SD_muGS_log10a,
SD_rGS_b, SD_muGS_b){
# Define JAGS model
Model = "
model {
sigma1 ~ dgamma( SD_rObs_log10a, SD_muObs_log10a) # posterior distribution for measurement error in log10a
sigma2 ~ dgamma( SD_rObs_b, SD_muObs_b) # posterior distribution for measurement error in log10a
ro ~ dunif(-0.99,0) # uniform prior for negative correlation between log10a and b
abTrue[1] ~ dnorm(prior_mean_log10a,prior_tau_log10a) # normal posterior distribution for log10a
abTrue[2] ~ dnorm(prior_mean_b,prior_tau_b) # normal posterior distribution for b
CovObs[1,1] <- pow(sigma1,2)
CovObs[2,2] <- pow(sigma2,2)
CovObs[1,2] <- ro * sigma1 * sigma2
CovObs[2,1] <- CovObs[1,2]
TauObs[1:2,1:2] <- inverse(CovObs[1:2,1:2]) # create inverse covariance matrix
for(i in 1:N){
TauObsI[i,1:2,1:2] <- TauObs[1:2,1:2] * pow(Weights[i],2) # converts prior SD into prior weighted precision
# given the data, the priors and the covariance, create multivariate normal posteriors for log(a) and b
ab[i,1:2] ~ dmnorm(abTrue[1:2],TauObsI[i,1:2,1:2])
}
}
"
# Write JAGS model
cat(Model, file=JAGSFILE)
# JAGS settings
Nchains = 3 # number of MCMC chains to be used in JAGS
Nburnin = 1e4 # number of burn-in runs, to be discarded; 10000 iterations for burn-in
Niter = 3e4 # number of iterations after burn-in; 3e4 = 30000 iterations
Nthin = 1e1 # subset of iterations to be used for analysis; 1e1 = every 10th iteration
# Run JAGS: define data to be passed on in DataJags; determine parameters to be returned in Param2Save; call JAGS with function Jags()
DataJags = list(ab=cbind(log10(a),b), N=length(a), Weights=wts, prior_mean_b=prior_mean_b,
prior_tau_b=prior_tau_b, prior_mean_log10a=prior_mean_log10a, prior_tau_log10a=prior_tau_log10a,
SD_rObs_log10a=SD_rObs_log10a, SD_muObs_log10a=SD_muObs_log10a,
SD_rObs_b=SD_rObs_b, SD_muObs_b=SD_muObs_b)
Params2Save = c("abTrue","sigma1","sigma2","ro")
Jags <- jags(inits=NULL, model.file=JAGSFILE, working.directory=NULL, data=DataJags, parameters.to.save=Params2Save, n.chains=Nchains, n.thin=Nthin, n.iter=Niter, n.burnin=Nburnin)
Jags$BUGSoutput # contains the results from the JAGS run
# Analyze output
abTrue <- Jags$BUGSoutput$sims.list$abTrue
mean_log10a <- mean(abTrue[,1]) # true mean of log10(a)
sd_log10a <- sd(abTrue[,1]) # true SE of log10(a)
mean_b <- mean(abTrue[,2]) # true mean of b
sd_b <- sd(abTrue[,2]) # true SE of b
mean_sigma_log10a <- mean(Jags$BUGSoutput$sims.list$sigma1) # measurement error of log10(a)
sd_sigma_log10a <- apply(as.matrix(Jags$BUGSoutput$sims.list$sigma1), 2, sd)
mean_sigma_b <- mean(Jags$BUGSoutput$sims.list$sigma2) # measurement error of b
sd_sigma_b <- apply(as.matrix(Jags$BUGSoutput$sims.list$sigma2), 2, sd)
ro_ab <- mean(Jags$BUGSoutput$sims.list$ro) # measurement correlation of log10(a),b
out.list <- list(N=length(a), mean_log10a=mean_log10a, sd_log10a=sd_log10a, mean_b=mean_b, sd_b=sd_b)
return(out.list)
} # End of Functions section
#--------------------------------
# Analysis by Family
#--------------------------------
# Do LWR analysis by Family, Subfamily and Body shape, depending on available LWR studies
# for(Fam in "Acanthuridae") {
for(Fam in Families) {
Subfamilies <- sort(unique(Data$Subfamily[Data$Family==Fam]))
for(SF in Subfamilies) {
for(BS in Bshape) {
# get species (SpecCodes) in this Subfamily and with this body shape
SpecCode.SF.BS <- unique(Data$SpecCode[Data$Family==Fam & Data$Subfamily==SF & Data$BodyShapeI==BS])
# if there are species with this body shape
if(length(SpecCode.SF.BS > 0)) {
# get priors for this body shape
prior <- Get.BS.pr(BS)
# get LWR data for this body shape
b_raw <- Data$b[Data$Family==Fam & Data$Subfamily==SF & Data$BodyShapeI==BS]
cat("\n")
cat("Family =", Fam, ", Subfamily =", SF, ", Body shape =", BS, ", Species =", length(SpecCode.SF.BS), ", LWR =",
length(b_raw[is.na(b_raw)==F]), "\n")
# if no LWR studies exist for this body shape, assign the respective priors to all species
if(length(b_raw[is.na(b_raw)==F])==0) {
# assign priors to species with no LWR in this Subfamily with this body shape
cat("Assigning overall body shape prior to", length(SpecCode.SF.BS), " species \n")
for(SpC in SpecCode.SF.BS) {
out.prior <- data.frame(Fam, SF, BS, SpC, 0, prior$mean_log10a, prior$sd_log10a, prior$mean_b, prior$sd_b,
paste("all LWR estimates for this BS"))
write.table(out.prior, file=OutFile, append = T, sep=",", dec=".", row.names=F, col.names=F)
}
} else {
# Update priors for this body shape using existing LWR studies
# get LWR data for this Subfamily and body shape
Keep <- which(Data$Family==Fam & Data$Subfamily==SF & Data$BodyShapeI==BS & is.na(Data$b)==F & Data$Score>0)
wts <- Data$Score[Keep] # Un-normalized weights (so that Cov is comparable among analyses)
a <- Data$a[Keep]
b <- Data$b[Keep]
GenSpec <- paste(Data$Genus[Keep],Data$Species[Keep])
# add a first dummy record with prior LWR and low score = 0.3, as pseudo target species
# Name of dummy target species is Dum1 dum1
TargetSpec = paste("Dum1", "dum1")
wts <- c(0.3, wts)
a <- c(10^(prior$mean_log10a), a)
b <- c(prior$mean_b, b)
GenSpec <- c(TargetSpec, GenSpec)
# Relabel GenSpec so that TargetSpec = level 1
OtherSpecies = unique(GenSpec[GenSpec != TargetSpec])
GenusSpecies = factor(GenSpec, levels=c(TargetSpec, OtherSpecies))
Nspecies = nlevels(GenusSpecies) # number of species
# run Bayesian analysis for pseudo target species with Subfamily members
# The resulting R_mean_log10a, R_sd_log10a, R_mean_b, R_sd_b will be used for species without LWR
cat("Updating Subfamily-Bodyshape prior using", Nspecies-1, "species with LWR studies \n")
prior.SFam.BS <- SpecRelLWR(a, b, wts, GenusSpecies, Nspecies, prior_mean_b=prior$mean_b,
prior_tau_b=prior$tau_b, prior_mean_log10a=prior$mean_log10a,
prior_tau_log10a=prior$tau_log10a, SD_rObs_log10a=prior$SD_rObs_log10a,
SD_muObs_log10a=prior$SD_muObs_log10a, SD_rObs_b=prior$SD_rObs_b,
SD_muObs_b=prior$SD_muObs_b, SD_rGS_log10a=prior$SD_rGS_log10a,
SD_muGS_log10a=prior$SD_muGS_log10a, SD_rGS_b=prior$SD_rGS_b,
SD_muGS_b=prior$SD_muGS_b)
#------------------------------------------------------------------------------------------
# if there are Genera with >= 5 species with LWR, update body shape priors for these Genera
#------------------------------------------------------------------------------------------
Genera <- unique(as.character(Data$Genus[Keep]))
# create empty list of lists for storage of generic priors
prior.Gen.BS <- rep(list(list()),length(Genera)) # create a list of empty lists
names(prior.Gen.BS) <- Genera # name the list elements according to the Genera
for(Genus in Genera){
# check if Genus contains >= 5 species with LWR data
if(length(unique(Data$SpecCode[Data$Family==Fam & Data$Subfamily==SF & Data$BodyShapeI==BS & is.na(Data$b)==F &
Data$Score>0 & Data$Genus==Genus]))>=5) {
# run Subfamily analysis with only data for this genus
Keep <- which(Data$Family==Fam & Data$Subfamily==SF & Data$BodyShapeI==BS & is.na(Data$b)==F & Data$Score>0 &
Data$Genus==Genus)
wts <- Data$Score[Keep] # Un-normalized weights (so that Cov is comparable among analyses)
a <- Data$a[Keep]
b <- Data$b[Keep]
GenSpec <- paste(Data$Genus[Keep],Data$Species[Keep])
# add a first dummy record with prior LWR and low score = 0.3, as pseudo target species
# Name of dummy target species is Dum1 dum1
TargetSpec = paste("Dum1", "dum1")
wts <- c(0.3, wts)
a <- c(10^(prior$mean_log10a), a)
b <- c(prior$mean_b, b)
GenSpec <- c(TargetSpec, GenSpec)
# Relabel GenSpec so that TargetSpec = level 1
OtherSpecies = unique(GenSpec[GenSpec != TargetSpec])
GenusSpecies = factor(GenSpec, levels=c(TargetSpec, OtherSpecies))
Nspecies = nlevels(GenusSpecies) # number of species
# run Bayesian analysis for pseudo target species with Genus members
# R_mean_log10a, R_sd_log10a, R_mean_b, R_sd_b will be used for species without LWR
cat("Updating prior for Genus =", Genus, ", with", Nspecies -1, "LWR Species \n")
prior.Gen.BS[[Genus]] <- SpecRelLWR(a, b, wts, GenusSpecies, Nspecies,
prior_mean_b=prior.SFam.BS$R_mean_b,
prior_tau_b=1/prior.SFam.BS$R_sd_b^2,
prior_mean_log10a=prior.SFam.BS$R_mean_log10a,
prior_tau_log10a=1/prior.SFam.BS$R_sd_log10a,
SD_rObs_log10a=prior$SD_rObs_log10a,
SD_muObs_log10a=prior$SD_muObs_log10a, SD_rObs_b=prior$SD_rObs_b,
SD_muObs_b=prior$SD_muObs_b, SD_rGS_log10a=prior$SD_rGS_log10a,
SD_muGS_log10a=prior$SD_muGS_log10a, SD_rGS_b=prior$SD_rGS_b,
SD_muGS_b=prior$SD_muGS_b)
}
}
# new Subfamily-BS priors have been generated
# for some genera, new Genus-BS priors have been generated
# ---------------------------------------------------------------------
# Loop through all species in this Subfamily-BS; assign LWR as appropriate
# ---------------------------------------------------------------------
for(SpC in SpecCode.SF.BS) {
Genus <- as.character(unique(Data$Genus[Data$SpecCode==SpC]))
Species <- as.character(unique(Data$Species[Data$SpecCode==SpC]))
TargetSpec = paste(Genus, Species)
LWR <- length(Data$b[Data$SpecCode==SpC & is.na(Data$b)==F & Data$Score>0])
LWRGenspec <- length(unique(Data$SpecCode[Data$BodyShapeI==BS & is.na(Data$b)==F &
Data$Score>0 & Data$Genus==Genus]))
LWRSFamspec <- length(unique(Data$SpecCode[Data$BodyShapeI==BS & is.na(Data$b)==F &
Data$Score>0 & Data$Family==Fam & Data$Subfamily==SF]))
#---------------------------------------------------------
# >= 5 LWR in target species, run single species analysis
if(LWR >= 5) {
# Run analysis with data only for this species
Keep <- which(Data$SpecCode==SpC & is.na(Data$b)==F & Data$Score>0)
wts = Data$Score[Keep] # Un-normalized weights (so that Cov is comparable among analyses)
a = Data$a[Keep]
b = Data$b[Keep]
# determine priors to be used
if(LWRGenspec >= 5) {
prior_mean_b=prior.Gen.BS[[Genus]]$R_mean_b
prior_tau_b=1/prior.Gen.BS[[Genus]]$R_sd_b^2
prior_mean_log10a=prior.Gen.BS[[Genus]]$R_mean_log10a
prior_tau_log10a=1/prior.Gen.BS[[Genus]]$R_sd_log10a^2
} else
if (LWRSFamspec > 0) {
prior_mean_b=prior.SFam.BS$R_mean_b
prior_tau_b=1/prior.SFam.BS$R_sd_b^2
prior_mean_log10a=prior.SFam.BS$R_mean_log10a
prior_tau_log10a=1/prior.SFam.BS$R_sd_log10a^2
} else {
prior_mean_b=prior$mean_b
prior_tau_b=prior$tau_b
prior_mean_log10a=prior$mean_log10a
prior_tau_log10a=prior$tau_log10a
}
cat("Running single species analysis for", TargetSpec, "LWR =", LWR, ", LWR species in Genus=",LWRGenspec,"\n" )
# call function for single species analysis
post <- SpecLWR(a, b, wts, prior_mean_b=prior_mean_b,
prior_tau_b=prior_tau_b, prior_mean_log10a=prior_mean_log10a,
prior_tau_log10a=prior_tau_log10a, SD_rObs_log10a=prior$SD_rObs_log10a,
SD_muObs_log10a=prior$SD_muObs_log10a, SD_rObs_b=prior$SD_rObs_b,
SD_muObs_b=prior$SD_muObs_b, SD_rGS_log10a=prior$SD_rGS_log10a,
SD_muGS_log10a=prior$SD_muGS_log10a, SD_rGS_b=prior$SD_rGS_b,
SD_muGS_b=prior$SD_muGS_b)
out.SpC <- data.frame(Fam, SF, BS, SpC, LWR, format(post$mean_log10a, digits=3), format(post$sd_log10a, digits=3), format(post$mean_b, disgits=3), format(post$sd_b, digits=3),
paste("LWR estimates for this species"))
write.table(out.SpC, file=OutFile, append = T, sep=",", dec=".", row.names=F, col.names=F)
} else
#--------------------------------------------------------
# 1-4 LWR in target species and >= 5 LWR species in Genus
# run hierarchical analysis for genus members, with Subfamily-BS prior
if(LWR >= 1 & LWRGenspec >=5) {
# run Subfamily analysis with only data for this genus
Keep <- which(Data$Family==Fam & Data$Subfamily==SF & Data$BodyShapeI==BS & is.na(Data$b)==F & Data$Score>0 &
Data$Genus==Genus)
wts <- Data$Score[Keep] # Un-normalized weights (so that Cov is comparable among analyses)
a <- Data$a[Keep]
b <- Data$b[Keep]
GenSpec <- paste(Data$Genus[Keep],Data$Species[Keep])
# Relabel GenSpec so that TargetSpec = level 1
OtherSpecies = unique(GenSpec[GenSpec != TargetSpec])
GenusSpecies = factor(GenSpec, levels=c(TargetSpec, OtherSpecies))
Nspecies = nlevels(GenusSpecies) # number of species
# run Bayesian analysis for target species with Genus members
cat("Running analysis with congeners for", TargetSpec, ", LWR =", LWR,", LWR species in Genus =", LWRGenspec,"\n")
post <- SpecRelLWR(a, b, wts, GenusSpecies, Nspecies,
prior_mean_b=prior.SFam.BS$R_mean_b,
prior_tau_b=1/prior.SFam.BS$R_sd_b^2,
prior_mean_log10a=prior.SFam.BS$R_mean_log10a,
prior_tau_log10a=1/prior.SFam.BS$R_sd_log10a^2,
SD_rObs_log10a=prior$SD_rObs_log10a,
SD_muObs_log10a=prior$SD_muObs_log10a, SD_rObs_b=prior$SD_rObs_b,
SD_muObs_b=prior$SD_muObs_b, SD_rGS_log10a=prior$SD_rGS_log10a,
SD_muGS_log10a=prior$SD_muGS_log10a, SD_rGS_b=prior$SD_rGS_b,
SD_muGS_b=prior$SD_muGS_b)
out.SpC <- data.frame(Fam, SF, BS, SpC, LWR, format(post$mean_log10a, digits=3), format(post$sd_log10a, digits=3), format(post$mean_b, disgits=3), format(post$sd_b, digits=3),
paste("LWR estimates for species & Genus-BS"))
write.table(out.SpC, file=OutFile, append = T, sep=",", dec=".", row.names=F, col.names=F)
} else
#-------------------------------------------------------
# 1-4 LWR in target species and < 5 LWR species in Genus
# run hierarchical analysis for Subfamily members, with bodyshape prior
if(LWR >= 1 & LWRSFamspec > 1) {
# run Subfamily analysis
Keep <- which(Data$Family==Fam & Data$Subfamily==SF & Data$BodyShapeI==BS & is.na(Data$b)==F & Data$Score>0)
wts <- Data$Score[Keep] # Un-normalized weights (so that Cov is comparable among analyses)
a <- Data$a[Keep]
b <- Data$b[Keep]
GenSpec <- paste(Data$Genus[Keep],Data$Species[Keep])
# Relabel GenSpec so that TargetSpec = level 1
OtherSpecies = unique(GenSpec[GenSpec != TargetSpec])
GenusSpecies = factor(GenSpec, levels=c(TargetSpec, OtherSpecies))
Nspecies = nlevels(GenusSpecies) # number of species
# run Bayesian analysis for target species with Subfamily members
cat("Running analysis with Subfamily members for", TargetSpec, ", LWR =", LWR,", LWR species in Subfamily-BS =",
LWRSFamspec, "\n")
post <- SpecRelLWR(a, b, wts, GenusSpecies, Nspecies,
prior_mean_b=prior$mean_b,
prior_tau_b=prior$tau_b,
prior_mean_log10a=prior$mean_log10a,
prior_tau_log10a=prior$tau_log10a,
SD_rObs_log10a=prior$SD_rObs_log10a,
SD_muObs_log10a=prior$SD_muObs_log10a, SD_rObs_b=prior$SD_rObs_b,
SD_muObs_b=prior$SD_muObs_b, SD_rGS_log10a=prior$SD_rGS_log10a,
SD_muGS_log10a=prior$SD_muGS_log10a, SD_rGS_b=prior$SD_rGS_b,
SD_muGS_b=prior$SD_muGS_b)
out.SpC <- data.frame(Fam, SF, BS, SpC, LWR, format(post$mean_log10a, digits=3), format(post$sd_log10a, digits=3),
format(post$mean_b, disgits=3), format(post$sd_b, digits=3),
paste("LWR estimates for species & Subfamily-BS"))
write.table(out.SpC, file=OutFile, append = T, sep=",", dec=".", row.names=F, col.names=F)
} else
#--------------------------------------------------
# assign Genus-BS priors to target species
if(LWRGenspec >= 5) {
cat("Assign Genus-BS prior for", TargetSpec, "\n")
out.SpC <- data.frame(Fam, SF, BS, SpC, LWR, format(prior.Gen.BS[[Genus]]$mean_log10a, digits=3),
format(prior.Gen.BS[[Genus]]$sd_log10a, digits=3),
format(prior.Gen.BS[[Genus]]$mean_b, digits=3), format(prior.Gen.BS[[Genus]]$sd_b, digits=3),
paste("LWR estimates for this Genus-BS"))
write.table(out.SpC, file=OutFile, append = T, sep=",", dec=".", row.names=F, col.names=F)
} else {
# -----------------------------------------------
# assign Subfamily-BS priors to target species
cat("Assign Subfamily-BS prior for", TargetSpec,"\n")
out.SpC <- data.frame(Fam, SF, BS, SpC, LWR, format(prior.SFam.BS$mean_log10a, digits=3), format(prior.SFam.BS$sd_log10a, digits=3),
format(prior.SFam.BS$mean_b, digits=3), format(prior.SFam.BS$sd_b, digits=3), paste("LWR estimates for this Subfamily-BS"))
write.table(out.SpC, file=OutFile, append = T, sep=",", dec=".", row.names=F, col.names=F)
}
} # end of species loop for this Subfamily and body shape
} # end of section dealing with Subfamily - body shapes that contain LWR estimates
} # end of section that deals with Subfamily - body shapes that contain species
} # end of body shape section
} # end of Subfamily section
} # end of Family section
cat("End", date(),"\n")

BIN
PARALLEL_PROCESSING/YASMEEN-matcher-1.2.0.1.jar
View File

Binary file not shown.

BIN
PARALLEL_PROCESSING/YASMEEN-parser-1.2.0.jar
View File

Binary file not shown.

BIN
PARALLEL_PROCESSING/c3p0-0.9.1.2.jar
View File

Binary file not shown.

BIN
PARALLEL_PROCESSING/common-configuration-scanner-1.0.1-SNAPSHOT.jar
View File

Binary file not shown.

BIN
PARALLEL_PROCESSING/common-encryption-1.0.1-3.5.0.jar
View File

Binary file not shown.

BIN
PARALLEL_PROCESSING/common-gcore-resources-1.2.0-3.5.0.jar
View File

Binary file not shown.

BIN
PARALLEL_PROCESSING/common-gcore-stubs-1.2.0-3.5.0.jar
View File

Binary file not shown.

BIN
PARALLEL_PROCESSING/common-scope-1.2.1-SNAPSHOT.jar
View File

Binary file not shown.

BIN
PARALLEL_PROCESSING/common-scope-maps-1.0.2-3.5.0.jar
View File

Binary file not shown.

BIN
PARALLEL_PROCESSING/commons-collections-3.1.jar
View File

Binary file not shown.

BIN
PARALLEL_PROCESSING/commons-io-1.2.jar
View File

Binary file not shown.

BIN
PARALLEL_PROCESSING/discovery-client-1.0.1-3.5.0.jar
View File

Binary file not shown.

BIN
PARALLEL_PROCESSING/dom4j-1.6.1.jar
View File

Binary file not shown.

BIN
PARALLEL_PROCESSING/ecological-engine-1.8.1-SNAPSHOT.jar
View File

Binary file not shown.

BIN
PARALLEL_PROCESSING/hibernate3.jar
View File

Binary file not shown.

BIN
PARALLEL_PROCESSING/ic-client-1.0.1-3.5.0.jar
View File

Binary file not shown.

BIN
PARALLEL_PROCESSING/jaxen-1.1.2.jar
View File

Binary file not shown.

BIN
PARALLEL_PROCESSING/jta-1.1.jar
View File

Binary file not shown.

BIN
PARALLEL_PROCESSING/log4j-1.2.16.jar
View File

Binary file not shown.

BIN
PARALLEL_PROCESSING/mongo-java-driver-2.12.4.jar
View File

Binary file not shown.

BIN
PARALLEL_PROCESSING/postgresql-8.4-702.jdbc4.jar
View File

Binary file not shown.

5
PARALLEL_PROCESSING/script_FAOMSY.sh Normal file
View File

@ -0,0 +1,5 @@
#!/bin/sh
# FAOMSY
cd $1
java -Xmx1024M -classpath ./:./c3p0-0.9.1.2.jar:./common-configuration-scanner-1.0.1-SNAPSHOT.jar:./common-encryption-1.0.1-3.5.0.jar:./common-gcore-resources-1.2.0-3.5.0.jar:./common-gcore-stubs-1.2.0-3.5.0.jar:./common-scope-1.2.1-SNAPSHOT.jar:./common-scope-maps-1.0.2-3.5.0.jar:./commons-collections-3.1.jar:./commons-io-1.2.jar:./discovery-client-1.0.1-3.5.0.jar:./dom4j-1.6.1.jar:./ecological-engine-1.8.1-SNAPSHOT.jar:./EcologicalEngineExecutor-1.6.4-SNAPSHOT.jar:./hibernate3.jar:./ic-client-1.0.1-3.5.0.jar:./jaxen-1.1.2.jar:./jta-1.1.jar:./log4j-1.2.16.jar:./mongo-java-driver-2.12.4.jar:./postgresql-8.4-702.jdbc4.jar:./slf4j-api-1.6.0.jar:./slf4j-log4j12-1.6.0.jar:./storage-manager-core-2.1.3-3.6.0.jar:./storage-manager-wrapper-2.1.0-3.5.0.jar:./xalan-2.6.0.jar:./xpp3_min-1.1.4c.jar:./xstream-1.3.1.jar:./YASMEEN-matcher-1.2.0.1.jar:./YASMEEN-parser-1.2.0.jar org.gcube.dataanalysis.executor.nodes.algorithms.FAOMSY $2 execution.output

BIN
PARALLEL_PROCESSING/slf4j-api-1.6.0.jar
View File

Binary file not shown.

BIN
PARALLEL_PROCESSING/slf4j-log4j12-1.6.0.jar
View File

Binary file not shown.

BIN
PARALLEL_PROCESSING/storage-manager-core-2.1.3-3.6.0.jar
View File

Binary file not shown.

BIN
PARALLEL_PROCESSING/storage-manager-wrapper-2.1.0-3.5.0.jar
View File

Binary file not shown.

BIN
PARALLEL_PROCESSING/xalan-2.6.0.jar
View File

Binary file not shown.

BIN
PARALLEL_PROCESSING/xpp3_min-1.1.4c.jar
View File

Binary file not shown.

BIN
PARALLEL_PROCESSING/xstream-1.3.1.jar
View File

Binary file not shown.