inst/Assessments/LFA35-38Assessment/11.LFA3538Assessment.R
In LobsterScience/bio.lobster: Lobster Stock Assessment Tools for DFO Maritimes
p = bio.lobster::load.environment()
la()
assessment.year = 2019 ########### check the year ############### !!!!!!!!!!!
p$syr = 1989
p$yrs = p$syr:assessment.year
ff = "LFA35-38Assessment"
figdir = file.path(project.figuredirectory('bio.lobster'),ff)
p$lfas = c( "35", "36", "38") # specify lfas for data summary
p$subareas = c( "35", "36", "38") # specify lfas for data summary
logsInSeason=lobster.db("process.logs")
## Fishery Footprint - Landings
catchLevels = c(0,100000,200000,300000,400000,500000,600000,700000,800000)
yrs = 2011:2018
for(i in 1:length(yrs)){
catchgrids = lobGridPlot(subset(logsInSeason,LFA%in%p$lfas&SYEAR==2016,c("LFA","GRID_NUM","TOTAL_WEIGHT_KG")),FUN=sum,lvls=catchLevels)
pdf(file.path(figdir,paste0("FisheryFootprint",yrs[i],".pdf")))
LobsterMap('34-38',poly.lst=catchgrids)
title(yrs[i],line=-3,cex.main=2,adj=0.3)
SpatialHub::contLegend('bottomright',lvls=catchgrids$lvls/1000,Cont.data=catchgrids,title="Catch (tons)",inset=0.02,cex=0.8,bg='white')
dev.off()
}
## Fishery Footprint - CPUE
cpueLevels = c(0,0.2,0.4,0.6,0.8,0.9,1,2,3)
yrs = 2011:2018
#logsInSeason$logCPUE = log(logsInSeason$CPUE+1)
for(i in 1:length(yrs)){
cpuegrids = lobGridPlot(subset(logsInSeason,LFA%in%p$lfas&SYEAR == yrs[i],c("LFA","GRID_NUM","CPUE")),FUN=median,lvls=cpueLevels)
pdf(file.path(figdir,paste0("FishFootcpue", yrs[i],".pdf")))
LobsterMap('34-38',poly.lst=cpuegrids)
title(yrs[i],line=-3,cex.main=2,adj=0.3)
SpatialHub::contLegend('bottomright',lvls=cpuegrids$lvls,Cont.data=cpuegrids,title="CPUE (kg/TH)",inset=0.02,cex=0.8,bg='white')
dev.off()
}
## Fishery Footprint - Mean Pots Hauled
potLevels = c (0,1000,100000,200000,300000,400000,500000,600000)
yrs = 2011:2018
for(i in 1:length(yrs)){
potgrids = lobGridPlot(subset(logsInSeason,LFA%in%p$lfas&SYEAR == yrs[i],c("LFA","GRID_NUM","NUM_OF_TRAPS")),FUN=sum,lvls=potLevels)
pdf(file.path(figdir,paste0("FishFootpot", yrs[i],".pdf")))
LobsterMap('34-38',poly.lst=potgrids)
title(yrs[i],line=-3,cex.main=2,adj=0.3)
SpatialHub::contLegend('bottomright',lvls=potgrids$lvls/1000,Cont.data=potgrids,title="Pots Hauled (000s)",inset=0.02,cex=0.8,bg='white')
dev.off()
}
## Fishery Footprint - Days Fished
daysLevels = c(0,500,1000,1500,2000,2500,3000)
daysFished<-aggregate(DATE_FISHED ~ SYEAR + LFA + GRID_NUM + LICENCE_ID, data=logsInSeason,FUN= function(x) length(unique(x)))
yrs = 2011:2018
for (i in 1: length(yrs)){
daysgrids = lobGridPlot(subset(daysFished, LFA%in%p$lfas&SYEAR == yrs[i],c("LFA", "GRID_NUM", "DATE_FISHED")),FUN=sum, lvls= daysLevels)
pdf(file.path(figdir,paste0("FishFootDaysFished", yrs[i],".pdf")))
LobsterMap('34-38',poly.lst=daysgrids)
title(yrs[i],line=-3,cex.main=2,adj=0.3)
SpatialHub::contLegend('bottomright',lvls=daysgrids$lvls,Cont.data=daysgrids,title="Total Days Fished",inset=0.02,cex=0.8,bg='white')
dev.off()
}
## Fishery Footprint - Licences Fished
licenceLevels = c(0,15,30,45,60,75,90,105,120)
yrs=2011:2018
daysFished$LICENCE<-1
for(i in 1: length(yrs)){
licencegrids = lobGridPlot(subset(daysFished, LFA%in%p$lfas&SYEAR==yrs[i], c("LFA", "GRID_NUM", "LICENCE")), FUN=sum, lvls= licenceLevels)
pdf(file.path(figdir,paste0("FishFootLicenceFished", yrs[i],".pdf")))
LobsterMap('34-38', poly.lst=licencegrids)
title(yrs[i],line=-3,cex.main=2,adj=0.3)
SpatialHub::contLegend('bottomright', lvls=licencegrids$lvls, Cont.data=licencegrids, title= "Number of Licence Fished", inset =0.02,cex=0.8,bg='white')
dev.off()
}
## CPUE
logsInSeason<-lobster.db('process.logs.redo')
logsInSeason<-lobster.db('process.logs')
write.csv(logsInSeason,file.path(project.datadirectory("bio.lobster"),'data',"Logs.csv"),row.names=F)
cpueLFA.dat = CPUEplot(logsInSeason,lfa= p$lfas,yrs=2002:2019,graphic='R',export=F)
cpueLFA.dat = CPUEplot(logsInSeason,lfa= p$lfas,yrs=2006:2019,graphic='pdf',path=figdir)
cpueSubArea.dat = CPUEplot(logsInSeason,subarea= p$subareas,yrs=2006:2019,graphic='R')
## Commercial CPUE MOdels
mf1 = formula(logWEIGHT ~ fYEAR + DOS + TEMP + DOS * TEMP)
mf2 = formula(logWEIGHT ~ fYEAR + DOS + TEMP)
mf3 = formula(logWEIGHT ~ fYEAR + DOS)
mf4 = formula(logWEIGHT ~ fYEAR + TEMP)
#mf5 = formula(logWEIGHT ~ fYEAR + DOS + TEMP + (1 | fYEAR/fAREA)) # combined
TempModelling = TempModel( annual.by.area=F)
#CPUE.data<-CPUEModelData(p,redo=T,TempModelling)
CPUE.data<- CPUEModelData(p,redo=F)
CPUE.data=subset(CPUE.data,!(LFA==35&SYEAR<2006)) #exclude partial year of data in 35
CPUE.data=subset(CPUE.data,!(LFA==36&SYEAR<2005)) #exclude partial year of data in 36
#Modelled Temperature at first day of season
pL=0
t=c()
d=c()
k=1
for(i in 1:length(p$lfas)){
for(j in 2:length(p$yrs)){
Cdat=subset(CPUE.data,LFA==p$lfas[i]&SYEAR==p$yrs[j])
Cdat=Cdat[order(Cdat$DATE_FISHED),]
Cdat$pLanded = cumsum(Cdat$WEIGHT_KG)/sum(Cdat$WEIGHT_KG)
if(nrow(Cdat)>1){
x=abs(Cdat$pLanded-pL)
d[k]=Cdat$DOS[which(x==min(x))]
t[k]=Cdat$TEMP[which(x==min(x))]
names(d)[k]=paste(p$lfas[i],p$yrs[j],sep='.')
names(t)[k]=paste(p$lfas[i],p$yrs[j],sep='.')
k=k+1
}
}
}
##t and d Overwrite with summary
t=with(subset(CPUE.data,DOS==1),tapply(TEMP,LFA,mean))
d=1
aicc = function(aa = model.output) {
k = attr(logLik(aa),'df')
logLiks = logLik(aa)[1]
n = nobs(aa)
aaa = 2*nParams-2*logLiks
aaa + (2*k^2+2*k) / (n-k-1)
}
pData=list()
CPUEModelResults1 = list()
CPUEModelResults2 = list()
CPUEModelResults3 = list()
CPUEModelResults4 = list()
AICs1 = c()
AICs2 = c()
AICs3 = c()
AICs4 = c()
for(i in 1:length( p$lfas)){
mdata = subset(CPUE.data,LFA==p$lfas[i]&SYEAR%in%p$yrs)
CPUEModelResults1[[i]] = CPUEmodel(mf1,mdata,t=t[i],d=d)
CPUEModelResults2[[i]] = CPUEmodel(mf2,mdata,t=t[i],d=d)
CPUEModelResults3[[i]] = CPUEmodel(mf3,mdata,t=t[i],d=d)
CPUEModelResults4[[i]] = CPUEmodel(mf4,mdata,t=t[i],d=d)
AICs1[i] = aicc(CPUEModelResults1[[i]]$model)
AICs2[i] = aicc(CPUEModelResults2[[i]]$model)
AICs3[i] = aicc(CPUEModelResults3[[i]]$model)
AICs4[i] = aicc(CPUEModelResults4[[i]]$model)
}
names(CPUEModelResults1) = p$lfas
names(CPUEModelResults2) = p$lfas
names(CPUEModelResults3) = p$lfas
names(CPUEModelResults4) = p$lfas
AICs = data.frame(rbind(AICs1,AICs2,AICs3,AICs4))
names(AICs) = p$lfas
AICs
AICtable=sweep(AICs,2,FUN='-',apply(AICs,2,min))
pData=list()
for(i in 1:length( p$lfas)){
pData[[i]]=CPUEModelResults1[[i]]$pData
pData[[i]]$LFA=p$lfas[i]
}
CPUEindex=do.call("rbind",pData)
write.csv(AICtable,file.path( figdir,"CPUEmodelAIC.csv"),row.names=F)
write.csv(CPUEindex,file.path( figdir,"CPUEmodelindex.csv"),row.names=F)
#CPUECombinedModelResults = CPUEmodel(mf5,CPUE.data,combined=T)
cpue1= CPUEModelPlot(CPUEModelResults1,TempModelling,lfa = p$lfas,xlim=c(1989,2018.4),ylim=c(0,10.5),graphic='R',path=figdir,lab=1,wd=11,ht=8)
ty=sapply(1:length(p$lfas),function(x){with(subset(CPUE.data,DOS==1&LFA==p$lfas[x]),tapply(TEMP,SYEAR,mean))})
fs=lobster.db('season.dates')
d=merge(data.frame(LFA=rep(p$lfas,lapply(ty,length)),SYEAR=names(unlist(ty)),TEMP2=unlist(ty)),fs[,c('LFA','SYEAR','START_DATE')])
names(d)[2]="YEAR"
CPUEindex = merge(d,CPUEindex)
m2=subset(cpue1,DOS==1,c("LFA","YEAR","mu"))
names(m2)[3]="mu2"
CPUEindex = merge(CPUEindex,m2)
write.csv(subset(CPUEindex,LFA==34&YEAR%in%2005:2018,c("LFA","YEAR","START_DATE","TEMP2","TEMP","mu","mu2")),file.path( figdir,"figures","Brad","CPUEmodelindex34.csv"),row.names=F)
write.csv(subset(CPUEindex,LFA==35&YEAR%in%2005:2018,c("LFA","YEAR","START_DATE","TEMP2","TEMP","mu","mu2")),file.path( figdir,"figures","Brad","CPUEmodelindex35.csv"),row.names=F)
write.csv(subset(CPUEindex,LFA==36&YEAR%in%2005:2018,c("LFA","YEAR","START_DATE","TEMP2","TEMP","mu","mu2")),file.path( figdir,"figures","Brad","CPUEmodelindex36.csv"),row.names=F)
write.csv(subset(CPUEindex,LFA==38&YEAR%in%2005:2018,c("LFA","YEAR","START_DATE","TEMP2","TEMP","mu","mu2")),file.path( figdir,"figures","Brad","CPUEmodelindex38.csv"),row.names=F)
#pdf2png(file.path(figdir,"CPUEmodel1"))
cpueLFA.dat = CPUEplot(CPUE.data,lfa= p$lfas,yrs=1989:2018,graphic='png',export=T,path=figdir)
cpue.annual=list()
for(i in 1:length(p$lfas)){
MU=c()
MU.sd=c()
for(j in 1:length(p$yrs)){
MU[j]=with(subset(cpue1,LFA==p$lfas[i]&YEAR==p$yrs[j]),weighted.mean(mu,WEIGHT_KG))
MU.sd[j]=with(subset(cpue1,LFA==p$lfas[i]&YEAR==p$yrs[j]),sqrt(sum(WEIGHT_KG/sum(WEIGHT_KG) * (mu - MU[j])^2)))
}
#MU=with(subset(cpue1,LFA==p$lfas[i]),tapply(mu,YEAR,weighted.mean,WEIGHT_KG))
#MU.sd=with(subset(cpue1,LFA==p$lfas[i]),tapply(mu,YEAR,sd))
#xm <- weighted.mean(x, wt)y6723
#v <- sum(wt * (x - xm)^2)
cpue.annual[[i]] = data.frame(Area=p$lfas[i],Year=p$yrs,CPUE=MU,CPUE.ub=MU+MU.sd,CPUE.lb=MU-MU.sd)
#cpue.annual[[i]] = with(CPUEModelResults1[[i]]$pData,data.frame(Area=p$lfas[i],Year=YEAR,CPUE=mu,CPUE.ub=ub,CPUE.lb=lb))
}
cpueModel = subset(do.call("rbind",cpue.annual),Year<2019)
#x11()
#pdf(file.path( figdir,"CPUEmodelAnnualIndex.pdf"),8, 10)
par(mfrow=c(length(p$lfas),1),mar=c(0,0,0,0),omi=c(0.5,1,0.5,0.5),las=1)
for(i in 1:length(p$lfas)){
plot(mu~YEAR,CPUEModelResults1[[i]]$pData,type='b',pch=21,bg='red',ylim=c(0,7),xlim=c(1989,2018),xaxt='n')
points(CPUE~YEAR,subset(cpueLFA.dat$annual.dat,LFA==p$lfas[i]&YEAR<2019),pch=16,col='blue',cex=0.9)
#lines(ub~YEAR,CPUEModelResults1[[i]]$pData,lty=2)
#lines(lb~YEAR,CPUEModelResults1[[i]]$pData,lty=2)
axis(1,lab=F)
axis(4)
if(i==length(p$lfas))axis(1)
text(1989,6,paste(p$lfas[i]),cex=2,pos=4)
}
mtext("CPUE (kg/TH)", 2, 3, outer = T, cex = 1,las=0)
dev.off()
cpueData2= CPUEplot(CPUE.data,lfa= p$lfas,yrs=1981:2018,graphic='R')$annual.data
save(list=c("cpueModel","cpueData2"),file=file.path(project.datadirectory("bio.lobster"),"outputs","cpueIndicators3438.rdata"))
save(cpueData2,file=file.path(project.datadirectory("bio.lobster"),"outputs","cpueIndicators3438_2.rdata"))
#write.csv(cpueLFA.dat$annual.data,"CPUEannualData.csv",row.names=F)
#write.csv(na.omit(cpueLFA.dat$daily.data),"CPUEdailyData.csv",row.names=F)
load(file=file.path(project.datadirectory("bio.lobster"),"outputs","cpueIndicators3438.rdata"))
# Landings and Effort ############
land = lobster.db('seasonal.landings')
land$YEAR = as.numeric(substr(land$SYEAR,6,9))
for(i in 1:length(p$lfas)){
d1 = data.frame(YEAR = land$YEAR, LANDINGS = land[,paste0("LFA",p$lfas[i])])
d2 = subset(cpueData2,LFA==p$lfas[i])
d2 = merge(data.frame(LFA=d2$LFA[1],YEAR=min(d2$YEAR):max(d2$YEAR)),d2,all.x=T)
fishData = merge(d2,d1)
fishData$EFFORT2 = fishData$LANDINGS * 1000 / fishData$CPUE
# plot
x11(width=8,height=5)
FisheryPlot(fishData[,c("YEAR","LANDINGS","EFFORT2")],lfa = p$lfas[i],fd=figdir)
}
## FSRS MOdels
#Base
FSRSvesday = FSRSModelData()
FSRSvesday = subset(FSRSvesday,SYEAR<2019)
FSRSModelResultsRecruit = list()
FSRSModelResultsShort = list()
FSRSModelResultsLegal = list()
shorts.lst = list()
legals.lst = list()
recruit.lst = list()
p$areas = c('34','35')
for(i in 1:length( p$areas)){
mdata = subset(FSRSvesday,LFA==p$areas[i])
FSRSModelResultsShort[[i]]=FSRSmodel(mdata, response="SHORTS",interaction=F)
pdata = FSRSModelResultsShort[[i]]$pData
pdata$Area = p$areas[i]
shorts.lst[[i]] = pdata
FSRSModelResultsLegal[[i]]=FSRSmodel(mdata, response="LEGALS",interaction=F)
pdata = FSRSModelResultsLegal[[i]]$pData
pdata$Area = p$areas[i]
legals.lst[[i]] = pdata
FSRSModelResultsRecruit[[i]]=FSRSmodel(mdata, response="RECRUITS",interaction=F)
pdata = FSRSModelResultsRecruit[[i]]$pData
pdata$Area = p$areas[i]
recruit.lst[[i]] = pdata
}
names(FSRSModelResultsShort) = p$areas
names(FSRSModelResultsLegal) = p$areas
names(FSRSModelResultsRecruit) = p$areas
shorts = do.call("rbind",shorts.lst)
legals = do.call("rbind",legals.lst)
recruit = do.call("rbind",recruit.lst)
library(ggplot2)
pdf(file.path( figdir,"FSRSmodelBase.pdf"),8, 10)
sp <- ggplot()
sp <- sp + geom_point(data = shorts, aes(y = mu, x = YEAR), shape = 16, size = 2)
sp <- sp + xlab("Year") + ylab("Lobsters / Trap")
sp <- sp + theme(text = element_text(size=15)) + theme_bw()
sp <- sp + geom_line(data = shorts, aes(x = YEAR, y = mu), colour = "black")
sp <- sp + geom_ribbon(data = shorts, aes(x = YEAR, ymax = ub, ymin = lb ), alpha = 0.5)
sp <- sp + facet_wrap( ~Area, ncol=1,scales = "fixed")
sp
lp <- ggplot()
lp <- lp + geom_point(data = legals, aes(y = mu, x = YEAR), shape = 16, size = 2)
lp <- lp + xlab("Year") + ylab("Lobsters / Trap")
lp <- lp + theme(text = element_text(size=15)) + theme_bw()
lp <- lp + geom_line(data = legals, aes(x = YEAR, y = mu), colour = "black")
lp <- lp + geom_ribbon(data = legals, aes(x = YEAR, ymax = ub, ymin = lb ), alpha = 0.5)
lp <- lp + facet_wrap( ~Area, ncol=1,scales = "fixed")
lp
rp <- ggplot()
rp <- rp + geom_point(data = recruit, aes(y = mu, x = YEAR), shape = 16, size = 2)
rp <- rp + xlab("Year") + ylab("Lobsters / Trap")
rp <- rp + theme(text = element_text(size=15)) + theme_bw()
rp <- rp + geom_line(data = recruit, aes(x = YEAR, y = mu), colour = "black")
rp <- rp + geom_ribbon(data = recruit, aes(x = YEAR, ymax = ub, ymin = lb ), alpha = 0.5)
rp <- rp + facet_wrap( ~Area, ncol=1,scales = "fixed")
rp
dev.off()
#Bayes
FSRSvesday<-FSRSModelData()
FSRSvesday = subset(FSRSvesday,SYEAR<2019)
#FSRSvesdayComm<-FSRSModelData(trap.type="commercial")
FSRSModelResultsRecruit = list()
FSRSModelResultsShort = list()
FSRSModelResultsLegal = list()
shorts.lst = list()
legals.lst = list()
recruit.lst = list()
for(i in 1:length( p$areas)){
st = Sys.time()
mdata = subset(FSRSvesday,subarea==p$areas[i])
FSRSModelResultsShort[[i]]=FSRSmodel(mdata, response="SHORTS",interaction=F,type="bayesian",iter=5000,redo=T,ptraps=1000)
pdata = FSRSModelResultsShort[[i]]$pData
pdata$Area = p$areas[i]
shorts.lst[[i]] = pdata
FSRSModelResultsLegal[[i]]=FSRSmodel(mdata, response="LEGALS",interaction=F,type="bayesian",iter=5000,redo=T,ptraps=1000)
pdata = FSRSModelResultsLegal[[i]]$pData
pdata$Area = p$areas[i]
legals.lst[[i]] = pdata
FSRSModelResultsRecruit[[i]]=FSRSmodel(mdata, response="RECRUITS",interaction=F,type="bayesian",iter=5000,redo=T,ptraps=1000)
pdata = FSRSModelResultsRecruit[[i]]$pData
pdata$Area = p$areas[i]
recruit.lst[[i]] = pdata
print( Sys.time() - st)
}
names(FSRSModelResultsShort) = p$areas
names(FSRSModelResultsLegal) = p$areas
names(FSRSModelResultsRecruit) = p$areas
shorts = do.call("rbind",shorts.lst)
legals = do.call("rbind",legals.lst)
recruit = do.call("rbind",recruit.lst)
library(ggplot2)
#pdf(file.path( figdir,"FSRSmodelBayesShorts.pdf"),8, 10)
png(file.path(figdir,"FSRSmodelBayesShorts.png"),width=8,height=10,units='in',res=200)
sp <- ggplot()
sp <- sp + geom_point(data = shorts, aes(y = median, x = YEAR), shape = 16, size = 2)
sp <- sp + xlab("Year") + ylab("Lobsters / Trap")
sp <- sp + theme(text = element_text(size=15)) + theme_bw()
sp <- sp + geom_line(data = shorts, aes(x = YEAR, y = median), colour = "black")
sp <- sp + geom_ribbon(data = shorts, aes(x = YEAR, ymax = ub, ymin = lb ), alpha = 0.5)
sp <- sp + facet_wrap( ~Area, ncol=1,scales = "fixed")
sp
dev.off()
#pdf(file.path( figdir,"FSRSmodelBayesLegals.pdf"),8, 10)
png(file.path(figdir,"FSRSmodelBayesLegals.png"),width=8,height=10,units='in',res=200)
lp <- ggplot()
lp <- lp + geom_point(data = legals, aes(y = median, x = YEAR), shape = 16, size = 2)
lp <- lp + xlab("Year") + ylab("Lobsters / Trap")
lp <- lp + theme(text = element_text(size=15)) + theme_bw()
lp <- lp + geom_line(data = legals, aes(x = YEAR, y = median), colour = "black")
lp <- lp + geom_ribbon(data = legals, aes(x = YEAR, ymax = ub, ymin = lb ), alpha = 0.5)
lp <- lp + facet_wrap( ~Area, ncol=1,scales = "fixed")
lp
dev.off()
#pdf(file.path( figdir,"FSRSmodelBayesRecruits.pdf"),8, 10)
png(file.path(figdir,"FSRSmodelBayesRecruits.png"),width=8,height=10,units='in',res=200)
rp <- ggplot()
rp <- rp + geom_point(data = recruit, aes(y = median, x = YEAR), shape = 16, size = 2)
rp <- rp + xlab("Year") + ylab("Lobsters / Trap")
rp <- rp + theme(text = element_text(size=15)) + theme_bw()
rp <- rp + geom_line(data = recruit, aes(x = YEAR, y = median), colour = "black")
rp <- rp + geom_ribbon(data = recruit, aes(x = YEAR, ymax = ub, ymin = lb ), alpha = 0.5)
rp <- rp + facet_wrap( ~Area, ncol=1,scales = "fixed")
rp
dev.off()
save(list=c("shorts","legals","recruit"),file=file.path(project.datadirectory("bio.lobster"),"outputs","fsrsModelIndicators3435.rdata"))
TempModelling = TempModel(areas = 'subarea')
TempModelPlot(TempModelling,xlim=c(1980,2018),depths=c(5,25,50),Area=c("27N","27S", "29", "30","31A","31B", "32", "33E", "33W"),graphic='png',type=1:2)
tempModel=TempModelPlot(TempModelling,xlim=c(1980,2018),depths=c(5,25,50),Area=c("27N","27S", "29", "30","31A","31B", "32", "33E", "33W"),graphic='png',type=3)
tempData=TempModelPlot(TempModelling,xlim=c(1980,2018),depths=c(5,25,50),Area=c("27N","27S", "29", "30","31A","31B", "32", "33E", "33W"),graphic='png',type=4)
save(list=c("tempModel","tempData"),file=file.path(project.datadirectory("bio.lobster"),"outputs","tempIndicators.rdata"))
tempData2=TempModelPlot(TempModelling,xlim=c(1980,2018),depths=c(5,25,50),Area=c("27", "33"),lfa=T,graphic='R',type=4)
save(tempData2,file=file.path(project.datadirectory("bio.lobster"),"outputs","tempIndicators2.rdata"))
# map
pdf(file.path( figdir,"ScallopSurveyBubblesBoF2018.pdf"),11,8)
LobsterMap("BoF",mapRes="UR",title="BoF Scallop Survey",isobath=seq(50,500,50),bathcol=rgb(0,0,1,0.2),bathy.source='bathy')
points(y~x,zeros,pch=4)
surveyBubbles(data,scaler=0.08,pie=T)
dev.off()
########### CCIR
load(file.path(project.datadirectory('bio.lobster'),'data','inputs','ccir_groupings.rdata')) #object names Groupings
lobster.db('ccir.redo')
ccir_data = subset(ccir_data,YEAR<2019)
inp = read.csv(file.path(project.datadirectory('bio.lobster'),'data','inputs','ccir_inputs.csv'))
# fill in table where data is missing for recent years
inp.lst=list()
lfas = unique(inp$LFA)
for(i in 1:length(lfas)){
inpt = subset(inp,LFA==lfas[i])
maxyr=max(inpt$Year)
inp.lst[[i]] = rbind(inpt, data.frame(LFA=lfas[i],Year=(maxyr+1):assessment.year,inpt[inpt$Year==maxyr,3:ncol(inpt)]))
}
inp = do.call("rbind",inp.lst)
write.csv(inp,file.path(project.datadirectory('bio.lobster'),'data','inputs',paste0('ccir_inputs',assessment.year,'.csv')))
inp = read.csv(file.path(project.datadirectory('bio.lobster'),'data','inputs',paste0('ccir_inputs',assessment.year,'.csv')))
inp33 = subset(inp,LFA==33)
inp34 = inp33
inp34$LFA = 34
inp35 = inp33
inp35$LFA = 35
inp=rbind(inp,inp34,inp35)
load(file.path(project.datadirectory('bio.lobster'),'data','inputs','ccir_groupings.rdata')) #object names Groupings
load(file.path(project.datadirectory('bio.lobster'),'data','inputs','ccir_seasons.rdata'))
lobster.db('ccir')
logs = lobster.db('process.logs')
require(bio.ccir)
require(rstan)
load_all(paste(git.repo,'bio.ccir',sep="/")) # for debugging
dat = ccir_compile_data(x = ccir_data,log.data = logs, area.defns = Groupings[1:6], size.defns = inp, season.defns = Seasons, sexs = 1.5) #sexs 1.5 means no sex defn
lobster.db('ccir.redo')
ccir_data = subset(ccir_data,YEAR<2019)
# inp = read.csv(file.path(project.datadirectory('bio.lobster'),'data','inputs','ccir_inputs.csv'))
#
# # fill in table where data is missing for recent years
# inp.lst=list()
# lfas = unique(inp$LFA)
# for(i in 1:length(lfas)){
# inpt = subset(inp,LFA==lfas[i])
# maxyr=max(inpt$Year)
# inp.lst[[i]] = rbind(inpt, data.frame(LFA=lfas[i],Year=(maxyr+1):assessment.year,inpt[inpt$Year==maxyr,3:ncol(inpt)]))
# }
# inp = do.call("rbind",inp.lst)
#
# write.csv(inp,file.path(project.datadirectory('bio.lobster'),'data','inputs',paste0('ccir_inputs',assessment.year,'.csv')))
#
# inp = read.csv(file.path(project.datadirectory('bio.lobster'),'data','inputs',paste0('ccir_inputs',assessment.year,'.csv')))
# inp33 = subset(inp,LFA==33)
# inp34 = inp33
# inp34$LFA = 34
# inp35 = inp33
# inp35$LFA = 35
# inp=rbind(inp,inp34,inp35)
# write.csv(inp,file.path(project.datadirectory('bio.lobster'),'data','inputs',paste0('ccir_inputs',assessment.year,'.csv')))
inp = read.csv(file.path(project.datadirectory('bio.lobster'),'data','inputs',paste0('ccir_inputs',assessment.year,'.csv')))
load(file.path(project.datadirectory('bio.lobster'),'data','inputs','ccir_groupings.rdata')) #object names Groupings
load(file.path(project.datadirectory('bio.lobster'),'data','inputs','ccir_seasons.rdata'))
lobster.db('ccir')
gridsbylfa=with(ccir_data,tapply(Grid,LFA,unique))
groups3435=list(list(lfa=34,G1=gridsbylfa$'34'),list(lfa=35,G1=gridsbylfa$'35'))
logs = lobster.db('process.logs')
require(bio.ccir)
require(rstan)
load_all(paste(git.repo,'bio.ccir',sep="/")) # for debugging
dat = ccir_compile_data(x = ccir_data,log.data = logs, area.defns = groups3435[1], size.defns = inp, season.defns = Seasons, sexs = 1.5) #sexs 1.5 means no sex defn
out.binomial = list()
attr(out.binomial,'model') <- 'binomial'
for(i in 1:length(dat)) {
ds = dat[[i]]
ds$method = 'binomial'
x = ccir_stan_run(dat = ds,save=F)
out.binomial[[i]] <- ccir_stan_summarize(x)
}
ouBin = ccir_collapse_summary(out.binomial)
attr(ouBin,'model') <- 'binomial'
#ouBin$Yr = ouBin$Yr +1
save(ouBin,file=file.path(project.datadirectory('bio.lobster'),'outputs','ccir','summary','compiledBinomialModels2732.rdata'))
#load(file=file.path(project.datadirectory('bio.lobster'),'outputs','ccir','summary','compiledBinomialModels2732.rdata'))
u = subset(ouBin, LFA == 27)
g = unique(u$Grid)
g = strsplit(g,"\\.")
o = aggregate(WEIGHT_KG~SYEAR,data=subset(logs,GRID_NUM %in% g[[1]]),FUN=sum)
names(o)[2] = g[[1]][1]
o2 = aggregate(WEIGHT_KG~SYEAR,data=subset(logs,GRID_NUM %in% g[[2]]),FUN=sum)
names(o2)[2] = g[[2]][1]
o = merge(o,o2)
names(o)[1] = 'Yr'
oo <- ccir_timeseries_exploitation_plots(ouBin,combined.LFA=T,landings=o)
u = subset(ouBin, LFA != 27)
kl = unique(u$Grid)
outs=list()
for(i in 1:length(kl)) {
u = subset(ouBin, Grid == kl[i])
outs[[i]] <- ccir_timeseries_exploitation_plots(u)
}
o = do.call(rbind,outs)
ooo = subset(o,select=c(Yr,ERfl,ERfm,ERfu,ERf75,LFA))
oo = rbind(oo,ooo)
oo$LFA[oo$LFA == "LFA 27 Combined"] = 27
oo$LFA[oo$LFA == "LFA 29"] = 29
oo$LFA[oo$LFA == "LFA 30"] = 30
oo$LFA[oo$LFA == "LFA 31A"] = "31A"
oo$LFA[oo$LFA == "LFA 31B"] = "31B"
oo$LFA[oo$LFA == "LFA 32"] = 32
save(oo,file=file.path(project.datadirectory('bio.lobster'),'outputs','ccir','summary','compiledExploitationCCIR2732.rdata'))
load(file=file.path(project.datadirectory('bio.lobster'),'outputs','ccir','summary','compiledExploitationCCIR2732.rdata'))
RR75 = aggregate(ERf75~LFA,data=oo[oo$Yr<2017,],FUN=max)
ouBin = subset(ouBin,Yr<2018&LFA==34,-2)
save(ouBin,file=file.path(project.datadirectory('bio.lobster'),'outputs','ccir','summary','compiledBinomialModels34.rdata'))
load(file=file.path(project.datadirectory('bio.lobster'),'outputs','ccir','summary','compiledBinomialModels34.rdata'))
oo <- ccir_timeseries_exploitation_plots(ouBin,Main="34")
save(oo,file=file.path(project.datadirectory('bio.lobster'),'outputs','ccir','summary','compiledExploitationCCIR34.rdata'))
load(file=file.path(project.datadirectory('bio.lobster'),'outputs','ccir','summary','compiledExploitationCCIR33.rdata'))
oo33=oo
load(file=file.path(project.datadirectory('bio.lobster'),'outputs','ccir','summary','compiledExploitationCCIR34.rdata'))
RR75 = aggregate(ERf75~LFA,data=oo[oo$Yr<2018,],FUN=max)$ERf75
# plot
x11(width=8,height=5)
ExploitationRatePlots(data = oo[,c("Yr","ERfm","ERfl","ERfu")],lfa = 34,fd=figdir,runM=F)
load(file=file.path(project.datadirectory('bio.lobster'),'outputs','ccir','summary','compiledBinomialModels33.rdata'))
with(subset(ouBin,LFA=='33W'),lines(Yr,ERfm,lty=2,col='blue'))
ExploitationRatePlots(data = oo[,c("Yr","ERfm","ERfl","ERfu")],lrp=RR75,lfa = 34,fd=figdir)
}
######################## sim Molt
LobsterScience/bio.lobster documentation built on Feb. 14, 2025, 3:28 p.m.
R Package Documentation
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p = bio.lobster::load.environment()
la()
assessment.year = 2019 ########### check the year ############### !!!!!!!!!!!
p$syr = 1989
p$yrs = p$syr:assessment.year
ff = "LFA35-38Assessment"
figdir = file.path(project.figuredirectory('bio.lobster'),ff)
p$lfas = c( "35", "36", "38") # specify lfas for data summary
p$subareas = c( "35", "36", "38") # specify lfas for data summary
logsInSeason=lobster.db("process.logs")
## Fishery Footprint - Landings
catchLevels = c(0,100000,200000,300000,400000,500000,600000,700000,800000)
yrs = 2011:2018
for(i in 1:length(yrs)){
catchgrids = lobGridPlot(subset(logsInSeason,LFA%in%p$lfas&SYEAR==2016,c("LFA","GRID_NUM","TOTAL_WEIGHT_KG")),FUN=sum,lvls=catchLevels)
pdf(file.path(figdir,paste0("FisheryFootprint",yrs[i],".pdf")))
LobsterMap('34-38',poly.lst=catchgrids)
title(yrs[i],line=-3,cex.main=2,adj=0.3)
SpatialHub::contLegend('bottomright',lvls=catchgrids$lvls/1000,Cont.data=catchgrids,title="Catch (tons)",inset=0.02,cex=0.8,bg='white')
dev.off()
}
## Fishery Footprint - CPUE
cpueLevels = c(0,0.2,0.4,0.6,0.8,0.9,1,2,3)
yrs = 2011:2018
#logsInSeason$logCPUE = log(logsInSeason$CPUE+1)
for(i in 1:length(yrs)){
cpuegrids = lobGridPlot(subset(logsInSeason,LFA%in%p$lfas&SYEAR == yrs[i],c("LFA","GRID_NUM","CPUE")),FUN=median,lvls=cpueLevels)
pdf(file.path(figdir,paste0("FishFootcpue", yrs[i],".pdf")))
LobsterMap('34-38',poly.lst=cpuegrids)
title(yrs[i],line=-3,cex.main=2,adj=0.3)
SpatialHub::contLegend('bottomright',lvls=cpuegrids$lvls,Cont.data=cpuegrids,title="CPUE (kg/TH)",inset=0.02,cex=0.8,bg='white')
dev.off()
}
## Fishery Footprint - Mean Pots Hauled
potLevels = c (0,1000,100000,200000,300000,400000,500000,600000)
yrs = 2011:2018
for(i in 1:length(yrs)){
potgrids = lobGridPlot(subset(logsInSeason,LFA%in%p$lfas&SYEAR == yrs[i],c("LFA","GRID_NUM","NUM_OF_TRAPS")),FUN=sum,lvls=potLevels)
pdf(file.path(figdir,paste0("FishFootpot", yrs[i],".pdf")))
LobsterMap('34-38',poly.lst=potgrids)
title(yrs[i],line=-3,cex.main=2,adj=0.3)
SpatialHub::contLegend('bottomright',lvls=potgrids$lvls/1000,Cont.data=potgrids,title="Pots Hauled (000s)",inset=0.02,cex=0.8,bg='white')
dev.off()
}
## Fishery Footprint - Days Fished
daysLevels = c(0,500,1000,1500,2000,2500,3000)
daysFished<-aggregate(DATE_FISHED ~ SYEAR + LFA + GRID_NUM + LICENCE_ID, data=logsInSeason,FUN= function(x) length(unique(x)))
yrs = 2011:2018
for (i in 1: length(yrs)){
daysgrids = lobGridPlot(subset(daysFished, LFA%in%p$lfas&SYEAR == yrs[i],c("LFA", "GRID_NUM", "DATE_FISHED")),FUN=sum, lvls= daysLevels)
pdf(file.path(figdir,paste0("FishFootDaysFished", yrs[i],".pdf")))
LobsterMap('34-38',poly.lst=daysgrids)
title(yrs[i],line=-3,cex.main=2,adj=0.3)
SpatialHub::contLegend('bottomright',lvls=daysgrids$lvls,Cont.data=daysgrids,title="Total Days Fished",inset=0.02,cex=0.8,bg='white')
dev.off()
}
## Fishery Footprint - Licences Fished
licenceLevels = c(0,15,30,45,60,75,90,105,120)
yrs=2011:2018
daysFished$LICENCE<-1
for(i in 1: length(yrs)){
licencegrids = lobGridPlot(subset(daysFished, LFA%in%p$lfas&SYEAR==yrs[i], c("LFA", "GRID_NUM", "LICENCE")), FUN=sum, lvls= licenceLevels)
pdf(file.path(figdir,paste0("FishFootLicenceFished", yrs[i],".pdf")))
LobsterMap('34-38', poly.lst=licencegrids)
title(yrs[i],line=-3,cex.main=2,adj=0.3)
SpatialHub::contLegend('bottomright', lvls=licencegrids$lvls, Cont.data=licencegrids, title= "Number of Licence Fished", inset =0.02,cex=0.8,bg='white')
dev.off()
}
## CPUE
logsInSeason<-lobster.db('process.logs.redo')
logsInSeason<-lobster.db('process.logs')
write.csv(logsInSeason,file.path(project.datadirectory("bio.lobster"),'data',"Logs.csv"),row.names=F)
cpueLFA.dat = CPUEplot(logsInSeason,lfa= p$lfas,yrs=2002:2019,graphic='R',export=F)
cpueLFA.dat = CPUEplot(logsInSeason,lfa= p$lfas,yrs=2006:2019,graphic='pdf',path=figdir)
cpueSubArea.dat = CPUEplot(logsInSeason,subarea= p$subareas,yrs=2006:2019,graphic='R')
## Commercial CPUE MOdels
mf1 = formula(logWEIGHT ~ fYEAR + DOS + TEMP + DOS * TEMP)
mf2 = formula(logWEIGHT ~ fYEAR + DOS + TEMP)
mf3 = formula(logWEIGHT ~ fYEAR + DOS)
mf4 = formula(logWEIGHT ~ fYEAR + TEMP)
#mf5 = formula(logWEIGHT ~ fYEAR + DOS + TEMP + (1 | fYEAR/fAREA)) # combined
TempModelling = TempModel( annual.by.area=F)
#CPUE.data<-CPUEModelData(p,redo=T,TempModelling)
CPUE.data<- CPUEModelData(p,redo=F)
CPUE.data=subset(CPUE.data,!(LFA==35&SYEAR<2006)) #exclude partial year of data in 35
CPUE.data=subset(CPUE.data,!(LFA==36&SYEAR<2005)) #exclude partial year of data in 36
#Modelled Temperature at first day of season
pL=0
t=c()
d=c()
k=1
for(i in 1:length(p$lfas)){
for(j in 2:length(p$yrs)){
Cdat=subset(CPUE.data,LFA==p$lfas[i]&SYEAR==p$yrs[j])
Cdat=Cdat[order(Cdat$DATE_FISHED),]
Cdat$pLanded = cumsum(Cdat$WEIGHT_KG)/sum(Cdat$WEIGHT_KG)
if(nrow(Cdat)>1){
x=abs(Cdat$pLanded-pL)
d[k]=Cdat$DOS[which(x==min(x))]
t[k]=Cdat$TEMP[which(x==min(x))]
names(d)[k]=paste(p$lfas[i],p$yrs[j],sep='.')
names(t)[k]=paste(p$lfas[i],p$yrs[j],sep='.')
k=k+1
}
}
}
##t and d Overwrite with summary
t=with(subset(CPUE.data,DOS==1),tapply(TEMP,LFA,mean))
d=1
aicc = function(aa = model.output) {
k = attr(logLik(aa),'df')
logLiks = logLik(aa)[1]
n = nobs(aa)
aaa = 2*nParams-2*logLiks
aaa + (2*k^2+2*k) / (n-k-1)
}
pData=list()
CPUEModelResults1 = list()
CPUEModelResults2 = list()
CPUEModelResults3 = list()
CPUEModelResults4 = list()
AICs1 = c()
AICs2 = c()
AICs3 = c()
AICs4 = c()
for(i in 1:length( p$lfas)){
mdata = subset(CPUE.data,LFA==p$lfas[i]&SYEAR%in%p$yrs)
CPUEModelResults1[[i]] = CPUEmodel(mf1,mdata,t=t[i],d=d)
CPUEModelResults2[[i]] = CPUEmodel(mf2,mdata,t=t[i],d=d)
CPUEModelResults3[[i]] = CPUEmodel(mf3,mdata,t=t[i],d=d)
CPUEModelResults4[[i]] = CPUEmodel(mf4,mdata,t=t[i],d=d)
AICs1[i] = aicc(CPUEModelResults1[[i]]$model)
AICs2[i] = aicc(CPUEModelResults2[[i]]$model)
AICs3[i] = aicc(CPUEModelResults3[[i]]$model)
AICs4[i] = aicc(CPUEModelResults4[[i]]$model)
}
names(CPUEModelResults1) = p$lfas
names(CPUEModelResults2) = p$lfas
names(CPUEModelResults3) = p$lfas
names(CPUEModelResults4) = p$lfas
AICs = data.frame(rbind(AICs1,AICs2,AICs3,AICs4))
names(AICs) = p$lfas
AICs
AICtable=sweep(AICs,2,FUN='-',apply(AICs,2,min))
pData=list()
for(i in 1:length( p$lfas)){
pData[[i]]=CPUEModelResults1[[i]]$pData
pData[[i]]$LFA=p$lfas[i]
}
CPUEindex=do.call("rbind",pData)
write.csv(AICtable,file.path( figdir,"CPUEmodelAIC.csv"),row.names=F)
write.csv(CPUEindex,file.path( figdir,"CPUEmodelindex.csv"),row.names=F)
#CPUECombinedModelResults = CPUEmodel(mf5,CPUE.data,combined=T)
cpue1= CPUEModelPlot(CPUEModelResults1,TempModelling,lfa = p$lfas,xlim=c(1989,2018.4),ylim=c(0,10.5),graphic='R',path=figdir,lab=1,wd=11,ht=8)
ty=sapply(1:length(p$lfas),function(x){with(subset(CPUE.data,DOS==1&LFA==p$lfas[x]),tapply(TEMP,SYEAR,mean))})
fs=lobster.db('season.dates')
d=merge(data.frame(LFA=rep(p$lfas,lapply(ty,length)),SYEAR=names(unlist(ty)),TEMP2=unlist(ty)),fs[,c('LFA','SYEAR','START_DATE')])
names(d)[2]="YEAR"
CPUEindex = merge(d,CPUEindex)
m2=subset(cpue1,DOS==1,c("LFA","YEAR","mu"))
names(m2)[3]="mu2"
CPUEindex = merge(CPUEindex,m2)
write.csv(subset(CPUEindex,LFA==34&YEAR%in%2005:2018,c("LFA","YEAR","START_DATE","TEMP2","TEMP","mu","mu2")),file.path( figdir,"figures","Brad","CPUEmodelindex34.csv"),row.names=F)
write.csv(subset(CPUEindex,LFA==35&YEAR%in%2005:2018,c("LFA","YEAR","START_DATE","TEMP2","TEMP","mu","mu2")),file.path( figdir,"figures","Brad","CPUEmodelindex35.csv"),row.names=F)
write.csv(subset(CPUEindex,LFA==36&YEAR%in%2005:2018,c("LFA","YEAR","START_DATE","TEMP2","TEMP","mu","mu2")),file.path( figdir,"figures","Brad","CPUEmodelindex36.csv"),row.names=F)
write.csv(subset(CPUEindex,LFA==38&YEAR%in%2005:2018,c("LFA","YEAR","START_DATE","TEMP2","TEMP","mu","mu2")),file.path( figdir,"figures","Brad","CPUEmodelindex38.csv"),row.names=F)
#pdf2png(file.path(figdir,"CPUEmodel1"))
cpueLFA.dat = CPUEplot(CPUE.data,lfa= p$lfas,yrs=1989:2018,graphic='png',export=T,path=figdir)
cpue.annual=list()
for(i in 1:length(p$lfas)){
MU=c()
MU.sd=c()
for(j in 1:length(p$yrs)){
MU[j]=with(subset(cpue1,LFA==p$lfas[i]&YEAR==p$yrs[j]),weighted.mean(mu,WEIGHT_KG))
MU.sd[j]=with(subset(cpue1,LFA==p$lfas[i]&YEAR==p$yrs[j]),sqrt(sum(WEIGHT_KG/sum(WEIGHT_KG) * (mu - MU[j])^2)))
}
#MU=with(subset(cpue1,LFA==p$lfas[i]),tapply(mu,YEAR,weighted.mean,WEIGHT_KG))
#MU.sd=with(subset(cpue1,LFA==p$lfas[i]),tapply(mu,YEAR,sd))
#xm <- weighted.mean(x, wt)y6723
#v <- sum(wt * (x - xm)^2)
cpue.annual[[i]] = data.frame(Area=p$lfas[i],Year=p$yrs,CPUE=MU,CPUE.ub=MU+MU.sd,CPUE.lb=MU-MU.sd)
#cpue.annual[[i]] = with(CPUEModelResults1[[i]]$pData,data.frame(Area=p$lfas[i],Year=YEAR,CPUE=mu,CPUE.ub=ub,CPUE.lb=lb))
}
cpueModel = subset(do.call("rbind",cpue.annual),Year<2019)
#x11()
#pdf(file.path( figdir,"CPUEmodelAnnualIndex.pdf"),8, 10)
par(mfrow=c(length(p$lfas),1),mar=c(0,0,0,0),omi=c(0.5,1,0.5,0.5),las=1)
for(i in 1:length(p$lfas)){
plot(mu~YEAR,CPUEModelResults1[[i]]$pData,type='b',pch=21,bg='red',ylim=c(0,7),xlim=c(1989,2018),xaxt='n')
points(CPUE~YEAR,subset(cpueLFA.dat$annual.dat,LFA==p$lfas[i]&YEAR<2019),pch=16,col='blue',cex=0.9)
#lines(ub~YEAR,CPUEModelResults1[[i]]$pData,lty=2)
#lines(lb~YEAR,CPUEModelResults1[[i]]$pData,lty=2)
axis(1,lab=F)
axis(4)
if(i==length(p$lfas))axis(1)
text(1989,6,paste(p$lfas[i]),cex=2,pos=4)
}
mtext("CPUE (kg/TH)", 2, 3, outer = T, cex = 1,las=0)
dev.off()
cpueData2= CPUEplot(CPUE.data,lfa= p$lfas,yrs=1981:2018,graphic='R')$annual.data
save(list=c("cpueModel","cpueData2"),file=file.path(project.datadirectory("bio.lobster"),"outputs","cpueIndicators3438.rdata"))
save(cpueData2,file=file.path(project.datadirectory("bio.lobster"),"outputs","cpueIndicators3438_2.rdata"))
#write.csv(cpueLFA.dat$annual.data,"CPUEannualData.csv",row.names=F)
#write.csv(na.omit(cpueLFA.dat$daily.data),"CPUEdailyData.csv",row.names=F)
load(file=file.path(project.datadirectory("bio.lobster"),"outputs","cpueIndicators3438.rdata"))
# Landings and Effort ############
land = lobster.db('seasonal.landings')
land$YEAR = as.numeric(substr(land$SYEAR,6,9))
for(i in 1:length(p$lfas)){
d1 = data.frame(YEAR = land$YEAR, LANDINGS = land[,paste0("LFA",p$lfas[i])])
d2 = subset(cpueData2,LFA==p$lfas[i])
d2 = merge(data.frame(LFA=d2$LFA[1],YEAR=min(d2$YEAR):max(d2$YEAR)),d2,all.x=T)
fishData = merge(d2,d1)
fishData$EFFORT2 = fishData$LANDINGS * 1000 / fishData$CPUE
# plot
x11(width=8,height=5)
FisheryPlot(fishData[,c("YEAR","LANDINGS","EFFORT2")],lfa = p$lfas[i],fd=figdir)
}
## FSRS MOdels
#Base
FSRSvesday = FSRSModelData()
FSRSvesday = subset(FSRSvesday,SYEAR<2019)
FSRSModelResultsRecruit = list()
FSRSModelResultsShort = list()
FSRSModelResultsLegal = list()
shorts.lst = list()
legals.lst = list()
recruit.lst = list()
p$areas = c('34','35')
for(i in 1:length( p$areas)){
mdata = subset(FSRSvesday,LFA==p$areas[i])
FSRSModelResultsShort[[i]]=FSRSmodel(mdata, response="SHORTS",interaction=F)
pdata = FSRSModelResultsShort[[i]]$pData
pdata$Area = p$areas[i]
shorts.lst[[i]] = pdata
FSRSModelResultsLegal[[i]]=FSRSmodel(mdata, response="LEGALS",interaction=F)
pdata = FSRSModelResultsLegal[[i]]$pData
pdata$Area = p$areas[i]
legals.lst[[i]] = pdata
FSRSModelResultsRecruit[[i]]=FSRSmodel(mdata, response="RECRUITS",interaction=F)
pdata = FSRSModelResultsRecruit[[i]]$pData
pdata$Area = p$areas[i]
recruit.lst[[i]] = pdata
}
names(FSRSModelResultsShort) = p$areas
names(FSRSModelResultsLegal) = p$areas
names(FSRSModelResultsRecruit) = p$areas
shorts = do.call("rbind",shorts.lst)
legals = do.call("rbind",legals.lst)
recruit = do.call("rbind",recruit.lst)
library(ggplot2)
pdf(file.path( figdir,"FSRSmodelBase.pdf"),8, 10)
sp <- ggplot()
sp <- sp + geom_point(data = shorts, aes(y = mu, x = YEAR), shape = 16, size = 2)
sp <- sp + xlab("Year") + ylab("Lobsters / Trap")
sp <- sp + theme(text = element_text(size=15)) + theme_bw()
sp <- sp + geom_line(data = shorts, aes(x = YEAR, y = mu), colour = "black")
sp <- sp + geom_ribbon(data = shorts, aes(x = YEAR, ymax = ub, ymin = lb ), alpha = 0.5)
sp <- sp + facet_wrap( ~Area, ncol=1,scales = "fixed")
sp
lp <- ggplot()
lp <- lp + geom_point(data = legals, aes(y = mu, x = YEAR), shape = 16, size = 2)
lp <- lp + xlab("Year") + ylab("Lobsters / Trap")
lp <- lp + theme(text = element_text(size=15)) + theme_bw()
lp <- lp + geom_line(data = legals, aes(x = YEAR, y = mu), colour = "black")
lp <- lp + geom_ribbon(data = legals, aes(x = YEAR, ymax = ub, ymin = lb ), alpha = 0.5)
lp <- lp + facet_wrap( ~Area, ncol=1,scales = "fixed")
lp
rp <- ggplot()
rp <- rp + geom_point(data = recruit, aes(y = mu, x = YEAR), shape = 16, size = 2)
rp <- rp + xlab("Year") + ylab("Lobsters / Trap")
rp <- rp + theme(text = element_text(size=15)) + theme_bw()
rp <- rp + geom_line(data = recruit, aes(x = YEAR, y = mu), colour = "black")
rp <- rp + geom_ribbon(data = recruit, aes(x = YEAR, ymax = ub, ymin = lb ), alpha = 0.5)
rp <- rp + facet_wrap( ~Area, ncol=1,scales = "fixed")
rp
dev.off()
#Bayes
FSRSvesday<-FSRSModelData()
FSRSvesday = subset(FSRSvesday,SYEAR<2019)
#FSRSvesdayComm<-FSRSModelData(trap.type="commercial")
FSRSModelResultsRecruit = list()
FSRSModelResultsShort = list()
FSRSModelResultsLegal = list()
shorts.lst = list()
legals.lst = list()
recruit.lst = list()
for(i in 1:length( p$areas)){
st = Sys.time()
mdata = subset(FSRSvesday,subarea==p$areas[i])
FSRSModelResultsShort[[i]]=FSRSmodel(mdata, response="SHORTS",interaction=F,type="bayesian",iter=5000,redo=T,ptraps=1000)
pdata = FSRSModelResultsShort[[i]]$pData
pdata$Area = p$areas[i]
shorts.lst[[i]] = pdata
FSRSModelResultsLegal[[i]]=FSRSmodel(mdata, response="LEGALS",interaction=F,type="bayesian",iter=5000,redo=T,ptraps=1000)
pdata = FSRSModelResultsLegal[[i]]$pData
pdata$Area = p$areas[i]
legals.lst[[i]] = pdata
FSRSModelResultsRecruit[[i]]=FSRSmodel(mdata, response="RECRUITS",interaction=F,type="bayesian",iter=5000,redo=T,ptraps=1000)
pdata = FSRSModelResultsRecruit[[i]]$pData
pdata$Area = p$areas[i]
recruit.lst[[i]] = pdata
print( Sys.time() - st)
}
names(FSRSModelResultsShort) = p$areas
names(FSRSModelResultsLegal) = p$areas
names(FSRSModelResultsRecruit) = p$areas
shorts = do.call("rbind",shorts.lst)
legals = do.call("rbind",legals.lst)
recruit = do.call("rbind",recruit.lst)
library(ggplot2)
#pdf(file.path( figdir,"FSRSmodelBayesShorts.pdf"),8, 10)
png(file.path(figdir,"FSRSmodelBayesShorts.png"),width=8,height=10,units='in',res=200)
sp <- ggplot()
sp <- sp + geom_point(data = shorts, aes(y = median, x = YEAR), shape = 16, size = 2)
sp <- sp + xlab("Year") + ylab("Lobsters / Trap")
sp <- sp + theme(text = element_text(size=15)) + theme_bw()
sp <- sp + geom_line(data = shorts, aes(x = YEAR, y = median), colour = "black")
sp <- sp + geom_ribbon(data = shorts, aes(x = YEAR, ymax = ub, ymin = lb ), alpha = 0.5)
sp <- sp + facet_wrap( ~Area, ncol=1,scales = "fixed")
sp
dev.off()
#pdf(file.path( figdir,"FSRSmodelBayesLegals.pdf"),8, 10)
png(file.path(figdir,"FSRSmodelBayesLegals.png"),width=8,height=10,units='in',res=200)
lp <- ggplot()
lp <- lp + geom_point(data = legals, aes(y = median, x = YEAR), shape = 16, size = 2)
lp <- lp + xlab("Year") + ylab("Lobsters / Trap")
lp <- lp + theme(text = element_text(size=15)) + theme_bw()
lp <- lp + geom_line(data = legals, aes(x = YEAR, y = median), colour = "black")
lp <- lp + geom_ribbon(data = legals, aes(x = YEAR, ymax = ub, ymin = lb ), alpha = 0.5)
lp <- lp + facet_wrap( ~Area, ncol=1,scales = "fixed")
lp
dev.off()
#pdf(file.path( figdir,"FSRSmodelBayesRecruits.pdf"),8, 10)
png(file.path(figdir,"FSRSmodelBayesRecruits.png"),width=8,height=10,units='in',res=200)
rp <- ggplot()
rp <- rp + geom_point(data = recruit, aes(y = median, x = YEAR), shape = 16, size = 2)
rp <- rp + xlab("Year") + ylab("Lobsters / Trap")
rp <- rp + theme(text = element_text(size=15)) + theme_bw()
rp <- rp + geom_line(data = recruit, aes(x = YEAR, y = median), colour = "black")
rp <- rp + geom_ribbon(data = recruit, aes(x = YEAR, ymax = ub, ymin = lb ), alpha = 0.5)
rp <- rp + facet_wrap( ~Area, ncol=1,scales = "fixed")
rp
dev.off()
save(list=c("shorts","legals","recruit"),file=file.path(project.datadirectory("bio.lobster"),"outputs","fsrsModelIndicators3435.rdata"))
TempModelling = TempModel(areas = 'subarea')
TempModelPlot(TempModelling,xlim=c(1980,2018),depths=c(5,25,50),Area=c("27N","27S", "29", "30","31A","31B", "32", "33E", "33W"),graphic='png',type=1:2)
tempModel=TempModelPlot(TempModelling,xlim=c(1980,2018),depths=c(5,25,50),Area=c("27N","27S", "29", "30","31A","31B", "32", "33E", "33W"),graphic='png',type=3)
tempData=TempModelPlot(TempModelling,xlim=c(1980,2018),depths=c(5,25,50),Area=c("27N","27S", "29", "30","31A","31B", "32", "33E", "33W"),graphic='png',type=4)
save(list=c("tempModel","tempData"),file=file.path(project.datadirectory("bio.lobster"),"outputs","tempIndicators.rdata"))
tempData2=TempModelPlot(TempModelling,xlim=c(1980,2018),depths=c(5,25,50),Area=c("27", "33"),lfa=T,graphic='R',type=4)
save(tempData2,file=file.path(project.datadirectory("bio.lobster"),"outputs","tempIndicators2.rdata"))
# map
pdf(file.path( figdir,"ScallopSurveyBubblesBoF2018.pdf"),11,8)
LobsterMap("BoF",mapRes="UR",title="BoF Scallop Survey",isobath=seq(50,500,50),bathcol=rgb(0,0,1,0.2),bathy.source='bathy')
points(y~x,zeros,pch=4)
surveyBubbles(data,scaler=0.08,pie=T)
dev.off()
########### CCIR
load(file.path(project.datadirectory('bio.lobster'),'data','inputs','ccir_groupings.rdata')) #object names Groupings
lobster.db('ccir.redo')
ccir_data = subset(ccir_data,YEAR<2019)
inp = read.csv(file.path(project.datadirectory('bio.lobster'),'data','inputs','ccir_inputs.csv'))
# fill in table where data is missing for recent years
inp.lst=list()
lfas = unique(inp$LFA)
for(i in 1:length(lfas)){
inpt = subset(inp,LFA==lfas[i])
maxyr=max(inpt$Year)
inp.lst[[i]] = rbind(inpt, data.frame(LFA=lfas[i],Year=(maxyr+1):assessment.year,inpt[inpt$Year==maxyr,3:ncol(inpt)]))
}
inp = do.call("rbind",inp.lst)
write.csv(inp,file.path(project.datadirectory('bio.lobster'),'data','inputs',paste0('ccir_inputs',assessment.year,'.csv')))
inp = read.csv(file.path(project.datadirectory('bio.lobster'),'data','inputs',paste0('ccir_inputs',assessment.year,'.csv')))
inp33 = subset(inp,LFA==33)
inp34 = inp33
inp34$LFA = 34
inp35 = inp33
inp35$LFA = 35
inp=rbind(inp,inp34,inp35)
load(file.path(project.datadirectory('bio.lobster'),'data','inputs','ccir_groupings.rdata')) #object names Groupings
load(file.path(project.datadirectory('bio.lobster'),'data','inputs','ccir_seasons.rdata'))
lobster.db('ccir')
logs = lobster.db('process.logs')
require(bio.ccir)
require(rstan)
load_all(paste(git.repo,'bio.ccir',sep="/")) # for debugging
dat = ccir_compile_data(x = ccir_data,log.data = logs, area.defns = Groupings[1:6], size.defns = inp, season.defns = Seasons, sexs = 1.5) #sexs 1.5 means no sex defn
lobster.db('ccir.redo')
ccir_data = subset(ccir_data,YEAR<2019)
# inp = read.csv(file.path(project.datadirectory('bio.lobster'),'data','inputs','ccir_inputs.csv'))
#
# # fill in table where data is missing for recent years
# inp.lst=list()
# lfas = unique(inp$LFA)
# for(i in 1:length(lfas)){
# inpt = subset(inp,LFA==lfas[i])
# maxyr=max(inpt$Year)
# inp.lst[[i]] = rbind(inpt, data.frame(LFA=lfas[i],Year=(maxyr+1):assessment.year,inpt[inpt$Year==maxyr,3:ncol(inpt)]))
# }
# inp = do.call("rbind",inp.lst)
#
# write.csv(inp,file.path(project.datadirectory('bio.lobster'),'data','inputs',paste0('ccir_inputs',assessment.year,'.csv')))
#
# inp = read.csv(file.path(project.datadirectory('bio.lobster'),'data','inputs',paste0('ccir_inputs',assessment.year,'.csv')))
# inp33 = subset(inp,LFA==33)
# inp34 = inp33
# inp34$LFA = 34
# inp35 = inp33
# inp35$LFA = 35
# inp=rbind(inp,inp34,inp35)
# write.csv(inp,file.path(project.datadirectory('bio.lobster'),'data','inputs',paste0('ccir_inputs',assessment.year,'.csv')))
inp = read.csv(file.path(project.datadirectory('bio.lobster'),'data','inputs',paste0('ccir_inputs',assessment.year,'.csv')))
load(file.path(project.datadirectory('bio.lobster'),'data','inputs','ccir_groupings.rdata')) #object names Groupings
load(file.path(project.datadirectory('bio.lobster'),'data','inputs','ccir_seasons.rdata'))
lobster.db('ccir')
gridsbylfa=with(ccir_data,tapply(Grid,LFA,unique))
groups3435=list(list(lfa=34,G1=gridsbylfa$'34'),list(lfa=35,G1=gridsbylfa$'35'))
logs = lobster.db('process.logs')
require(bio.ccir)
require(rstan)
load_all(paste(git.repo,'bio.ccir',sep="/")) # for debugging
dat = ccir_compile_data(x = ccir_data,log.data = logs, area.defns = groups3435[1], size.defns = inp, season.defns = Seasons, sexs = 1.5) #sexs 1.5 means no sex defn
out.binomial = list()
attr(out.binomial,'model') <- 'binomial'
for(i in 1:length(dat)) {
ds = dat[[i]]
ds$method = 'binomial'
x = ccir_stan_run(dat = ds,save=F)
out.binomial[[i]] <- ccir_stan_summarize(x)
}
ouBin = ccir_collapse_summary(out.binomial)
attr(ouBin,'model') <- 'binomial'
#ouBin$Yr = ouBin$Yr +1
save(ouBin,file=file.path(project.datadirectory('bio.lobster'),'outputs','ccir','summary','compiledBinomialModels2732.rdata'))
#load(file=file.path(project.datadirectory('bio.lobster'),'outputs','ccir','summary','compiledBinomialModels2732.rdata'))
u = subset(ouBin, LFA == 27)
g = unique(u$Grid)
g = strsplit(g,"\\.")
o = aggregate(WEIGHT_KG~SYEAR,data=subset(logs,GRID_NUM %in% g[[1]]),FUN=sum)
names(o)[2] = g[[1]][1]
o2 = aggregate(WEIGHT_KG~SYEAR,data=subset(logs,GRID_NUM %in% g[[2]]),FUN=sum)
names(o2)[2] = g[[2]][1]
o = merge(o,o2)
names(o)[1] = 'Yr'
oo <- ccir_timeseries_exploitation_plots(ouBin,combined.LFA=T,landings=o)
u = subset(ouBin, LFA != 27)
kl = unique(u$Grid)
outs=list()
for(i in 1:length(kl)) {
u = subset(ouBin, Grid == kl[i])
outs[[i]] <- ccir_timeseries_exploitation_plots(u)
}
o = do.call(rbind,outs)
ooo = subset(o,select=c(Yr,ERfl,ERfm,ERfu,ERf75,LFA))
oo = rbind(oo,ooo)
oo$LFA[oo$LFA == "LFA 27 Combined"] = 27
oo$LFA[oo$LFA == "LFA 29"] = 29
oo$LFA[oo$LFA == "LFA 30"] = 30
oo$LFA[oo$LFA == "LFA 31A"] = "31A"
oo$LFA[oo$LFA == "LFA 31B"] = "31B"
oo$LFA[oo$LFA == "LFA 32"] = 32
save(oo,file=file.path(project.datadirectory('bio.lobster'),'outputs','ccir','summary','compiledExploitationCCIR2732.rdata'))
load(file=file.path(project.datadirectory('bio.lobster'),'outputs','ccir','summary','compiledExploitationCCIR2732.rdata'))
RR75 = aggregate(ERf75~LFA,data=oo[oo$Yr<2017,],FUN=max)
ouBin = subset(ouBin,Yr<2018&LFA==34,-2)
save(ouBin,file=file.path(project.datadirectory('bio.lobster'),'outputs','ccir','summary','compiledBinomialModels34.rdata'))
load(file=file.path(project.datadirectory('bio.lobster'),'outputs','ccir','summary','compiledBinomialModels34.rdata'))
oo <- ccir_timeseries_exploitation_plots(ouBin,Main="34")
save(oo,file=file.path(project.datadirectory('bio.lobster'),'outputs','ccir','summary','compiledExploitationCCIR34.rdata'))
load(file=file.path(project.datadirectory('bio.lobster'),'outputs','ccir','summary','compiledExploitationCCIR33.rdata'))
oo33=oo
load(file=file.path(project.datadirectory('bio.lobster'),'outputs','ccir','summary','compiledExploitationCCIR34.rdata'))
RR75 = aggregate(ERf75~LFA,data=oo[oo$Yr<2018,],FUN=max)$ERf75
# plot
x11(width=8,height=5)
ExploitationRatePlots(data = oo[,c("Yr","ERfm","ERfl","ERfu")],lfa = 34,fd=figdir,runM=F)
load(file=file.path(project.datadirectory('bio.lobster'),'outputs','ccir','summary','compiledBinomialModels33.rdata'))
with(subset(ouBin,LFA=='33W'),lines(Yr,ERfm,lty=2,col='blue'))
ExploitationRatePlots(data = oo[,c("Yr","ERfm","ERfl","ERfu")],lrp=RR75,lfa = 34,fd=figdir)
}
######################## sim Molt
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