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99_Indicators.R
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99_Indicators.R
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# Functions for extracting indices
slp3<-function(y0){
y=log(y0)
x1<-1:length(y0)
x1[is.na(y)]=NA
mux<-mean(x1,na.rm=T)
muy<-mean(y,na.rm=T)
SS<-sum((x1-mux)^2,na.rm=T)
(1/SS)*sum((x1-mux)*(y-muy),na.rm=T)
}
slp3_nolog<-function(y){
x1<-1:length(y)
x1[is.na(y)]=NA
mux<-mean(x1,na.rm=T)
muy<-mean(y,na.rm=T)
SS<-sum((x1-mux)^2,na.rm=T)
(1/SS)*sum((x1-mux)*(y-muy),na.rm=T)
}
smooth2<-function(xx,plot=F,enp.mult=0.2,plotname="",ret = "pred"){
tofill<-!is.na(xx)
xx[xx==0]<-1E3
#xx[tofill] = log(xx[tofill])
predout<-rep(NA,length(xx))
dat<-data.frame(x=1:length(xx),y=log(xx))
enp.target<-sum(tofill)*enp.mult
out<-loess(y~x,dat=dat,enp.target=enp.target)
#out<-loess(y~x,dat=dat,span=0.5)
predout[tofill]<-exp(predict(out))
if(plot){
plot(xx,type="p",xlab="x",ylab="y",main=plotname)
lines(predout,col="#ff000090",lwd=2)
}
if(ret =="pred")return(predout)
if(ret =="resid")return(log(xx/predout))
}
smooth3<-function(xx,plot=F,enp.mult=0.3,plotname=""){
tofill<-!is.na(xx)
predout<-rep(NA,length(xx))
dat<-data.frame(x=1:length(xx),y=xx)
enp.target<-sum(tofill)*enp.mult
out<-loess(y~x,dat=dat,enp.target=enp.target)
predout[tofill]<-predict(out)
if(plot){
plot(xx,type="p",xlab="x",ylab="y",main=plotname)
lines(predout,col="#ff000090",lwd=2)
}
predout
}
proc_dat<-function(MMSE,Iind=NA,sno = 1, fno=1, plotsmooth=F){
dat = MMSE@PPD[[sno]][[fno]][[1]]
nsim<-MMSE@nsim
nyears<-MMSE@nyears
proyears = MMSE@proyears
allyears = nyears+proyears
datlist<-list()
Iobs<-dat@Ind
Cobs<-dat@Cat
CAL = dat@CAL
mids = dat@CAL_mids
#prob<-seq(1,0.5,length.out=length(allyears))
if(length(Iind)==1){
Iyr<-sample((nyears+1):(allyears-2),nsim,replace=T)#,prob=prob)
Byr = Iyr - nyears
Iind<-cbind(1:nsim,Iyr)
}else{
Iyr = Iind[,2]
Byr = Iyr - nyears
}
Bt = MMSE@SB_SBMSY
I_cur<-I_mu<-I_rel<-I_s5<-I_s10<-I_s20<-
Isd1 <- Isd2 <- Isd3 <-
C_cur<- C_mu<-C_rel<-C_s5<-C_s10<- C_s20<-
Csd <-
ML_cur<-ML_mu<-ML_rel<-ML_s5<-ML_s10<-ML_s20<- ML_L50 <- ML_Linf <-
MV_cur<- MV_mu<-MV_rel<- MV_s5<-MV_s10<-MV_s20<-
FM_cur <- FM_mu <- FM_rel <- FM_s5 <- FM_s10 <-FM_s20 <- rep(NA,nsim)
matage = MMSE@multiHist[[sno]][[fno]]@AtAge$Maturity[,,1]
lenage = MMSE@multiHist[[sno]][[fno]]@AtAge$Length[,,1]
OM = MMSE@OM[[sno]][[fno]]
L50 = OM$L50
Linf = OM$Linf
M = OM$M
K = OM$K
M_K = M/K
maxa = -log(0.05)/M # age at 5% cumulative survival
L5 = MMSE@multiHist[[sno]][[fno]]@SampPars$Fleet$L5_y[,1]
LFS = MMSE@multiHist[[sno]][[fno]]@SampPars$Fleet$LFS_y[,1]
VML = MMSE@multiHist[[sno]][[fno]]@SampPars$Fleet$Vmaxlen_y[,1]
L5_L50 = L5/L50
LFS_L50 = LFS/L50
for(i in 1:nsim){
# Index
Is<-smooth2(Iobs[i,],plot=plotsmooth)
I_cur[i]<-Is[Iind[i,2]]
I_mu[i] = mean(Iobs[i,nyears:Iind[i,2]])
I_rel[i]<-I_cur[i] / mean(Is[1:Iind[i,2]])
I_s5[i]<-slp3(Iobs[i,Iind[i,2]-(5:1)])
I_s10[i]<-slp3(Iobs[i,Iind[i,2]-(10:1)])
I_s20[i]<-slp3(Iobs[i,Iind[i,2]-(20:1)])
ind = Iind[i,2] - (20:1)
Isd1[i] = sd(smooth2(Iobs[i,ind],ret = 'resid',enp.mult = 0.4,plot=plotsmooth))
Isd2[i] = sd(smooth2(Iobs[i,ind],ret = 'resid',enp.mult = 0.2,plot=plotsmooth))
Isd3[i] = sd(smooth2(Iobs[i,ind],ret = 'resid',enp.mult = 0.1,plot=plotsmooth))
# Catch
Cs = smooth2(Cobs[i,],plot=plotsmooth)
Csd[i] = sd(smooth2(Cobs[i,],plot=plotsmooth,ret='resid'))
C_cur[i]<-Cs[Iind[i,2]]
C_mu[i]<-mean(Cobs[i,nyears:Iind[i,2]])
C_rel[i] <- C_cur[i] / mean(Cs[1:Iind[i,2]],na.rm=T)
C_s5[i]<-slp3(Cobs[i,Iyr[i]-(5:1)])
C_s10[i]<-slp3(Cobs[i,Iyr[i]-(10:1)])
C_s20[i]<-slp3(Cobs[i,Iyr[i]-(20:1)])
# Length
CALi = CAL[i,,]
totlen = CALi*t(array(mids,dim(t(CALi))))
mulen = apply(totlen,1,sum)/apply(CALi,1,sum)
if(all(is.na(mulen))){
Ls = rep(NA,length(mulen))
}else{
Ls = smooth2(mulen,plot=plotsmooth, enp.mult = 0.1)
}
ML_cur[i]=Ls[Iind[i,2]]
ML_mu[i]=mean(mulen[nyears:Iind[i,2]])
ML_rel[i] <- ML_cur[i] / mean(Ls[1:Iind[i,2]],na.rm=T)
ML_s5[i]<-slp3(mulen[Iyr[i]-(5:1)])
ML_s10[i]<-slp3(mulen[Iyr[i]-(10:1)])
ML_s20[i]<-slp3(mulen[Iyr[i]-(20:1)])
ML_Linf[i]= ML_cur[i]/Linf[i]
ML_L50[i]= ML_cur[i]/L50[i]
# standard deviation in length
CVlen = sapply(1:dim(CALi)[1],function(X,CALi,mids){
sd(rep(mids,CALi[X,]))/mean(rep(mids,CALi[X,]))
},CALi=CALi,mids=mids)
if(all(is.na(CVlen))){
CVs = rep(NA,length(CVlen))
}else{
CVs = smooth2(CVlen, plot=plotsmooth,enp.mult = 0.1)
}
MV_cur[i]=CVs[Iind[i,2]]
MV_mu[i] = mean(CVlen[nyears:Iind[i,2]],na.rm=T)
MV_rel[i] <- MV_cur[i] / mean(CVs[1:Iind[i,2]])
MV_s5[i]<-slp3(CVlen[Iyr[i]-(5:1)])
MV_s10[i]<-slp3(CVlen[Iyr[i]-(10:1)])
MV_s20[i]<-slp3(CVlen[Iyr[i]-(20:1)])
L50i = L50[i]
Fmat = sapply(1:dim(CALi)[1],function(X,CALi,mids,L50i){
indivs = rep(mids,CALi[X,])
mean(indivs > L50i,na.rm=T)
},CALi=CALi,mids=mids,L50i=L50i)
if(all(is.na(Fmat))){
FMs = rep(NA,length(Fmat))
}else{
FMs = smooth2(Fmat, plot=plotsmooth, enp.mult=0.1)
}
FM_cur[i]= FMs[Iind[i,2]]
FM_mu[i] = mean(FMs[nyears:Iind[i,2]])
FM_rel[i] <- FM_cur[i] / mean(FMs[1:Iind[i,2]])
FM_s5[i]<-slp3(Fmat[Iyr[i]-(5:1)])
FM_s10[i]<-slp3(Fmat[Iyr[i]-(10:1)])
FM_s20[i]<-slp3(Fmat[Iyr[i]-(20:1)])
}
Bind<-cbind(1:nsim,rep(sno,nsim),rep(1,nsim),Byr)
Brel = MMSE@SB_SBMSY[Bind]
data.frame(Brel, I_rel, I_s5, I_s10, I_s20,
Isd1, Isd2, Isd3,
C_rel, C_s5, C_s10, C_s20, Csd,
ML_cur, ML_rel, ML_s5, ML_s10, ML_s20, ML_L50, ML_Linf,
MV_cur, MV_rel, MV_s5, MV_s10, MV_s20,
FM_cur, FM_rel, FM_s5, FM_s10, FM_s20,
M_K, maxa, L5_L50, LFS_L50, VML)
}
cat_ratios = function(MMSE, Iind, fno=1, plotsmooth=F, refyrs = 20){
ns = MMSE@nstocks
nsim = MMSE@nsim
allyears = MMSE@proyears+MMSE@nyears
nyears = MMSE@nyears
CRtab = NULL
for(i in 1:nsim){
catsmth = catsmth2 = array(NA,c(ns,allyears-1))
for(sno in 1:ns){
catsmth[sno,] = smooth2(MMSE@PPD[[sno]][[fno]][[1]]@Cat[i,],plot=plotsmooth)
catsmth2[sno,] = smooth2(MMSE@PPD[[sno]][[fno]][[1]]@Cat[i,],enp.mult = 0.075,ret='resid',plot=plotsmooth) # log residuals after broad detrending
}
ncomp = ((ns-1)*ns)/2
CR_rel = CR_s5 = CR_s10 = CR_mu = CR = namy = rep(NA,ncomp)
sampyr = Iind[i,2]
j = 0
for(ss in 1:(ns-1)){
for(s2 in (ss+1):ns){
j=j+1
namy[j] = paste(ss,s2,sep="_")
rat = catsmth[ss,]/catsmth[s2,]
CR[j] = rat[sampyr]
invalidrat = rat<(0.01*mean(rat))&((1:length(rat))<refyrs)
rat2 = rat; rat2[invalidrat] = NA
CR_mu[j] = mean(rat2[1:refyrs],na.rm=T)
CR_rel[j] = rat2[sampyr] / mean(rat2[1:refyrs],na.rm=T)
CR_s5[j] = slp3(rat2[sampyr-(5:1)])
CR_s10[j] = slp3(rat2[sampyr-(10:1)])
}
}
CRvec = c(CR,CR_mu,CR_rel,CR_s5,CR_s10)
names(CRvec) = paste(rep(c("CR","CR_mu","CR_rel","CR_s5","CR_s10"),each=ncomp),
rep(namy,5),sep="_")
CC20 = CC40 = CC60 = rep(NA, ncomp)
j = 0
ind20 = Iind[i,2] - (20:1)
ind40 = Iind[i,2] - (40:21)
ind60 = Iind[i,2] - (60:41)
for(ss in 1:(ns-1)){
for(s2 in (ss+1):ns){
j=j+1
CC20[j] = cor(catsmth2[ss,ind20],catsmth2[s2,ind20])
CC40[j] = cor(catsmth2[ss,ind40],catsmth2[s2,ind40])
CC60[j] = cor(catsmth2[ss,ind60],catsmth2[s2,ind60])
}
}
CCvec = c(CC20,CC40,CC60)
names(CCvec) = paste(rep(c("CC20","CC40","CC60"),each=ncomp),
rep(namy,3),sep="_")
CRtab = rbind(CRtab, c(CRvec,CCvec))
#cat(".")
} # simulation
#cat("\n")
CRtab
}
filt=function(x){
x[x%in%c("Inf","-Inf")] = NA
x
}
corplot = function(out,quanty=0.5,ptcex=0.8,maxn=20, lasinv=F,lab=""){
nams = names(out)
ndat = dim(out)[2]
nobs = dim(out[1])
ni <- min(ndat,maxn)
ncol = 200
cols <- rainbow(ncol,start=0.1,end=0.3,alpha=0.7)
cols <- c(cols,rep(cols[ncol],1000-ncol))
Brel = out$Brel
Bcol = cols[ceiling(Brel*100)]; plot(Brel[order(Brel)],col=Bcol[order(Brel)],pch=19)
par(mfrow=c(ni-1,ni-1),mai=rep(0,4),omi=c(0.55,0.75,0.05,0.05))
cutoff= c(quanty/100,(100-quanty)/100)
# labys = c(letters,paste0(letters,letters),paste0(letters,letters,letters),paste0(letters,letters,letters,letters))
labys = paste0(rep(letters,9),rep(1:9,each=length(letters)))
labys = paste0("(",labys,")")
k = 0
for(i in 2:ni){ # i<-i+1
for(j in 1:(ni-1)){
if(j==i|j>i){
plot(1,1,col='white',axes=F)
}else{
xs = filt(out[,j])
ys = filt(out[,i])
xlim<-range(quantile(xs,cutoff,na.rm=T))
ylim<-range(quantile(ys,cutoff,na.rm=T))
plot(xs,ys,pch=19,xlim=xlim,ylim=ylim,cex=ptcex,col=Bcol,axes=F)
k=k+1; mtext(labys[k],3,adj=0.02,line=-1,cex=0.6)
axis(1,c(-1000,1000))
axis(2,c(-1000,1000))
axis(3,c(-1000,1000))
axis(4,c(-1000,1000))
#points(obs[j],obs[i],pch=19,cex=1.2,col=cols[2])
if(nams[j]=="Brel")abline(v=c(0.5,1),lty=c(2,1),col="#99999999")
}
if(i==2&j==(ni-1)){
legend('center',legend=lab,text.col="blue",bty='n',text.font=2,cex=1.2)
}
if(!lasinv){
if(j==1)mtext(nams[i],2,line=2,cex=0.6,las=2)
if(i==ni)mtext(nams[j],1,line=1,cex=0.6,las=2)
}else{
if(j==1)mtext(nams[i],2,line=2,cex=0.6,srt=90)
if(i==ni)mtext(nams[j],1,line=1,cex=0.6,las=1)
}
#if(j==1)mtext(i,2,line=2,cex=0.5,las=2)
#if(i==nplotted)mtext(j,1,line=1,cex=0.5,las=2)
}
}
#}
}
simulate_tc_lm = function(object, Brel){
ftd <- predict(object,newdata = data.frame(x=log(Brel))) # == napredict(*, object$fitted)
vars <- deviance(object)/ df.residual(object)
ftd + rnorm(length(Brel), sd = sqrt(vars))
}
sim_spatial_dat = function(MMSE,Iind, spat_mods){
Iyr = Iind[,2]
nyears= MMSE@nyears
ns = MMSE@nstocks
nsim = MMSE@nsim
ny = 15
spatstat = array(NA,c(nsim,ns))
for(sno in 1:ns){
histSSB = apply(MMSE@multiHist[[sno]][[1]]@TSdata$SBiomass,1:2,sum)
futureSSB = MMSE@SSB[,sno,1,]
SSB = cbind(histSSB,futureSSB)
SSBMSY = (futureSSB / MMSE@SB_SBMSY[,sno,1,])[1,]
SBrel = SSB / SSBMSY
object = spat_mods[[sno]]
for(i in 1:nsim){
Bind<-cbind(rep(i,ny),Iyr[i]-(1:ny))
Brel = SBrel[Bind]
obs = simulate_tc_lm(object, Brel)
spatstat[i,sno] = smooth3(obs,plot=F)[length(obs)]
}
}
stat= as.data.frame(spatstat)
names(stat) = paste0("spat_",1:sno)
stat
}
get_sim_data = function(ff,filelocs, spat_mods){
set.seed(ff)
MMSE = readRDS(filelocs[ff])
nsim<-MMSE@nsim
nyears<-MMSE@nyears
proyears = MMSE@proyears
allyears = nyears+proyears
Iyr<-sample((nyears+1):(allyears-2),nsim,replace=T)
Byr = Iyr - nyears
Iind<-cbind(1:nsim,Iyr)
ns=MMSE@nstocks
outs = list()
for(sno in 1:ns)outs[[sno]] = proc_dat(MMSE,Iind=Iind,sno=sno)
outs[[ns+1]] = cat_ratios(MMSE, Iind=Iind)
outs[[ns+2]] = sim_spatial_dat(MMSE,Iind=Iind,spat_mods)
for(i in 1:(ns+2)){
out = outs[[i]]
if(i <=ns) names(out) = paste0(names(out),"_",i)
if(i==1) one_tab=out
if(i>1) one_tab=cbind(one_tab,out)
}
row.names(one_tab) = paste0("sim_",1:nrow(one_tab))
cat(".")
one_tab
}
process_sim_data = function(MSEdir, spat_mods, parallel=T, cores = NA){
files = list.files(MSEdir)
keep = grepl("MMSE",files)
filelocs = list.files(MSEdir,full.names=T)[keep]
nfile = length(filelocs)
if(parallel){
library(snowfall)
library(parallel)
if(is.na(cores))cores = detectCores()/2
sfInit(parallel=T,cpus = cores)
sfExport("proc_dat"); sfExport("smooth2"); sfExport('slp3'); sfExport('cat_ratios');
sfExport("smooth3"); sfExport("sim_spatial_dat"); sfExport("simulate_tc_lm")
allout = sfLapply(1:nfile,get_sim_data,filelocs=filelocs, spat_mods=spat_mods)
}else{
allout = lapply(1:2, get_sim_data, filelocs=filelocs, spat_mods=spat_mods)
allout=list()
for(i in 1:nfile)allout[[i]]=get_sim_data(i,filelocs=filelocs, spat_mods=spat_mods)
}
cat("\n")
allout
}