inst/IP/bycatchCSAS/3a.ComparisonOfCPUESamplesToLogs.r
In LobsterScience/bio.lobster: Lobster Stock Assessment Tools for DFO Maritimes
require(bio.lobster)
require(bio.utilities)
require(RODBC)
require(lubridate)
require(devtools)
options(stringAsFactors=F)
la()
wd = ('C:\\Users\\Cooka\\OneDrive - DFO-MPO\\BycatchLobster')
setwd(wd)
####Comparison of Lobster Trap catches to Reported on Logs
b=bycatch.db('logbook.merge')
#ISDB
#ISDB
ao = bycatch.db('ISDB.reshape')
aCo = aggregate(cbind(Lobster,Cod,Cusk,Jonah,Legal,Berried, Empty,LegalWt)~TRIP+LFA+Cluster,data=ao,FUN=mean)
aCs = aggregate(cbind(Lobster,Cod,Cusk,Jonah,Legal,Berried, Empty,LegalWt)~TRIP+LFA+Cluster,data=ao,FUN=var,na.rm=T)
aCs = rename.df(aCs,c('Lobster','Cod','Cusk','Jonah','Legal','Berried', 'Empty','LegalWt'),c('Lobstervar','Codvar','Cuskvar','Jonahvar','Legalvar','Berriedvar', 'Emptyvar','LegalWtvar'))
aCC = aggregate(UID~TRIP+LFA+Cluster,data=ao,FUN=function(x) length(unique(x)))
aCo = merge(aCo, aCC)
aCo = merge(aCo, aCs)
aCo$mean =aCo$LegalWt
aCo$var =aCo$LegalWtvar
aCo$n = aCo$UID
#clustered mean
o = list()
ui = unique(aCo$TRIP)
for(i in 1:length(ui)){
j = which(aCo$TRIP == ui[i])
o[[i]] =c(TRIP=unique(aCo$TRIP[j]),LFA=unique(aCo$LFA[j]),clusterStatistics(aCo[j,]))
}
aCo = as.data.frame(do.call(rbind,o))
aCo = toNums(aCo,1:6)
ms = read.csv('data/ISDB_Trip_id to sd_log_idFinal.csv')
ms = subset(ms,!is.na(SD_LOG_ID_1),select=c(trip_id,SD_LOG_ID_1))
bLo = merge(b,ms,by.x='SD_LOG_ID', by.y='SD_LOG_ID_1')
bLoL = aggregate(cbind(WEIGHT_KG,NUM_OF_TRAPS)~SD_LOG_ID+trip_id+LFA+SYEAR,data=bLo,FUN=sum)
SBUMP0 = merge(aCo,bLoL,by.x='TRIP',by.y='trip_id')
SBUMP0$TotLegalNTraps = SBUMP0$mean * SBUMP0$NUM_OF_TRAPS
#SWLSS
aoS = bycatch.db('SWLSS')
aCoS = aggregate(cbind(Lobster,Cod,Cusk,Jonah,Legal,Berried, Empty,LegalWt)~TRIP+LFA+Cluster,data=aoS,FUN=mean)
aCsS = aggregate(cbind(Lobster,Cod,Cusk,Jonah,Legal,Berried, Empty,LegalWt)~TRIP+LFA+Cluster,data=aoS,FUN=var,na.rm=T)
aCsS = rename.df(aCsS,c('Lobster','Cod','Cusk','Jonah','Legal','Berried', 'Empty','LegalWt'),c('Lobstervar','Codvar','Cuskvar','Jonahvar','Legalvar','Berriedvar', 'Emptyvar','LegalWtvar'))
aCCS = aggregate(UID~TRIP+LFA+Cluster,data=aoS,FUN=function(x) length(unique(x)))
aCoS = merge(aCoS, aCCS)
aCoS = merge(aCoS, aCsS)
aCoS$mean =aCoS$LegalWt
aCoS$var =aCoS$LegalWtvar
aCoS$n = aCoS$UID
o = list()
ui = unique(aCoS$TRIP)
for(i in 1:length(ui)){
j = which(aCoS$TRIP == ui[i])
o[[i]] =c(TRIP=unique(aCoS$TRIP[j]),LFA=unique(aCoS$LFA[j]),clusterStatistics(aCoS[j,]))
}
aCoS = as.data.frame(do.call(rbind,o))
aCoS = toNums(aCoS,2:6)
aacc = as.data.frame(rbind(aCo,aCoS))
write.csv(aacc,file='results/TRIPCPUE33-35.csv')
ms = read.csv('data/SWLSSTripmatch.csv')
ms = subset(ms,!is.na(SD_LOG_ID_1),select=c(TRIP,SD_LOG_ID_1))
bLoS = merge(b,ms,by.x='SD_LOG_ID', by.y='SD_LOG_ID_1')
bLoLS = aggregate(cbind(WEIGHT_KG,NUM_OF_TRAPS)~SD_LOG_ID+TRIP+LFA+SYEAR,data=bLoS,FUN=sum)
SBUMPS = merge(aCoS,bLoLS,by.x='TRIP',by.y='TRIP')
SBUMPS$TotLegalNTraps = SBUMPS$mean * SBUMPS$NUM_OF_TRAPS
SBUMP0 = subset(SBUMP0,mean>0)
SBUMPS = subset(SBUMPS,mean>0)
#FOR ALL DATA COMBINED
SB = rbind(SBUMPS,SBUMP0)
require(MASS)
require(SimDesign)
par(mfrow=c(1,2))
with(SBUMPS,plot(WEIGHT_KG,TotLegalNTraps,ylab='Extrapolated landings from Sea Samples',main='SWLSS'))
abline(a=0,b=1)
LMSW = with(SBUMPS,lm((TotLegalNTraps)~(WEIGHT_KG)-1)) #treating the SWLSS data as 'truth'
RLMSW = with(SBUMPS,rlm((TotLegalNTraps)~(WEIGHT_KG)-1)) #treating the SWLSS data as 'truth'
with(SBUMP0,plot(WEIGHT_KG,TotLegalNTraps,ylab='Extrapolated landings from Sea Samples',main='Observer'))
abline(a=0,b=1)
LMO = (with(SBUMP0,lm((TotLegalNTraps)~(WEIGHT_KG)-1)))
RLMO = (with(SBUMP0,rlm((TotLegalNTraps)~(WEIGHT_KG)-1)))
with(SB,plot(WEIGHT_KG,TotLegalNTraps,ylab='Extrapolated landings from Sea Samples',main='All'))
abline(a=0,b=1)
LMA = (with(SB,lm((TotLegalNTraps)~(WEIGHT_KG)-1)))
RLMA = (with(SB,rlm((TotLegalNTraps)~(WEIGHT_KG)-1)))
#RMSE
summary(LMO)$sigma
summary(RLMO)$sigma
summary(LMSW)$sigma
summary(RLMSW)$sigma
summary(LMA)$sigma
summary(RLMA)$sigma
biasSWLSS = SimDesign::bias(SBUMPS$TotLegalNTraps,SBUMPS$WEIGHT_KG,type='relative')
biasOBs = SimDesign::bias( SBUMP0$TotLegalNTraps ,SBUMP0$WEIGHT_KG ,type='relative') # sum(SBUMP0$TotLegalNTraps - SBUMP0$WEIGHT_KG)/nrow(SBUMP0) under
biasC = SimDesign::bias( SB$TotLegalNTraps ,SB$WEIGHT_KG ,type='relative')
#For Each LFA and YEAR
SBS = split(SB, f=list(SB$LFA.x,SB$SYEAR))
SBS = rm.from.list(SBS)
ooo = list()
for(i in 1:length(SBS)){
O = SBS[[i]]
L = with(O,lm((TotLegalNTraps)~(WEIGHT_KG)-1)) #treating the SWLSS data as 'truth'
RL = with(O,rlm((TotLegalNTraps)~(WEIGHT_KG)-1)) #treating the SWLSS data as 'truth'
#RMSE
summary(L)$sigma
summary(RL)$sigma
biasS = SimDesign::bias(O$TotLegalNTraps,O$WEIGHT_KG,type='relative')
ooo[[i]] = c(unique(O$LFA.x),unique(O$SYEAR),nrow(O),biasS,coef(L),summary(L)$sigma,coef(RL),summary(RL)$sigma)
}
ooo = as.data.frame(do.call(rbind,ooo))
###GCIFA and CBFHA
gg = bycatch.db('GCIFA')
aCg = aggregate(cbind(Lobster,Cod,Cusk,Jonah,Legal,Berried, Empty,LegalWt)~TRIP+LFA+Cluster,data=gg,FUN=mean)
aCgg = aggregate(cbind(Lobster,Cod,Cusk,Jonah,Legal,Berried, Empty,LegalWt)~TRIP+LFA+Cluster,data=gg,FUN=var,na.rm=T)
aCg = rename.df(aCg,c('Lobster','Cod','Cusk','Jonah','Legal','Berried', 'Empty','LegalWt'),c('Lobstervar','Codvar','Cuskvar','Jonahvar','Legalvar','Berriedvar', 'Emptyvar','LegalWtvar'))
aCCg = aggregate(UID~TRIP+LFA+Cluster,data=gg,FUN=function(x) length(unique(x)))
aCog = merge(aCg, aCCg)
aCog = merge(aCog, aCCg)
aCog$mean =aCog$LegalWt
aCog$var =aCog$LegalWtvar
aCog$n = aCog$UID
o = list()
ui = unique(aCog$TRIP)
for(i in 1:length(ui)){
j = which(aCog$TRIP == ui[i])
o[[i]] =c(TRIP=unique(aCog$TRIP[j]),LFA=unique(aCog$LFA[j]),clusterStatistics(aCog[j,]))
}
aCog = as.data.frame(do.call(rbind,o))
ggo = toNums(aCog,3:6)
gga=ggo
ms = read.csv('data/GCIFACBFHATripMatch.csv')
#off record matching...skip
#ms$SD_LOG_ID_DO[which(is.na(ms$SD_LOG_ID_DO))] = ms$SD_LOG_ID_DB[which(is.na(ms$SD_LOG_ID_DO))]
#ms$SD_LOG_ID_DO[which(is.na(ms$SD_LOG_ID_DO))] = ms$SD_LOG_ID_DA[which(is.na(ms$SD_LOG_ID_DO))]
ms = subset(ms,select=c(TRIP,SD_LOG_ID_DO))
bLo = merge(b,ms,by.x='SD_LOG_ID', by.y='SD_LOG_ID_DO')
bLoL = aggregate(cbind(WEIGHT_KG,NUM_OF_TRAPS)~SD_LOG_ID+TRIP+LFA+SYEAR,data=bLo,FUN=sum)
bu = merge(ggo,bLoL)
bu$TotLegalNTraps = bu$mean * bu$NUM_OF_TRAPS
GC = split(bu,f=list(bu$LFA,bu$SYEAR))
og = list()
for(i in 1:length(GC)){
O = GC[[i]]
L = with(O,lm((TotLegalNTraps)~(WEIGHT_KG)-1)) #treating the SWLSS data as 'truth'
RL = with(O,rlm((TotLegalNTraps)~(WEIGHT_KG)-1)) #treating the SWLSS data as 'truth'
biasS = SimDesign::bias(O$TotLegalNTraps,O$WEIGHT_KG,type='relative')
og[[i]] = c(unique(O$LFA),unique(O$SYEAR),nrow(O),biasS,coef(L),summary(L)$sigma,coef(RL),summary(RL)$sigma)
}
og = as.data.frame(do.call(rbind,og))
#################
### CBFHA
gg = bycatch.db('CBFHA')
aCg = aggregate(cbind(Lobster,Cod,Cusk,Jonah,Legal,Berried, Empty,LegalWt)~TRIP+LFA+Cluster,data=gg,FUN=mean)
aCgg = aggregate(cbind(Lobster,Cod,Cusk,Jonah,Legal,Berried, Empty,LegalWt)~TRIP+LFA+Cluster,data=gg,FUN=var,na.rm=T)
aCg = rename.df(aCg,c('Lobster','Cod','Cusk','Jonah','Legal','Berried', 'Empty','LegalWt'),c('Lobstervar','Codvar','Cuskvar','Jonahvar','Legalvar','Berriedvar', 'Emptyvar','LegalWtvar'))
aCCg = aggregate(UID~TRIP+LFA+Cluster,data=gg,FUN=function(x) length(unique(x)))
aCog = merge(aCg, aCCg)
aCog = merge(aCog, aCCg)
aCog$mean =aCog$LegalWt
aCog$var =aCog$LegalWtvar
aCog$n = aCog$UID
o = list()
ui = unique(aCog$TRIP)
for(i in 1:length(ui)){
j = which(aCog$TRIP == ui[i])
o[[i]] =c(TRIP=unique(aCog$TRIP[j]),LFA=unique(aCog$LFA[j]),clusterStatistics(aCog[j,]))
}
aCog = as.data.frame(do.call(rbind,o))
ggo = toNums(aCog,3:6)
gggg = as.data.frame(rbind(gga,ggo))
write.csv(gggg,file='results/CBFHAGCIFACPUE.csv')
ms = read.csv('data/GCIFACBFHATripMatch.csv')
#ms$SD_LOG_ID_DO[which(is.na(ms$SD_LOG_ID_DO))] = ms$SD_LOG_ID_DB[which(is.na(ms$SD_LOG_ID_DO))]
#ms$SD_LOG_ID_DO[which(is.na(ms$SD_LOG_ID_DO))] = ms$SD_LOG_ID_DA[which(is.na(ms$SD_LOG_ID_DO))]
ms = subset(ms,select=c(TRIP,SD_LOG_ID_DO))
bLo = merge(b,ms,by.x='SD_LOG_ID', by.y='SD_LOG_ID_DO')
bLoL = aggregate(cbind(WEIGHT_KG,NUM_OF_TRAPS)~SD_LOG_ID+TRIP+LFA+SYEAR,data=bLo,FUN=sum)
bu = merge(ggo,bLoL)
bu$TotLegalNTraps = bu$mean * bu$NUM_OF_TRAPS
GC = split(bu,f=list(bu$LFA,bu$SYEAR))
og = list()
for(i in 1:length(GC)){
O = GC[[i]]
L = with(O,lm((TotLegalNTraps)~(WEIGHT_KG)-1)) #treating the SWLSS data as 'truth'
RL = with(O,rlm((TotLegalNTraps)~(WEIGHT_KG)-1)) #treating the SWLSS data as 'truth'
biasS = SimDesign::bias(O$TotLegalNTraps,O$WEIGHT_KG,type='relative')
og[[i]] = c(unique(O$LFA),unique(O$SYEAR),nrow(O),biasS,coef(L),summary(L)$sigma,coef(RL),summary(RL)$sigma)
}
og = as.data.frame(do.call(rbind,og))
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require(bio.lobster)
require(bio.utilities)
require(RODBC)
require(lubridate)
require(devtools)
options(stringAsFactors=F)
la()
wd = ('C:\\Users\\Cooka\\OneDrive - DFO-MPO\\BycatchLobster')
setwd(wd)
####Comparison of Lobster Trap catches to Reported on Logs
b=bycatch.db('logbook.merge')
#ISDB
#ISDB
ao = bycatch.db('ISDB.reshape')
aCo = aggregate(cbind(Lobster,Cod,Cusk,Jonah,Legal,Berried, Empty,LegalWt)~TRIP+LFA+Cluster,data=ao,FUN=mean)
aCs = aggregate(cbind(Lobster,Cod,Cusk,Jonah,Legal,Berried, Empty,LegalWt)~TRIP+LFA+Cluster,data=ao,FUN=var,na.rm=T)
aCs = rename.df(aCs,c('Lobster','Cod','Cusk','Jonah','Legal','Berried', 'Empty','LegalWt'),c('Lobstervar','Codvar','Cuskvar','Jonahvar','Legalvar','Berriedvar', 'Emptyvar','LegalWtvar'))
aCC = aggregate(UID~TRIP+LFA+Cluster,data=ao,FUN=function(x) length(unique(x)))
aCo = merge(aCo, aCC)
aCo = merge(aCo, aCs)
aCo$mean =aCo$LegalWt
aCo$var =aCo$LegalWtvar
aCo$n = aCo$UID
#clustered mean
o = list()
ui = unique(aCo$TRIP)
for(i in 1:length(ui)){
j = which(aCo$TRIP == ui[i])
o[[i]] =c(TRIP=unique(aCo$TRIP[j]),LFA=unique(aCo$LFA[j]),clusterStatistics(aCo[j,]))
}
aCo = as.data.frame(do.call(rbind,o))
aCo = toNums(aCo,1:6)
ms = read.csv('data/ISDB_Trip_id to sd_log_idFinal.csv')
ms = subset(ms,!is.na(SD_LOG_ID_1),select=c(trip_id,SD_LOG_ID_1))
bLo = merge(b,ms,by.x='SD_LOG_ID', by.y='SD_LOG_ID_1')
bLoL = aggregate(cbind(WEIGHT_KG,NUM_OF_TRAPS)~SD_LOG_ID+trip_id+LFA+SYEAR,data=bLo,FUN=sum)
SBUMP0 = merge(aCo,bLoL,by.x='TRIP',by.y='trip_id')
SBUMP0$TotLegalNTraps = SBUMP0$mean * SBUMP0$NUM_OF_TRAPS
#SWLSS
aoS = bycatch.db('SWLSS')
aCoS = aggregate(cbind(Lobster,Cod,Cusk,Jonah,Legal,Berried, Empty,LegalWt)~TRIP+LFA+Cluster,data=aoS,FUN=mean)
aCsS = aggregate(cbind(Lobster,Cod,Cusk,Jonah,Legal,Berried, Empty,LegalWt)~TRIP+LFA+Cluster,data=aoS,FUN=var,na.rm=T)
aCsS = rename.df(aCsS,c('Lobster','Cod','Cusk','Jonah','Legal','Berried', 'Empty','LegalWt'),c('Lobstervar','Codvar','Cuskvar','Jonahvar','Legalvar','Berriedvar', 'Emptyvar','LegalWtvar'))
aCCS = aggregate(UID~TRIP+LFA+Cluster,data=aoS,FUN=function(x) length(unique(x)))
aCoS = merge(aCoS, aCCS)
aCoS = merge(aCoS, aCsS)
aCoS$mean =aCoS$LegalWt
aCoS$var =aCoS$LegalWtvar
aCoS$n = aCoS$UID
o = list()
ui = unique(aCoS$TRIP)
for(i in 1:length(ui)){
j = which(aCoS$TRIP == ui[i])
o[[i]] =c(TRIP=unique(aCoS$TRIP[j]),LFA=unique(aCoS$LFA[j]),clusterStatistics(aCoS[j,]))
}
aCoS = as.data.frame(do.call(rbind,o))
aCoS = toNums(aCoS,2:6)
aacc = as.data.frame(rbind(aCo,aCoS))
write.csv(aacc,file='results/TRIPCPUE33-35.csv')
ms = read.csv('data/SWLSSTripmatch.csv')
ms = subset(ms,!is.na(SD_LOG_ID_1),select=c(TRIP,SD_LOG_ID_1))
bLoS = merge(b,ms,by.x='SD_LOG_ID', by.y='SD_LOG_ID_1')
bLoLS = aggregate(cbind(WEIGHT_KG,NUM_OF_TRAPS)~SD_LOG_ID+TRIP+LFA+SYEAR,data=bLoS,FUN=sum)
SBUMPS = merge(aCoS,bLoLS,by.x='TRIP',by.y='TRIP')
SBUMPS$TotLegalNTraps = SBUMPS$mean * SBUMPS$NUM_OF_TRAPS
SBUMP0 = subset(SBUMP0,mean>0)
SBUMPS = subset(SBUMPS,mean>0)
#FOR ALL DATA COMBINED
SB = rbind(SBUMPS,SBUMP0)
require(MASS)
require(SimDesign)
par(mfrow=c(1,2))
with(SBUMPS,plot(WEIGHT_KG,TotLegalNTraps,ylab='Extrapolated landings from Sea Samples',main='SWLSS'))
abline(a=0,b=1)
LMSW = with(SBUMPS,lm((TotLegalNTraps)~(WEIGHT_KG)-1)) #treating the SWLSS data as 'truth'
RLMSW = with(SBUMPS,rlm((TotLegalNTraps)~(WEIGHT_KG)-1)) #treating the SWLSS data as 'truth'
with(SBUMP0,plot(WEIGHT_KG,TotLegalNTraps,ylab='Extrapolated landings from Sea Samples',main='Observer'))
abline(a=0,b=1)
LMO = (with(SBUMP0,lm((TotLegalNTraps)~(WEIGHT_KG)-1)))
RLMO = (with(SBUMP0,rlm((TotLegalNTraps)~(WEIGHT_KG)-1)))
with(SB,plot(WEIGHT_KG,TotLegalNTraps,ylab='Extrapolated landings from Sea Samples',main='All'))
abline(a=0,b=1)
LMA = (with(SB,lm((TotLegalNTraps)~(WEIGHT_KG)-1)))
RLMA = (with(SB,rlm((TotLegalNTraps)~(WEIGHT_KG)-1)))
#RMSE
summary(LMO)$sigma
summary(RLMO)$sigma
summary(LMSW)$sigma
summary(RLMSW)$sigma
summary(LMA)$sigma
summary(RLMA)$sigma
biasSWLSS = SimDesign::bias(SBUMPS$TotLegalNTraps,SBUMPS$WEIGHT_KG,type='relative')
biasOBs = SimDesign::bias( SBUMP0$TotLegalNTraps ,SBUMP0$WEIGHT_KG ,type='relative') # sum(SBUMP0$TotLegalNTraps - SBUMP0$WEIGHT_KG)/nrow(SBUMP0) under
biasC = SimDesign::bias( SB$TotLegalNTraps ,SB$WEIGHT_KG ,type='relative')
#For Each LFA and YEAR
SBS = split(SB, f=list(SB$LFA.x,SB$SYEAR))
SBS = rm.from.list(SBS)
ooo = list()
for(i in 1:length(SBS)){
O = SBS[[i]]
L = with(O,lm((TotLegalNTraps)~(WEIGHT_KG)-1)) #treating the SWLSS data as 'truth'
RL = with(O,rlm((TotLegalNTraps)~(WEIGHT_KG)-1)) #treating the SWLSS data as 'truth'
#RMSE
summary(L)$sigma
summary(RL)$sigma
biasS = SimDesign::bias(O$TotLegalNTraps,O$WEIGHT_KG,type='relative')
ooo[[i]] = c(unique(O$LFA.x),unique(O$SYEAR),nrow(O),biasS,coef(L),summary(L)$sigma,coef(RL),summary(RL)$sigma)
}
ooo = as.data.frame(do.call(rbind,ooo))
###GCIFA and CBFHA
gg = bycatch.db('GCIFA')
aCg = aggregate(cbind(Lobster,Cod,Cusk,Jonah,Legal,Berried, Empty,LegalWt)~TRIP+LFA+Cluster,data=gg,FUN=mean)
aCgg = aggregate(cbind(Lobster,Cod,Cusk,Jonah,Legal,Berried, Empty,LegalWt)~TRIP+LFA+Cluster,data=gg,FUN=var,na.rm=T)
aCg = rename.df(aCg,c('Lobster','Cod','Cusk','Jonah','Legal','Berried', 'Empty','LegalWt'),c('Lobstervar','Codvar','Cuskvar','Jonahvar','Legalvar','Berriedvar', 'Emptyvar','LegalWtvar'))
aCCg = aggregate(UID~TRIP+LFA+Cluster,data=gg,FUN=function(x) length(unique(x)))
aCog = merge(aCg, aCCg)
aCog = merge(aCog, aCCg)
aCog$mean =aCog$LegalWt
aCog$var =aCog$LegalWtvar
aCog$n = aCog$UID
o = list()
ui = unique(aCog$TRIP)
for(i in 1:length(ui)){
j = which(aCog$TRIP == ui[i])
o[[i]] =c(TRIP=unique(aCog$TRIP[j]),LFA=unique(aCog$LFA[j]),clusterStatistics(aCog[j,]))
}
aCog = as.data.frame(do.call(rbind,o))
ggo = toNums(aCog,3:6)
gga=ggo
ms = read.csv('data/GCIFACBFHATripMatch.csv')
#off record matching...skip
#ms$SD_LOG_ID_DO[which(is.na(ms$SD_LOG_ID_DO))] = ms$SD_LOG_ID_DB[which(is.na(ms$SD_LOG_ID_DO))]
#ms$SD_LOG_ID_DO[which(is.na(ms$SD_LOG_ID_DO))] = ms$SD_LOG_ID_DA[which(is.na(ms$SD_LOG_ID_DO))]
ms = subset(ms,select=c(TRIP,SD_LOG_ID_DO))
bLo = merge(b,ms,by.x='SD_LOG_ID', by.y='SD_LOG_ID_DO')
bLoL = aggregate(cbind(WEIGHT_KG,NUM_OF_TRAPS)~SD_LOG_ID+TRIP+LFA+SYEAR,data=bLo,FUN=sum)
bu = merge(ggo,bLoL)
bu$TotLegalNTraps = bu$mean * bu$NUM_OF_TRAPS
GC = split(bu,f=list(bu$LFA,bu$SYEAR))
og = list()
for(i in 1:length(GC)){
O = GC[[i]]
L = with(O,lm((TotLegalNTraps)~(WEIGHT_KG)-1)) #treating the SWLSS data as 'truth'
RL = with(O,rlm((TotLegalNTraps)~(WEIGHT_KG)-1)) #treating the SWLSS data as 'truth'
biasS = SimDesign::bias(O$TotLegalNTraps,O$WEIGHT_KG,type='relative')
og[[i]] = c(unique(O$LFA),unique(O$SYEAR),nrow(O),biasS,coef(L),summary(L)$sigma,coef(RL),summary(RL)$sigma)
}
og = as.data.frame(do.call(rbind,og))
#################
### CBFHA
gg = bycatch.db('CBFHA')
aCg = aggregate(cbind(Lobster,Cod,Cusk,Jonah,Legal,Berried, Empty,LegalWt)~TRIP+LFA+Cluster,data=gg,FUN=mean)
aCgg = aggregate(cbind(Lobster,Cod,Cusk,Jonah,Legal,Berried, Empty,LegalWt)~TRIP+LFA+Cluster,data=gg,FUN=var,na.rm=T)
aCg = rename.df(aCg,c('Lobster','Cod','Cusk','Jonah','Legal','Berried', 'Empty','LegalWt'),c('Lobstervar','Codvar','Cuskvar','Jonahvar','Legalvar','Berriedvar', 'Emptyvar','LegalWtvar'))
aCCg = aggregate(UID~TRIP+LFA+Cluster,data=gg,FUN=function(x) length(unique(x)))
aCog = merge(aCg, aCCg)
aCog = merge(aCog, aCCg)
aCog$mean =aCog$LegalWt
aCog$var =aCog$LegalWtvar
aCog$n = aCog$UID
o = list()
ui = unique(aCog$TRIP)
for(i in 1:length(ui)){
j = which(aCog$TRIP == ui[i])
o[[i]] =c(TRIP=unique(aCog$TRIP[j]),LFA=unique(aCog$LFA[j]),clusterStatistics(aCog[j,]))
}
aCog = as.data.frame(do.call(rbind,o))
ggo = toNums(aCog,3:6)
gggg = as.data.frame(rbind(gga,ggo))
write.csv(gggg,file='results/CBFHAGCIFACPUE.csv')
ms = read.csv('data/GCIFACBFHATripMatch.csv')
#ms$SD_LOG_ID_DO[which(is.na(ms$SD_LOG_ID_DO))] = ms$SD_LOG_ID_DB[which(is.na(ms$SD_LOG_ID_DO))]
#ms$SD_LOG_ID_DO[which(is.na(ms$SD_LOG_ID_DO))] = ms$SD_LOG_ID_DA[which(is.na(ms$SD_LOG_ID_DO))]
ms = subset(ms,select=c(TRIP,SD_LOG_ID_DO))
bLo = merge(b,ms,by.x='SD_LOG_ID', by.y='SD_LOG_ID_DO')
bLoL = aggregate(cbind(WEIGHT_KG,NUM_OF_TRAPS)~SD_LOG_ID+TRIP+LFA+SYEAR,data=bLo,FUN=sum)
bu = merge(ggo,bLoL)
bu$TotLegalNTraps = bu$mean * bu$NUM_OF_TRAPS
GC = split(bu,f=list(bu$LFA,bu$SYEAR))
og = list()
for(i in 1:length(GC)){
O = GC[[i]]
L = with(O,lm((TotLegalNTraps)~(WEIGHT_KG)-1)) #treating the SWLSS data as 'truth'
RL = with(O,rlm((TotLegalNTraps)~(WEIGHT_KG)-1)) #treating the SWLSS data as 'truth'
biasS = SimDesign::bias(O$TotLegalNTraps,O$WEIGHT_KG,type='relative')
og[[i]] = c(unique(O$LFA),unique(O$SYEAR),nrow(O),biasS,coef(L),summary(L)$sigma,coef(RL),summary(RL)$sigma)
}
og = as.data.frame(do.call(rbind,og))
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