inst/OldPotentiallyDepricatedCode/9.CCIR.r
In LobsterScience/bio.lobster: Lobster Stock Assessment Tools for DFO Maritimes
#9. CCIR.r
require(bio.lobster)
load_all('~/git/bio.ccir')
require(bio.utilities)
require(car)
require(rstan)
la()
redo.data = FALSE
if(redo.data) {
lobster.db('fsrs.redo') #this requires ODBC connection
lobster.db('ccir.redo') #this does not
}
#Groupings for seasons, and regions -- Hard coded---Need to be manually updated
inp = read.csv(file.path(project.datadirectory('bio.lobster'),'data','inputs','ccir_inputs.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')
logs = lobster.db('process.logs')
logs$LFA = ifelse(logs$LFA == '31A','31a',logs$LFA)
logs$LFA = ifelse(logs$LFA == '31B','31b',logs$LFA)
dat = ccir_compile_data(x = ccir_data,log.data = logs, area.defns = Groupings, size.defns = inp, season.defns = Seasons, sexs = 1.5) #sexs 1.5 means no sex defn
#dat = ccir_compile_data(x = ccir_data,area.defns = Groupings, size.defns = inp, season.defns = Seasons, sexs = c(1,2) #Not for stock assessment, but for Simulation models
redo.models =T
if(redo.models) {
#model options
out.normal = list() #this is the Claytor and Allard Model
attr(out.normal,'model') <- 'normal'
out.binomial = list()
attr(out.binomial,'model') <- 'binomial'
out.binomial.fishery.land = list()
attr(out.binomial.fishery.land,'model') <- 'binomial.fishery.land'
out.beta = list()
attr(out.beta,'model') <- 'beta'
out.logit.binomial = list()
attr(out.logit.binomial,'model') <- 'logit.binomial'
out.logit.binomial.cov = list()
attr(out.logit.binomial.cov,'model') <- 'logit.binomial.cov'
mm = c('binomial'),'binomial.fishery.land')
for(i in 1:length(dat)) {
print(i)
ds = dat[[i]]
if(any(mm == 'normal')){
ds$method = 'normal'
x = ccir_stan_run(dat = ds)
ccir_stan_plots(x,type='predicted')
ccir_stan_plots(x,type='exploitation')
ccir_stan_plots(x,type='traceplot')
ccir_stan_plots(x,type='prior.posterior')
# if(length(ds$p) == length(ds$Temp))ccir_stan_plots(x,type='Temp.by.Expl')1
out.normal[[i]] <- ccir_stan_summarize(x)
}
if(any(mm == 'logit.binomial')){
ds$method = 'logit.binomial'
x = ccir_stan_run(dat = ds)
ccir_stan_plots(x,type='predicted')
ccir_stan_plots(x,type='exploitation')
ccir_stan_plots(x,type='traceplot')
ccir_stan_plots(x,type='prior.posterior')
# if(length(ds$p) == length(ds$Temp))ccir_stan_plots(x,type='Temp.by.Expl')
out.logit.binomial[[i]] <- ccir_stan_summarize(x)
}
if(any(mm == 'binomial')){
ds$method = 'binomial'
x = ccir_stan_run(dat = ds,save=F)
ccir_stan_plots(x,type='predicted')
ccir_stan_plots(x,type='exploitation')
# ccir_stan_plots(x,type='traceplot')
# ccir_stan_plots(x,type='prior.posterior')
# if(length(ds$p) == length(ds$Temp))ccir_stan_plots(x,type='Temp.by.Expl')
out.binomial[[i]] <- ccir_stan_summarize(x)
}
if(any(mm == 'binomial.fishery.land')){
ds$method = 'binomial.fishery.land'
if(ds$Yr>2003) {
if(!all(is.na(ds$land))){
x = ccir_stan_run(dat = ds)
ccir_stan_plots(x,type='predicted')
ccir_stan_plots(x,type='exploitation')
# ccir_stan_plots(x,type='traceplot')
# ccir_stan_plots(x,type='prior.posterior')
# if(length(ds$p) == length(ds$Temp))ccir_stan_plots(x,type='Temp.by.Expl')
out.binomial.fishery.land[[i]] <- ccir_stan_summarize(x)
}
}
}
if(any(mm == 'logit.binomial.cov')){
ds$method = 'logit.binomial.cov'
x = ccir_stan_run(dat = ds)
ccir_stan_plots(x,type='predicted')
ccir_stan_plots(x,type='exploitation')
ccir_stan_plots(x,type='traceplot')
ccir_stan_plots(x,type='prior.posterior')
# if(length(ds$p) == length(ds$Temp))ccir_stan_plots(x,type='Temp.by.Expl')
out.logit.binomial.cov[[i]] <- ccir_stan_summarize(x)
if(any(mm == 'beta')){
ds$method = 'beta'
x = ccir_stan_run(dat = ds)
ccir_stan_plots(x,type='predicted')
ccir_stan_plots(x,type='exploitation')
ccir_stan_plots(x,type='traceplot')
ccir_stan_plots(x,type='prior.posterior')
out.beta[[i]] <- ccir_stan_summarize(x)
}
}
}
#ouBet = ccir_collapse_summary(out.beta)
#ouLoBin = ccir_collapse_summary(out.logit.binomial)
ouBin = ccir_collapse_summary(out.binomial)
attr(ouBin,'model') <- 'binomial'
ouBinFish = ccir_collapse_summary(out.binomial.fishery.land)
attr(ouBinFish,'model') <- 'binomial.fishery.land'
#save(ouBet,file=file.path(project.datadirectory('bio.lobster'),'outputs','ccir','summary','compiledBetaModels.rdata'))
save(ouBin,file=file.path(project.datadirectory('bio.lobster'),'outputs','ccir','summary','compiledBinomialModels.rdata'))
save(ouBinFish,file=file.path(project.datadirectory('bio.lobster'),'outputs','ccir','summary','compiledBinomialFisheryModels.rdata'))
#save(ouLoBin,file=file.path(project.datadirectory('bio.lobster'),'outputs','ccir','summary','compiledlogitBinomialModels.rdata'))
}
#load each ccir stan summarize for binomials
load(file=file.path(project.datadirectory('bio.lobster'),'outputs','ccir','summary','compiledBinomialModels.rdata'))
load(file=file.path(project.datadirectory('bio.lobster'),'outputs','ccir','summary','compiledBinomialFisheryModels.rdata'))
outs = list()
kl = unique(ouBin$Grid)
for(i in 1:length(kl)) {
u = subset(ouBin, Grid == kl[i])
outs[[i]] <- ccir_timeseries_exploitation_plots(u)
}
# iu = c(grep('LFA 27',outs),grep('LFA 33',outs))
# outs = outs[-c(iu)]
o = do.call(rbind,outs)
ooo = subset(o,select=c(Yr,ERfl,ERfm,ERfu,LFA))
#remove lfa 33 and 27 as treated as one
#Split LFAs combined for one exploitation rate
outs = list()
SLfa = c(27,33)
lan = lobster.db('process.logs')
for(i in 1:length(SLfa)) {
u = subset(ouBin, LFA == SLfa[i])
g = unique(u$Grid)
g = strsplit(g,"\\.")
o = aggregate(WEIGHT_KG~SYEAR,data=subset(lan,GRID_NUM %in% g[[1]]),FUN=sum)
names(o)[2] = g[[1]][1]
o2 = aggregate(WEIGHT_KG~SYEAR,data=subset(lan,GRID_NUM %in% g[[2]]),FUN=sum)
names(o2)[2] = g[[2]][1]
o = merge(o,o2)
names(o)[1] = 'Yr'
outs[[i]] <- ccir_timeseries_exploitation_plots(u,combined.LFA=T,landings=o)
}
oo = do.call(rbind,outs)
oi = rbind(ooo,oo)
###Model comparison time series exploitation plots
kl = unique(ouBinFish$Grid)
for(i in 1:length(kl)) {
u = subset(ouBin, Grid == kl[i])
w = subset(ouBinFish, Grid == kl[i])
ccir_timeseries_exploitation_plots_model_comparison(u,w)
}
##Exploitation and Changing Landings
lSe = lobster.db('seasonal.landings')
lSe$YR = as.numeric(substr(lSe$SYEAR,6,9))
lA = lobster.db('annual.landings')
#from above
o = rbind(ooo,oo)
o$LFA = ifelse(o$LFA == 'LFA 27 Combined','LFA 27',o$LFA)
o$LFA = ifelse(o$LFA == 'LFA 33 Combined','LFA 33',o$LFA)
outs = do.call(rbind,outs)
x = outs[[1]]
y = subset(lA,select=c(YR,LFA27))
y$D1 = c(diff(y[,2]),0)
a = merge(x,y,by.x='Yr',by.y = 'YR')
with(a,plot(ERfm,LFA27))
x = outs[[2]]
y = subset(lSe,select=c(SYEAR,LFA33))
y$YR = as.numeric(substr(lSe$SYEAR,6,9))
y$D1 = c(diff(y[,2]),0)
a = merge(x,y,by.x='Yr',by.y = 'YR')
with(a,plot(ERfm,LFA33))
######NOT USED for FRAMEWORK FROM HERE DOWN######
#partial exploitation rates and logbook effort
logs = lobster.db('process.logs')
gg = dir(file.path(project.datadirectory('bio.lobster'),'outputs','ccir','summary'),full.names=T)
gg = gg[grep('Sex.1.5',gg)] #sexes combined
outs = list()
for(i in 1:length(gg)){
print(i)
load(gg[i])
outs[[i]] = ccir_groupings_landings_effort(x=logs,ccir_model_summary=out)
}
outs = rbindFill(outs)
outs = toNums(outs,4:21)
outs$CredInt = outs[,'ERu.97.5%'] - outs[,'ERl.2.5%']
#Partial exploitation rates
## High correlation between recruit:exp ratio and temp-- to ensure that this change is due to fishing and not temp, compare the start of year ratios and temp within LFAs
#x is the list of out from ccir_stan_summarize
LFA = unlist(t(sapply(dat,"[",'LFA')))
Te = sapply(t(sapply(dat,'[','Temp')),function(x) mean( x[1:5]))
Sex = unlist(t(sapply(dat,"[",'Sex')))
N = sapply(t(sapply(dat,'[','N')),function(x) sum( x[1:5]))
E = sapply(t(sapply(dat,'[','E')),function(x) sum( x[1:5]))
Gr = unlist(sapply(sapply(dat,"[",'Grid'),function(x) paste(x,collapse=".")))
da = data.frame(LFA=LFA, Grid = Gr,Sex=Sex,Temp = Te, N=N, E= E,R = N-E, p = E/N)
da$id = paste(da$LFA,da$Grid,da$Sex,sep="-")
ij = unique(da$id)
for(i in 1:length(j)){
hy = subset(da,id==ij[i])
x11()
plot(hy$Te,hy$p,type='p',main = ij[i])
legend('topleft',paste('correlation =', round(cor.test(hy$Temp,hy$p)[[4]],2)),bty='n',cex=0.8)
}
##Tester for Runs
require(rstan))
load('~/git/bio.ccir/inst/trial.data.rdata')
Total = apply(SublegalLegal,1,sum)
CumLegal <- cumsum(SublegalLegal[,2])
CumLegal <- c(0,CumLegal[1:length(CumLegal)-1])
p = SublegalLegal[,2]/Total
n = length(p)
i = which(p==1)
p[i] = 0.99
i = which(p==0)
p[i] = 0.01
dts = as.Date(as.numeric(rownames(SublegalLegal)),origin = "1970-01-01")
n = length(p)
newp = seq(0.01,0.99,by=0.01)
np = length(newp)
dat = list(n = n, Cuml=CumLegal/CumLegal[n], p=as.numeric(p), newp = newp , np=np ,dates = dts, Nrec = sum(SublegalLegal[,1]),Nexp = sum(SublegalLegal[,2]))
x = ccir_stan_run(dat = dat, method = 'beta')
#Can I recapture the parameters
a=3
b=-2.67
d=1
x=seq(0.01,0.99,0.01)
phi=9.12
a = simulateCCIR(a,b,d,phi,x)
dat = list(n = nrow(a), Cuml=a[,1], p=a[,2], newp = a[,1] , np=nrow(a) ,dates = 1:nrow(a), Nrec = 11,Nexp = 11)
x = ccir_stan_run(dat = dat, method = 'beta')
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#9. CCIR.r
require(bio.lobster)
load_all('~/git/bio.ccir')
require(bio.utilities)
require(car)
require(rstan)
la()
redo.data = FALSE
if(redo.data) {
lobster.db('fsrs.redo') #this requires ODBC connection
lobster.db('ccir.redo') #this does not
}
#Groupings for seasons, and regions -- Hard coded---Need to be manually updated
inp = read.csv(file.path(project.datadirectory('bio.lobster'),'data','inputs','ccir_inputs.csv'))
load(file.path(project.datadirectory('bio.lobster'),'data','inputs','ccir_groupings.rdata')) #object names Groupings
load(file.path(project.datadirectory('bio.lobster'),'data','inputs','ccir_seasons.rdata'))
lobster.db('ccir')
logs = lobster.db('process.logs')
logs$LFA = ifelse(logs$LFA == '31A','31a',logs$LFA)
logs$LFA = ifelse(logs$LFA == '31B','31b',logs$LFA)
dat = ccir_compile_data(x = ccir_data,log.data = logs, area.defns = Groupings, size.defns = inp, season.defns = Seasons, sexs = 1.5) #sexs 1.5 means no sex defn
#dat = ccir_compile_data(x = ccir_data,area.defns = Groupings, size.defns = inp, season.defns = Seasons, sexs = c(1,2) #Not for stock assessment, but for Simulation models
redo.models =T
if(redo.models) {
#model options
out.normal = list() #this is the Claytor and Allard Model
attr(out.normal,'model') <- 'normal'
out.binomial = list()
attr(out.binomial,'model') <- 'binomial'
out.binomial.fishery.land = list()
attr(out.binomial.fishery.land,'model') <- 'binomial.fishery.land'
out.beta = list()
attr(out.beta,'model') <- 'beta'
out.logit.binomial = list()
attr(out.logit.binomial,'model') <- 'logit.binomial'
out.logit.binomial.cov = list()
attr(out.logit.binomial.cov,'model') <- 'logit.binomial.cov'
mm = c('binomial'),'binomial.fishery.land')
for(i in 1:length(dat)) {
print(i)
ds = dat[[i]]
if(any(mm == 'normal')){
ds$method = 'normal'
x = ccir_stan_run(dat = ds)
ccir_stan_plots(x,type='predicted')
ccir_stan_plots(x,type='exploitation')
ccir_stan_plots(x,type='traceplot')
ccir_stan_plots(x,type='prior.posterior')
# if(length(ds$p) == length(ds$Temp))ccir_stan_plots(x,type='Temp.by.Expl')1
out.normal[[i]] <- ccir_stan_summarize(x)
}
if(any(mm == 'logit.binomial')){
ds$method = 'logit.binomial'
x = ccir_stan_run(dat = ds)
ccir_stan_plots(x,type='predicted')
ccir_stan_plots(x,type='exploitation')
ccir_stan_plots(x,type='traceplot')
ccir_stan_plots(x,type='prior.posterior')
# if(length(ds$p) == length(ds$Temp))ccir_stan_plots(x,type='Temp.by.Expl')
out.logit.binomial[[i]] <- ccir_stan_summarize(x)
}
if(any(mm == 'binomial')){
ds$method = 'binomial'
x = ccir_stan_run(dat = ds,save=F)
ccir_stan_plots(x,type='predicted')
ccir_stan_plots(x,type='exploitation')
# ccir_stan_plots(x,type='traceplot')
# ccir_stan_plots(x,type='prior.posterior')
# if(length(ds$p) == length(ds$Temp))ccir_stan_plots(x,type='Temp.by.Expl')
out.binomial[[i]] <- ccir_stan_summarize(x)
}
if(any(mm == 'binomial.fishery.land')){
ds$method = 'binomial.fishery.land'
if(ds$Yr>2003) {
if(!all(is.na(ds$land))){
x = ccir_stan_run(dat = ds)
ccir_stan_plots(x,type='predicted')
ccir_stan_plots(x,type='exploitation')
# ccir_stan_plots(x,type='traceplot')
# ccir_stan_plots(x,type='prior.posterior')
# if(length(ds$p) == length(ds$Temp))ccir_stan_plots(x,type='Temp.by.Expl')
out.binomial.fishery.land[[i]] <- ccir_stan_summarize(x)
}
}
}
if(any(mm == 'logit.binomial.cov')){
ds$method = 'logit.binomial.cov'
x = ccir_stan_run(dat = ds)
ccir_stan_plots(x,type='predicted')
ccir_stan_plots(x,type='exploitation')
ccir_stan_plots(x,type='traceplot')
ccir_stan_plots(x,type='prior.posterior')
# if(length(ds$p) == length(ds$Temp))ccir_stan_plots(x,type='Temp.by.Expl')
out.logit.binomial.cov[[i]] <- ccir_stan_summarize(x)
if(any(mm == 'beta')){
ds$method = 'beta'
x = ccir_stan_run(dat = ds)
ccir_stan_plots(x,type='predicted')
ccir_stan_plots(x,type='exploitation')
ccir_stan_plots(x,type='traceplot')
ccir_stan_plots(x,type='prior.posterior')
out.beta[[i]] <- ccir_stan_summarize(x)
}
}
}
#ouBet = ccir_collapse_summary(out.beta)
#ouLoBin = ccir_collapse_summary(out.logit.binomial)
ouBin = ccir_collapse_summary(out.binomial)
attr(ouBin,'model') <- 'binomial'
ouBinFish = ccir_collapse_summary(out.binomial.fishery.land)
attr(ouBinFish,'model') <- 'binomial.fishery.land'
#save(ouBet,file=file.path(project.datadirectory('bio.lobster'),'outputs','ccir','summary','compiledBetaModels.rdata'))
save(ouBin,file=file.path(project.datadirectory('bio.lobster'),'outputs','ccir','summary','compiledBinomialModels.rdata'))
save(ouBinFish,file=file.path(project.datadirectory('bio.lobster'),'outputs','ccir','summary','compiledBinomialFisheryModels.rdata'))
#save(ouLoBin,file=file.path(project.datadirectory('bio.lobster'),'outputs','ccir','summary','compiledlogitBinomialModels.rdata'))
}
#load each ccir stan summarize for binomials
load(file=file.path(project.datadirectory('bio.lobster'),'outputs','ccir','summary','compiledBinomialModels.rdata'))
load(file=file.path(project.datadirectory('bio.lobster'),'outputs','ccir','summary','compiledBinomialFisheryModels.rdata'))
outs = list()
kl = unique(ouBin$Grid)
for(i in 1:length(kl)) {
u = subset(ouBin, Grid == kl[i])
outs[[i]] <- ccir_timeseries_exploitation_plots(u)
}
# iu = c(grep('LFA 27',outs),grep('LFA 33',outs))
# outs = outs[-c(iu)]
o = do.call(rbind,outs)
ooo = subset(o,select=c(Yr,ERfl,ERfm,ERfu,LFA))
#remove lfa 33 and 27 as treated as one
#Split LFAs combined for one exploitation rate
outs = list()
SLfa = c(27,33)
lan = lobster.db('process.logs')
for(i in 1:length(SLfa)) {
u = subset(ouBin, LFA == SLfa[i])
g = unique(u$Grid)
g = strsplit(g,"\\.")
o = aggregate(WEIGHT_KG~SYEAR,data=subset(lan,GRID_NUM %in% g[[1]]),FUN=sum)
names(o)[2] = g[[1]][1]
o2 = aggregate(WEIGHT_KG~SYEAR,data=subset(lan,GRID_NUM %in% g[[2]]),FUN=sum)
names(o2)[2] = g[[2]][1]
o = merge(o,o2)
names(o)[1] = 'Yr'
outs[[i]] <- ccir_timeseries_exploitation_plots(u,combined.LFA=T,landings=o)
}
oo = do.call(rbind,outs)
oi = rbind(ooo,oo)
###Model comparison time series exploitation plots
kl = unique(ouBinFish$Grid)
for(i in 1:length(kl)) {
u = subset(ouBin, Grid == kl[i])
w = subset(ouBinFish, Grid == kl[i])
ccir_timeseries_exploitation_plots_model_comparison(u,w)
}
##Exploitation and Changing Landings
lSe = lobster.db('seasonal.landings')
lSe$YR = as.numeric(substr(lSe$SYEAR,6,9))
lA = lobster.db('annual.landings')
#from above
o = rbind(ooo,oo)
o$LFA = ifelse(o$LFA == 'LFA 27 Combined','LFA 27',o$LFA)
o$LFA = ifelse(o$LFA == 'LFA 33 Combined','LFA 33',o$LFA)
outs = do.call(rbind,outs)
x = outs[[1]]
y = subset(lA,select=c(YR,LFA27))
y$D1 = c(diff(y[,2]),0)
a = merge(x,y,by.x='Yr',by.y = 'YR')
with(a,plot(ERfm,LFA27))
x = outs[[2]]
y = subset(lSe,select=c(SYEAR,LFA33))
y$YR = as.numeric(substr(lSe$SYEAR,6,9))
y$D1 = c(diff(y[,2]),0)
a = merge(x,y,by.x='Yr',by.y = 'YR')
with(a,plot(ERfm,LFA33))
######NOT USED for FRAMEWORK FROM HERE DOWN######
#partial exploitation rates and logbook effort
logs = lobster.db('process.logs')
gg = dir(file.path(project.datadirectory('bio.lobster'),'outputs','ccir','summary'),full.names=T)
gg = gg[grep('Sex.1.5',gg)] #sexes combined
outs = list()
for(i in 1:length(gg)){
print(i)
load(gg[i])
outs[[i]] = ccir_groupings_landings_effort(x=logs,ccir_model_summary=out)
}
outs = rbindFill(outs)
outs = toNums(outs,4:21)
outs$CredInt = outs[,'ERu.97.5%'] - outs[,'ERl.2.5%']
#Partial exploitation rates
## High correlation between recruit:exp ratio and temp-- to ensure that this change is due to fishing and not temp, compare the start of year ratios and temp within LFAs
#x is the list of out from ccir_stan_summarize
LFA = unlist(t(sapply(dat,"[",'LFA')))
Te = sapply(t(sapply(dat,'[','Temp')),function(x) mean( x[1:5]))
Sex = unlist(t(sapply(dat,"[",'Sex')))
N = sapply(t(sapply(dat,'[','N')),function(x) sum( x[1:5]))
E = sapply(t(sapply(dat,'[','E')),function(x) sum( x[1:5]))
Gr = unlist(sapply(sapply(dat,"[",'Grid'),function(x) paste(x,collapse=".")))
da = data.frame(LFA=LFA, Grid = Gr,Sex=Sex,Temp = Te, N=N, E= E,R = N-E, p = E/N)
da$id = paste(da$LFA,da$Grid,da$Sex,sep="-")
ij = unique(da$id)
for(i in 1:length(j)){
hy = subset(da,id==ij[i])
x11()
plot(hy$Te,hy$p,type='p',main = ij[i])
legend('topleft',paste('correlation =', round(cor.test(hy$Temp,hy$p)[[4]],2)),bty='n',cex=0.8)
}
##Tester for Runs
require(rstan))
load('~/git/bio.ccir/inst/trial.data.rdata')
Total = apply(SublegalLegal,1,sum)
CumLegal <- cumsum(SublegalLegal[,2])
CumLegal <- c(0,CumLegal[1:length(CumLegal)-1])
p = SublegalLegal[,2]/Total
n = length(p)
i = which(p==1)
p[i] = 0.99
i = which(p==0)
p[i] = 0.01
dts = as.Date(as.numeric(rownames(SublegalLegal)),origin = "1970-01-01")
n = length(p)
newp = seq(0.01,0.99,by=0.01)
np = length(newp)
dat = list(n = n, Cuml=CumLegal/CumLegal[n], p=as.numeric(p), newp = newp , np=np ,dates = dts, Nrec = sum(SublegalLegal[,1]),Nexp = sum(SublegalLegal[,2]))
x = ccir_stan_run(dat = dat, method = 'beta')
#Can I recapture the parameters
a=3
b=-2.67
d=1
x=seq(0.01,0.99,0.01)
phi=9.12
a = simulateCCIR(a,b,d,phi,x)
dat = list(n = nrow(a), Cuml=a[,1], p=a[,2], newp = a[,1] , np=nrow(a) ,dates = 1:nrow(a), Nrec = 11,Nexp = 11)
x = ccir_stan_run(dat = dat, method = 'beta')
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