git-svn-id: https://svn.d4science.research-infrastructures.eu/gcube/trunk/data-analysis/EcologicalEngineSmartExecutor@115360 82a268e6-3cf1-43bd-a215-b396298e98cf
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##--------------------------------------------------------
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## CMSY analysis with estimation of total biomass, including Bayesian Schaefer
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## written by Rainer Froese with support from Gianpaolo Coro in 2013-2014
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## This version adjusts biomass to average biomass over the year
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## It also contains the FutureCrash option to improve prediction of final biomass
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## Version 21 adds the purple point to indicate the 25th percentile of final biomass
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## Version 22 accepts that no biomass or CPUE area available
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##--------------------------------------------------------
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library(R2jags) # Interface with JAGS
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library(coda)
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#-----------------------------------------
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# Some general settings
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#-----------------------------------------
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# set.seed(999) # use for comparing results between runs
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rm(list=ls(all=TRUE)) # clear previous variables etc
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options(digits=3) # displays all numbers with three significant digits as default
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graphics.off() # close graphics windows from previous sessions
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#-----------------------------------------
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# General settings for the analysis
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#-----------------------------------------
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sigR <- 0.02 # overall process error; 0.05 works reasonable for simulations, 0.02 for real data; 0 if deterministic model
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n <- 10000 # initial number of r-k pairs
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batch.mode <- T # set to TRUE to suppress graphs
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write.output <- T # set to true if table of output is wanted
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FutureCrash <- "No"
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#-----------------------------------------
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# Start output to screen
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#-----------------------------------------
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cat("-------------------------------------------\n")
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cat("Catch-MSY Analysis,", date(),"\n")
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cat("-------------------------------------------\n")
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#------------------------------------------
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# Read data and assign to vectors
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#------------------------------------------
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# filename_1 <- "AllStocks_Catch4.csv"
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# filename_2 <- "AllStocks_ID4.csv"
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# filename_1 <- "SimCatch.csv"
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# filename_2 <- "SimSpec.csv"
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# filename_2 <- "SimSpecWrongS.csv"
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# filename_2 <- "SimSpecWrongI.csv"
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# filename_2 <- "SimSpecWrongF.csv"
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# filename_2 <- "SimSpecWrongH.csv"
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# filename_2 <- "SimSpecWrongL.csv"
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# filename_1 <- "FishDataLim.csv"
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# filename_2 <- "FishDataLimSpec.csv"
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filename_1 <- "WKLIFE4Stocks.csv"
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filename_2 <- "WKLIFE4ID.csv"
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outfile<-"outfile"
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outfile.txt <- "outputfile.txt"
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cdat <- read.csv(filename_1, header=T, dec=".", stringsAsFactors = FALSE)
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cinfo <- read.csv(filename_2, header=T, dec=".", stringsAsFactors = FALSE)
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cat("Files", filename_1, ",", filename_2, "read successfully","\n")
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# Stocks with total biomass data and catch data from StartYear to EndYear
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# stocks <- sort(as.character(cinfo$stock)) # All stocks
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stocks<-"HLH_M07"
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# select one stock after the other
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for(stock in stocks) {
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# assign data from cinfo to vectors
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res <- as.character(cinfo$Resilience[cinfo$stock==stock])
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StartYear <- as.numeric(cinfo$StartYear[cinfo$stock==stock])
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EndYear <- as.numeric(cinfo$EndYear[cinfo$stock==stock])
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r_low <- as.numeric(cinfo$r_low[cinfo$stock==stock])
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r_hi <- as.numeric(cinfo$r_hi[cinfo$stock==stock])
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stb_low <- as.numeric(cinfo$stb_low[cinfo$stock==stock])
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stb_hi <- as.numeric(cinfo$stb_hi[cinfo$stock==stock])
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intyr <- as.numeric(cinfo$intyr[cinfo$stock==stock])
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intbio_low <- as.numeric(cinfo$intbio_low[cinfo$stock==stock])
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intbio_hi <- as.numeric(cinfo$intbio_hi[cinfo$stock==stock])
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endbio_low <- as.numeric(cinfo$endbio_low[cinfo$stock==stock])
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endbio_hi <- as.numeric(cinfo$endbio_hi[cinfo$stock==stock])
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Btype <- as.character(cinfo$Btype[cinfo$stock==stock])
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FutureCrash <- as.character(cinfo$FutureCrash[cinfo$stock==stock])
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comment <- as.character(cinfo$comment[cinfo$stock==stock])
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# extract data on stock
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yr <- as.numeric(cdat$yr[cdat$stock==stock & cdat$yr >= StartYear & cdat$yr <= EndYear])
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ct <- as.numeric(cdat$ct[cdat$stock==stock & cdat$yr >= StartYear & cdat$yr <= EndYear])/1000 ## assumes that catch is given in tonnes, transforms to '000 tonnes
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if(Btype=="observed" | Btype=="CPUE" | Btype=="simulated") {
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bt <- as.numeric(cdat$TB[cdat$stock==stock & cdat$yr >= StartYear & cdat$yr <= EndYear])/1000 ## assumes that biomass is in tonnes, transforms to '000 tonnes
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} else {bt <- NA}
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nyr <- length(yr) # number of years in the time series
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if(Btype!="observed") {bio <- bt}
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# change biomass to moving average as assumed by Schaefer (but not for simulations or CPUE)
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# for last year use reported bio
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if(Btype=="observed") {
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ma <- function(x){filter(x,rep(1/2,2),sides=2)}
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bio <- ma(bt)
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bio[length(bio)] <- bt[length(bt)] }
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# initialize vectors for viable r, k, bt
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rv.all <- vector()
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kv.all <- vector()
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btv.all <- matrix(data=vector(),ncol=nyr+1)
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#----------------------------------------------------
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# Determine initial ranges for parameters and biomass
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#----------------------------------------------------
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# initial range of r from input file
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if(is.na(r_low)==F & is.na(r_hi)==F) {
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start_r <- c(r_low,r_hi)
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} else {
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# initial range of r and CatchMult values based on resilience
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if(res == "High") {
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start_r <- c(0.6,1.5)} else if(res == "Medium") {
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start_r <- c(0.2,0.8)} else if(res == "Low") {
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start_r <- c(0.05,0.5)} else { # i.e. res== "Very low"
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start_r <- c(0.015,0.1)}
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}
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# initial range of k values, assuming k will always be larger than max catch
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# and max catch will never be smaller than a quarter of MSY
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start_k <- c(max(ct),16*max(ct)/start_r[1])
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# initial biomass range from input file
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if(is.na(stb_low)==F & is.na(stb_hi)==F) {
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startbio <- c(stb_low,stb_hi)
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} else {
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# us low biomass at start as default
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startbio <- c(0.1,0.5)
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}
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MinYear <- yr[which.min(ct)]
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MaxYear <- yr[which.max(ct)]
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# use year and biomass range for intermediate biomass from input file
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if(is.na(intbio_low)==F & is.na(intbio_hi)==F) {
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intyr <- intyr
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intbio <- c(intbio_low,intbio_hi)
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# else if year of minimum catch is at least 3 years away from StartYear and EndYear of series, use min catch
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} else if((MinYear - StartYear) > 3 & (EndYear - MinYear) > 3 ) {
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# assume that biomass range in year before minimum catch was 0.01 - 0.4
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intyr <- MinYear-1
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intbio <- c(0.01,0.4)
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# else if year of max catch is at least 3 years away from StartYear and EndYear of series, use max catch
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} else if((MaxYear - StartYear) > 3 & (EndYear - MaxYear) > 3 ) {
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# assume that biomass range in year before maximum catch was 0.3 - 0.9
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intyr <- MaxYear-1
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intbio <- c(0.3,0.9)
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} else {
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# assume uninformative range 0-1 in mid-year
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intyr <- as.integer(mean(c(StartYear, EndYear)))
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intbio <- c(0,1) }
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# end of intbio setting
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# final biomass range from input file
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if(is.na(endbio_low)==F & is.na(endbio_hi)==F) {
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endbio <- c(endbio_low,endbio_hi)
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} else {
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# else use Catch/maxCatch to estimate final biomass
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endbio <- if(ct[nyr]/max(ct) > 0.5) {c(0.4,0.8)} else {c(0.01,0.4)}
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} # end of final biomass setting
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#----------------------------------------------
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# MC with Schaefer Function filtering
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#----------------------------------------------
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Schaefer <- function(ri, ki, startbio, intyr, intbio, endbio, sigR, pt) {
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# if stock is not expected to crash within 3 years if last catch continues
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if(FutureCrash == "No") {
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yr.s <- c(yr,EndYear+1,EndYear+2,EndYear+3)
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ct.s <- c(ct,ct[yr==EndYear],ct[yr==EndYear],ct[yr==EndYear])
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nyr.s <- length(yr.s)
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} else{
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yr.s <- yr
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ct.s <- ct
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nyr.s <- nyr
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}
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# create vector for initial biomasses
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startbt <-seq(from =startbio[1], to=startbio[2], by = (startbio[2]-startbio[1])/10)
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# create vectors for viable r, k and bt
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rv <- array(-1:-1,dim=c(length(ri)*length(startbt))) #initialize array with -1. The -1 remaining after the process will be removed
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kv <- array(-1:-1,dim=c(length(ri)*length(startbt)))
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btv <- matrix(data=NA, nrow = (length(ri)*length(startbt)), ncol = nyr+1)
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intyr.i <- which(yr.s==intyr) # get index of intermediate year
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#loop through r-k pairs
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npoints = length(ri)
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nstartb = length(startbt)
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for(i in 1 : npoints) {
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if (i%%1000==0)
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cat(".")
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# create empty vector for annual biomasses
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bt <- vector()
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# loop through range of relative start biomasses
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for(j in startbt) {
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# set initial biomass, including process error
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bt[1]=j*ki[i]*exp(rnorm(1,0, sigR)) ## set biomass in first year
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#loop through years in catch time series
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for(t in 1:nyr.s) { # for all years in the time series
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xt=rnorm(1,0, sigR) # set new random process error for every year
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# calculate biomass as function of previous year's biomass plus surplus production minus catch
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bt[t+1]=(bt[t]+ri[i]*bt[t]*(1-bt[t]/ki[i])-ct.s[t])*exp(xt)
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# if biomass < 0.01 k or > 1.1 k, discard r-k pair
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if(bt[t+1] < 0.01*ki[i] || bt[t+1] > 1.1*ki[i]) { break } # stop looping through years, go to next upper level
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if ((t+1)==intyr.i && (bt[t+1]>(intbio[2]*ki[i]) || bt[t+1]<(intbio[1]*ki[i]))) { break } #intermediate year check
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} # end of loop of years
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# if last biomass falls without expected ranges goto next r-k pair
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if(t < nyr.s || bt[yr.s==EndYear] > (endbio[2]*ki[i]) || bt[yr.s==EndYear] < (endbio[1]*ki[i])) {
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next } else {
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# store r, k, and bt, plot point, then go to next startbt
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rv[((i-1)*nstartb)+j] <- ri[i]
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kv[((i-1)*nstartb)+j] <- ki[i]
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btv[((i-1)*nstartb)+j,] <- bt[1:(nyr+1)]/ki[i] #substitute a row into the matrix, exclude FutureCrash years
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if(pt==T) {points(x=ri[i], y=ki[i], pch=".", cex=2, col="black")
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next }
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}
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} # end of loop of initial biomasses
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} # end of loop of r-k pairs
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rv=rv[rv!=-1]
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kv=kv[kv!=-1]
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btv=na.omit(btv) #delete first line
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cat("\n")
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return(list(rv, kv,btv))
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} # end of Schaefer function
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#------------------------------------------------------------------
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# Uniform sampling of the r-k space
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#------------------------------------------------------------------
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# get random set of r and k from log space distribution
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ri1 = exp(runif(n, log(start_r[1]), log(start_r[2])))
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ki1 = exp(runif(n, log(start_k[1]), log(start_k[2])))
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#-----------------------------------------------------------------
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# Plot data and progress
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#-----------------------------------------------------------------
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#windows(14,9)
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par(mfcol=c(2,3))
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# plot catch
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plot(x=yr, y=ct, ylim=c(0,1.2*max(ct)), type ="l", bty="l", main=paste(stock,"catch"), xlab="Year",
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ylab="Catch", lwd=2)
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points(x=yr[which.max(ct)], y=max(ct), col="red", lwd=2)
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points(x=yr[which.min(ct)], y=min(ct), col="red", lwd=2)
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# plot r-k graph
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plot(ri1, ki1, xlim = start_r, ylim = start_k, log="xy", xlab="r", ylab="k", main="Finding viable r-k", pch=".", cex=2, bty="l", col="lightgrey")
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#1 - Call MC-Schaefer function to preliminary explore the space without prior information
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cat(stock, ": First Monte Carlo filtering of r-k space with ",n," points\n")
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MCA <- Schaefer(ri=ri1, ki=ki1, startbio=startbio, intyr=intyr, intbio=intbio, endbio=endbio, sigR=sigR, pt=T)
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rv.all <- append(rv.all,MCA[[1]])
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kv.all <- append(kv.all,MCA[[2]])
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btv.all <- rbind(btv.all,MCA[[3]])
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#take viable r and k values
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nviablepoints = length(rv.all)
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cat("* Found ",nviablepoints," viable points from ",n," samples\n");
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#if few points were found then resample and shrink the k log space
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if (nviablepoints<=1000){
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log.start_k.new <- log(start_k)
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max_attempts = 3
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current_attempts = 1
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while (nviablepoints<=1000 && current_attempts<=max_attempts){
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if(nviablepoints > 0) {
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log.start_k.new[1] <- mean(c(log.start_k.new[1], min(log(kv.all))))
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log.start_k.new[2] <- mean(c(log.start_k.new[2], max(log(kv.all)))) }
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n.new=n*current_attempts #add more points
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ri1 = exp(runif(n.new, log(start_r[1]), log(start_r[2])))
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ki1 = exp(runif(n.new, log.start_k.new[1], log.start_k.new[2]))
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cat("Shrinking k space: repeating Monte Carlo in the interval [",exp(log.start_k.new[1]),",",exp(log.start_k.new[2]),"]\n")
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cat("Attempt ",current_attempts," of ",max_attempts," with ",n.new," points","\n")
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MCA <- Schaefer(ri=ri1, ki=ki1, startbio=startbio, intyr=intyr, intbio=intbio, endbio=endbio, sigR=sigR, pt=T)
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rv.all <- append(rv.all,MCA[[1]])
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kv.all <- append(kv.all,MCA[[2]])
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btv.all <- rbind(btv.all,MCA[[3]])
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nviablepoints = length(rv.all) #recalculate viable points
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cat("* Found altogether",nviablepoints," viable points \n");
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current_attempts=current_attempts+1 #increment the number of attempts
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}
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}
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# If tip of viable r-k pairs is 'thin', do extra sampling there
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gm.rv = exp(mean(log(rv.all)))
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if(length(rv.all[rv.all > 0.9*start_r[2]]) < 10) {
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l.sample.r <- (gm.rv + max(rv.all))/2
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cat("Final sampling in the tip area above r =",l.sample.r,"\n")
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log.start_k.new <- c(log(0.8*min(kv.all)),log(max(kv.all[rv.all > l.sample.r])))
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ri1 = exp(runif(50000, log(l.sample.r), log(start_r[2])))
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ki1 = exp(runif(50000, log.start_k.new[1], log.start_k.new[2]))
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MCA <- Schaefer(ri=ri1, ki=ki1, startbio=startbio, intyr=intyr, intbio=intbio, endbio=endbio, sigR=sigR, pt=T)
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rv.all <- append(rv.all,MCA[[1]])
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kv.all <- append(kv.all,MCA[[2]])
|
|
||||||
btv.all <- rbind(btv.all,MCA[[3]])
|
|
||||||
nviablepoints = length(rv.all) #recalculate viable points
|
|
||||||
cat("Found altogether", length(rv.all), "unique viable r-k pairs and biomass trajectories\n")
|
|
||||||
}
|
|
||||||
|
|
||||||
|
|
||||||
# ------------------------------------------------------------
|
|
||||||
# Bayesian analysis of catch & biomass with Schaefer model
|
|
||||||
# ------------------------------------------------------------
|
|
||||||
if(Btype == "observed" | Btype=="simulated") {
|
|
||||||
cat("Running Schaefer MCMC analysis....\n")
|
|
||||||
mcmc.burn <- as.integer(30000)
|
|
||||||
mcmc.chainLength <- as.integer(60000) # burn-in plus post-burn
|
|
||||||
mcmc.thin = 10 # to reduce autocorrelation
|
|
||||||
mcmc.chains = 3 # needs to be at least 2 for DIC
|
|
||||||
|
|
||||||
# Parameters to be returned by JAGS
|
|
||||||
jags.save.params=c('r','k','sigma.b', 'alpha', 'sigma.r') #
|
|
||||||
|
|
||||||
# JAGS model
|
|
||||||
Model = "model{
|
|
||||||
# to avoid crash due to 0 values
|
|
||||||
eps<-0.01
|
|
||||||
# set a quite narrow variation from the expected value
|
|
||||||
sigma.b <- 1/16
|
|
||||||
tau.b <- pow(sigma.b,-2)
|
|
||||||
|
|
||||||
Bm[1] <- log(alpha*k)
|
|
||||||
bio[1] ~ dlnorm(Bm[1],tau.b)
|
|
||||||
|
|
||||||
|
|
||||||
for (t in 2:nyr){
|
|
||||||
bio[t] ~ dlnorm(Bm[t],tau.b)
|
|
||||||
Bm[t] <- log(max(bio[t-1] + r*bio[t-1]*(1 - (bio[t-1])/k) - ct[t-1], eps))
|
|
||||||
}
|
|
||||||
|
|
||||||
# priors
|
|
||||||
alpha ~ dunif(0.01,1) # needed for fit of first biomass
|
|
||||||
#inverse cubic root relationship between the range of viable r and the size of the search space
|
|
||||||
inverseRangeFactor <- 1/((start_r[2]-start_r[1])^1/3)
|
|
||||||
|
|
||||||
# give sigma some variability in the inverse relationship
|
|
||||||
sigma.r ~ dunif(0.001*inverseRangeFactor,0.02*inverseRangeFactor)
|
|
||||||
tau.r <- pow(sigma.r,-2)
|
|
||||||
rm <- log((start_r[1]+start_r[2])/2)
|
|
||||||
r ~ dlnorm(rm,tau.r)
|
|
||||||
|
|
||||||
# search in the k space from the center of the range. Allow high variability
|
|
||||||
km <- log((start_k[1]+start_k[2])/2)
|
|
||||||
tau.k <- pow(km,-2)
|
|
||||||
k ~ dlnorm(km,tau.k)
|
|
||||||
|
|
||||||
#end model
|
|
||||||
}"
|
|
||||||
|
|
||||||
# Write JAGS model to file
|
|
||||||
cat(Model, file="r2jags.bug")
|
|
||||||
|
|
||||||
### random seed
|
|
||||||
set.seed(runif(1,1,500)) # needed in JAGS
|
|
||||||
|
|
||||||
### run model
|
|
||||||
jags_outputs <- jags(data=c('ct','bio','nyr', 'start_r', 'start_k'),
|
|
||||||
working.directory=NULL, inits=NULL,
|
|
||||||
parameters.to.save= jags.save.params,
|
|
||||||
model.file="r2jags.bug", n.chains = mcmc.chains,
|
|
||||||
n.burnin = mcmc.burn, n.thin = mcmc.thin, n.iter = mcmc.chainLength,
|
|
||||||
refresh=mcmc.burn/20, )
|
|
||||||
|
|
||||||
# ------------------------------------------------------
|
|
||||||
# Results from JAGS Schaefer
|
|
||||||
# ------------------------------------------------------
|
|
||||||
r_out <- as.numeric(mcmc(jags_outputs$BUGSoutput$sims.list$r))
|
|
||||||
k_out <- as.numeric(mcmc(jags_outputs$BUGSoutput$sims.list$k))
|
|
||||||
## sigma_out <- as.numeric(mcmc(jags_outputs$BUGSoutput$sims.list$sigma.b))
|
|
||||||
alpha_out <- as.numeric(mcmc(jags_outputs$BUGSoutput$sims.list$alpha))
|
|
||||||
## sigma.r_out <- as.numeric(mcmc(jags_outputs$BUGSoutput$sims.list$sigma.r))
|
|
||||||
|
|
||||||
mean.log.r.jags <- mean(log(r_out))
|
|
||||||
SD.log.r.jags <- sd(log(r_out))
|
|
||||||
lcl.log.r.jags <- mean.log.r.jags-1.96*SD.log.r.jags
|
|
||||||
ucl.log.r.jags <- mean.log.r.jags+1.96*SD.log.r.jags
|
|
||||||
gm.r.jags <- exp(mean.log.r.jags)
|
|
||||||
lcl.r.jags <- exp(lcl.log.r.jags)
|
|
||||||
ucl.r.jags <- exp(ucl.log.r.jags)
|
|
||||||
mean.log.k.jags <- mean(log(k_out))
|
|
||||||
SD.log.k.jags <- sd(log(k_out))
|
|
||||||
lcl.log.k.jags <- mean.log.k.jags-1.96*SD.log.k.jags
|
|
||||||
ucl.log.k.jags <- mean.log.k.jags+1.96*SD.log.k.jags
|
|
||||||
gm.k.jags <- exp(mean.log.k.jags)
|
|
||||||
lcl.k.jags <- exp(lcl.log.k.jags)
|
|
||||||
ucl.k.jags <- exp(ucl.log.k.jags)
|
|
||||||
mean.log.MSY.jags<- mean(log(r_out)+log(k_out)-log(4))
|
|
||||||
SD.log.MSY.jags <- sd(log(r_out)+log(k_out)-log(4))
|
|
||||||
gm.MSY.jags <- exp(mean.log.MSY.jags)
|
|
||||||
lcl.MSY.jags <- exp(mean.log.MSY.jags-1.96*SD.log.MSY.jags)
|
|
||||||
ucl.MSY.jags <- exp(mean.log.MSY.jags+1.96*SD.log.MSY.jags)
|
|
||||||
|
|
||||||
} # end of MCMC Schaefer loop
|
|
||||||
|
|
||||||
#------------------------------------
|
|
||||||
# get results from CMSY
|
|
||||||
#------------------------------------
|
|
||||||
# get estimate of most probable r as median of mid log.r-classes above cut-off
|
|
||||||
# get remaining viable log.r and log.k
|
|
||||||
rem.log.r <- log(rv.all[rv.all > gm.rv])
|
|
||||||
rem.log.k <- log(kv.all[rv.all>gm.rv])
|
|
||||||
# get vectors with numbers of r and mid values in about 25 classes
|
|
||||||
hist.log.r <- hist(x=rem.log.r, breaks=25, plot=F)
|
|
||||||
log.r.counts <- hist.log.r$counts
|
|
||||||
log.r.mids <- hist.log.r$mids
|
|
||||||
# get most probable log.r as mean of mids with counts > 0
|
|
||||||
log.r.est <- median(log.r.mids[which(log.r.counts > 0)])
|
|
||||||
lcl.log.r <- as.numeric(quantile(x=log.r.mids[which(log.r.counts > 0)], 0.025))
|
|
||||||
ucl.log.r <- as.numeric(quantile(x=log.r.mids[which(log.r.counts > 0)], 0.975))
|
|
||||||
r.est <- exp(log.r.est)
|
|
||||||
lcl.r.est <- exp(lcl.log.r)
|
|
||||||
ucl.r.est <- exp(ucl.log.r)
|
|
||||||
|
|
||||||
# do linear regression of log k ~ log r with slope fixed to -1 (from Schaefer)
|
|
||||||
reg <- lm(rem.log.k ~ 1 + offset(-1*rem.log.r))
|
|
||||||
int.reg <- as.numeric(reg[1])
|
|
||||||
sd.reg <- sd(resid(reg))
|
|
||||||
se.reg <- summary(reg)$coefficients[2]
|
|
||||||
# get estimate of log(k) from y where x = log.r.est
|
|
||||||
log.k.est <- int.reg + (-1) * log.r.est
|
|
||||||
# get estimates of CL of log.k.est from y +/- SD where x = lcl.log r or ucl.log.r
|
|
||||||
lcl.log.k <- int.reg + (-1) * ucl.log.r - sd.reg
|
|
||||||
ucl.log.k <- int.reg + (-1) * lcl.log.r + sd.reg
|
|
||||||
k.est <- exp(log.k.est)
|
|
||||||
lcl.k.est <- exp(lcl.log.k)
|
|
||||||
ucl.k.est <- exp(ucl.log.k)
|
|
||||||
|
|
||||||
# get MSY from remaining log r-k pairs
|
|
||||||
log.MSY.est <- mean(rem.log.r + rem.log.k - log(4))
|
|
||||||
sd.log.MSY.est <- sd(rem.log.r + rem.log.k - log(4))
|
|
||||||
lcl.log.MSY.est <- log.MSY.est - 1.96*sd.log.MSY.est
|
|
||||||
ucl.log.MSY.est <- log.MSY.est + 1.96*sd.log.MSY.est
|
|
||||||
MSY.est <- exp(log.MSY.est)
|
|
||||||
lcl.MSY.est <- exp(lcl.log.MSY.est)
|
|
||||||
ucl.MSY.est <- exp(ucl.log.MSY.est)
|
|
||||||
|
|
||||||
# get predicted biomass vectors as median and quantiles of trajectories
|
|
||||||
median.btv <- apply(btv.all,2, median)
|
|
||||||
lastyr.bio <- median.btv[length(median.btv)-1]
|
|
||||||
nextyr.bio <- median.btv[length(median.btv)]
|
|
||||||
lcl.btv <- apply(btv.all,2, quantile, probs=0.025)
|
|
||||||
q.btv <- apply(btv.all,2, quantile, probs=0.25)
|
|
||||||
ucl.btv <- apply(btv.all,2, quantile, probs=0.975)
|
|
||||||
lcl.lastyr.bio <- lcl.btv[length(lcl.btv)-1]
|
|
||||||
ucl.lastyr.bio <- ucl.btv[length(lcl.btv)-1]
|
|
||||||
lcl.nextyr.bio <- lcl.btv[length(lcl.btv)]
|
|
||||||
ucl.nextyr.bio <- ucl.btv[length(lcl.btv)]
|
|
||||||
|
|
||||||
# -----------------------------------------
|
|
||||||
# Plot results
|
|
||||||
# -----------------------------------------
|
|
||||||
# Analysis of viable r-k pairs
|
|
||||||
plot(x=rv.all, y=kv.all, xlim=start_r,
|
|
||||||
ylim=c(0.9*min(kv.all, ifelse(Btype == "observed",k_out,NA), na.rm=T), 1.1*max(kv.all)),
|
|
||||||
pch=16, col="grey",log="xy", bty="l",
|
|
||||||
xlab="r", ylab="k", main="Analysis of viable r-k")
|
|
||||||
abline(v=gm.rv, lty="dashed")
|
|
||||||
|
|
||||||
# plot points and best estimate from full Schaefer analysis
|
|
||||||
if(Btype == "observed"|Btype=="simulated") {
|
|
||||||
# plot r-k pairs from MCMC
|
|
||||||
points(x=r_out, y=k_out, pch=16,cex=0.5)
|
|
||||||
# plot best r-k pair from MCMC
|
|
||||||
points(x=gm.r.jags, y=gm.k.jags, pch=19, col="green")
|
|
||||||
lines(x=c(lcl.r.jags, ucl.r.jags),y=c(gm.k.jags,gm.k.jags), col="green")
|
|
||||||
lines(x=c(gm.r.jags,gm.r.jags),y=c(lcl.k.jags, ucl.k.jags), col="green")
|
|
||||||
}
|
|
||||||
|
|
||||||
# if data are from simulation, plot true r and k
|
|
||||||
if(Btype=="simulated") {
|
|
||||||
l.stock <- nchar(stock) # get length of sim stock name
|
|
||||||
r.char <- substr(stock,l.stock-1,l.stock) # get last character of sim stock name
|
|
||||||
r.sim <- NA # initialize vector for r used in simulation
|
|
||||||
if(r.char=="_H") {r.sim=1; lcl.r.sim=0.8; ucl.r.sim=1.25} else
|
|
||||||
if(r.char=="_M") {r.sim=0.5;lcl.r.sim=0.4;ucl.r.sim=0.62} else
|
|
||||||
if(r.char=="_L") {r.sim=0.25;lcl.r.sim=0.2;ucl.r.sim=0.31} else {r.sim=0.05;lcl.r.sim=0.04;ucl.r.sim=0.062}
|
|
||||||
# plot true r-k point with error bars
|
|
||||||
points(x=r.sim, y=1000, pch=19, col="red")
|
|
||||||
# add +/- 20% error bars
|
|
||||||
lines(x=c(lcl.r.sim,ucl.r.sim), y=c(1000,1000), col="red")
|
|
||||||
lines(x=c(r.sim,r.sim), y=c(800,1250), col="red")
|
|
||||||
}
|
|
||||||
|
|
||||||
# plot blue dot for proposed r-k, with 95% CL lines
|
|
||||||
points(x=r.est, y=k.est, pch=19, col="blue")
|
|
||||||
lines(x=c(lcl.r.est, ucl.r.est),y=c(k.est,k.est), col="blue")
|
|
||||||
lines(x=c(r.est,r.est),y=c(lcl.k.est, ucl.k.est), col="blue")
|
|
||||||
|
|
||||||
# plot biomass graph
|
|
||||||
# determine k to use for red line in b/k plot
|
|
||||||
if(Btype=="simulated") {k2use <- 1000} else
|
|
||||||
if(Btype == "observed") {k2use <- gm.k.jags} else {k2use <- k.est}
|
|
||||||
# determine hight of y-axis in plot
|
|
||||||
max.y <- max(c(bio/k2use,ucl.btv,0.6,startbio[2], intbio[2],endbio[2]),na.rm=T)
|
|
||||||
|
|
||||||
plot(x=yr,y=median.btv[1:nyr], lwd=2, xlab="Year", ylab="Relative biomass b/k", type="l",
|
|
||||||
ylim=c(0,max.y), bty="l", main=paste("Pred. biomass vs ", Btype,sep=""))
|
|
||||||
lines(x=yr, y=lcl.btv[1:nyr],type="l")
|
|
||||||
lines(x=yr, y=ucl.btv[1:nyr],type="l")
|
|
||||||
points(x=EndYear,y=q.btv[yr==EndYear], col="purple", cex=1.5, lwd=2)
|
|
||||||
abline(h=0.5, lty="dashed")
|
|
||||||
abline(h=0.25, lty="dotted")
|
|
||||||
lines(x=c(yr[1],yr[1]), y=startbio, col="blue")
|
|
||||||
lines(x=c(intyr,intyr), y=intbio, col="blue")
|
|
||||||
lines(x=c(max(yr),max(yr)), y=endbio, col="blue")
|
|
||||||
|
|
||||||
# if observed biomass is available, plot red biomass line
|
|
||||||
if(Btype == "observed"|Btype=="simulated") {
|
|
||||||
lines(x=yr, y=bio/k2use,type="l", col="red", lwd=1)
|
|
||||||
}
|
|
||||||
|
|
||||||
# if CPUE data are available, scale to predicted biomass range, plot red biomass line
|
|
||||||
if(Btype == "CPUE") {
|
|
||||||
par(new=T) # prepares for new plot on top of previous
|
|
||||||
plot(x=yr, y=bio, type="l", col="red", lwd=1,
|
|
||||||
ann=F,axes=F,ylim=c(0,1.2*max(bio, na.rm=T))) # forces this plot on top of previous one
|
|
||||||
axis(4, col="red", col.axis="red")
|
|
||||||
}
|
|
||||||
|
|
||||||
# plot yield and biomass against equilibrium surplus parabola
|
|
||||||
max.y <-max(c(ct/MSY.est,ifelse(Btype=="observed"|Btype=="simulated",ct/gm.MSY.jags,NA),1.2),na.rm=T)
|
|
||||||
# plot parabola
|
|
||||||
x=seq(from=0,to=2,by=0.001)
|
|
||||||
y=4*x-(2*x)^2
|
|
||||||
plot(x=x, y=y, xlim=c(0,1), ylim=c(0,max.y), type="l", bty="l",xlab="Relative biomass b/k",
|
|
||||||
ylab="Catch / MSY", main="Equilibrium curve")
|
|
||||||
# plot catch against CMSY biomass estimates
|
|
||||||
points(x=median.btv[1:nyr], y=ct/MSY.est, pch=16, col="grey")
|
|
||||||
points(x=q.btv[yr==EndYear],y=ct[yr==EndYear]/MSY.est, col="purple", cex=1.5, lwd=2)
|
|
||||||
# plot catch against observed biomass or CPUE
|
|
||||||
if(Btype == "observed"|Btype=="simulated") {
|
|
||||||
points(x=bio/k2use, y=ct/gm.MSY.jags, pch=16, cex=0.5)
|
|
||||||
}
|
|
||||||
|
|
||||||
# plot exploitation rate u against u.msy
|
|
||||||
# get u derived from predicted CMSY biomass
|
|
||||||
u.CMSY <- ct/(median.btv[1:nyr]*k.est)
|
|
||||||
u.msy.CMSY <- 1-exp(-r.est/2) # # Fmsy from CMSY expressed as exploitation rate
|
|
||||||
# get u from observed or simulated biomass
|
|
||||||
if(Btype == "observed"|Btype=="simulated") {
|
|
||||||
u.bio <- ct/bio
|
|
||||||
u.msy.bio <- 1-exp(-gm.r.jags/2)
|
|
||||||
}
|
|
||||||
# get u from CPUE
|
|
||||||
if(Btype == "CPUE") {
|
|
||||||
q=max(median.btv[1:nyr][is.na(bio)==F],na.rm=T)*k.est/max(bio,na.rm=T)
|
|
||||||
u.CPUE <- ct/(q*bio)
|
|
||||||
}
|
|
||||||
|
|
||||||
# determine upper bound of Y-axis
|
|
||||||
max.y <- max(c(1.5, 1.2*u.CMSY/u.msy.CMSY,ct[yr==EndYear]/(q.btv[yr==EndYear]*k.est)/u.msy.CMSY,
|
|
||||||
ifelse(Btype=="observed"|Btype=="simulated",max(u.bio[is.na(u.bio)==F]/u.msy.bio),0),
|
|
||||||
na.rm=T))
|
|
||||||
# plot u from CMSY
|
|
||||||
plot(x=yr,y=u.CMSY/u.msy.CMSY, type="l", bty="l", ylim=c(0,max.y), xlab="Year",
|
|
||||||
ylab="u / u_msy", main="Exploitation rate")
|
|
||||||
abline(h=1, lty="dashed")
|
|
||||||
points(x=EndYear,y=ct[yr==EndYear]/(q.btv[yr==EndYear]*k.est)/u.msy.CMSY, col="purple", cex=1.5, lwd=2)
|
|
||||||
# plot u from biomass
|
|
||||||
if(Btype == "observed"|Btype=="simulated") lines(x=yr, y=u.bio/u.msy.bio, col="red")
|
|
||||||
# plot u from CPUE
|
|
||||||
if(Btype == "CPUE") {
|
|
||||||
par(new=T) # prepares for new plot on top of previous
|
|
||||||
plot(x=yr, y=u.CPUE, type="l", col="red", ylim=c(0, 1.2*max(u.CPUE,na.rm=T)),ann=F,axes=F)
|
|
||||||
axis(4, col="red", col.axis="red")
|
|
||||||
}
|
|
||||||
if(batch.mode == TRUE) {dev.off()} # close plot window
|
|
||||||
|
|
||||||
# ------------------------------------------
|
|
||||||
# print input and results to screen
|
|
||||||
cat("---------------------------------------\n")
|
|
||||||
|
|
||||||
cat("Species:", cinfo$ScientificName[cinfo$stock==stock], "\n")
|
|
||||||
cat("Name and region:", cinfo$EnglishName[cinfo$stock==stock], ",", cinfo$Name[cinfo$stock==stock], "\n")
|
|
||||||
cat("Stock:",stock,"\n")
|
|
||||||
cat("Catch data used from years", min(yr),"-", max(yr), "\n")
|
|
||||||
cat("Prior initial relative biomass =", startbio[1], "-", startbio[2], "\n")
|
|
||||||
cat("Prior intermediate rel. biomass=", intbio[1], "-", intbio[2], "in year", intyr, "\n")
|
|
||||||
cat("Prior final relative biomass =", endbio[1], "-", endbio[2], "\n")
|
|
||||||
cat("If current catches continue, is the stock likely to crash within 3 years?",FutureCrash,"\n")
|
|
||||||
cat("Prior range for r =", format(start_r[1],digits=2), "-", format(start_r[2],digits=2),
|
|
||||||
", prior range for k =", start_k[1], "-", start_k[2],"\n")
|
|
||||||
|
|
||||||
# if data are simulated, print true r-k
|
|
||||||
if(filename_1=="SimCatch.csv") {
|
|
||||||
cat("True r =", r.sim, "(because input data were simulated with Schaefer model)\n")
|
|
||||||
cat("True k = 1000 \n")
|
|
||||||
cat("True MSY =", 1000*r.sim/4,"\n")
|
|
||||||
cat("True biomass in last year =",bio[length(bio)],"or",bio[length(bio)]/1000,"k \n")
|
|
||||||
cat("True mean catch / MSY ratio =", mean(ct)/(1000*r.sim/4),"\n")
|
|
||||||
}
|
|
||||||
# print results from full Schaefer if available
|
|
||||||
if(Btype == "observed"|Btype=="simulated") {
|
|
||||||
cat("Results from Bayesian Schaefer model using catch & biomass (",Btype,")\n")
|
|
||||||
cat("MSY =", gm.MSY.jags,", 95% CL =", lcl.MSY.jags, "-", ucl.MSY.jags,"\n")
|
|
||||||
cat("Mean catch / MSY =", mean(ct)/gm.MSY.jags,"\n")
|
|
||||||
if(Btype != "CPUE") {
|
|
||||||
cat("r =", gm.r.jags,", 95% CL =", lcl.r.jags, "-", ucl.r.jags,"\n")
|
|
||||||
cat("k =", gm.k.jags,", 95% CL =", lcl.k.jags, "-", ucl.k.jags,"\n")
|
|
||||||
}
|
|
||||||
}
|
|
||||||
# results of CMSY analysis
|
|
||||||
cat("Results of CMSY analysis \n")
|
|
||||||
cat("Altogether", nviablepoints,"unique viable r-k pairs were found \n")
|
|
||||||
cat(nviablepoints-length(rem.log.r),"r-k pairs above the initial geometric mean of r =", gm.rv, "were analysed\n")
|
|
||||||
cat("r =", r.est,", 95% CL =", lcl.r.est, "-", ucl.r.est,"\n")
|
|
||||||
cat("k =", k.est,", 95% CL =", lcl.k.est, "-", ucl.k.est,"\n")
|
|
||||||
cat("MSY =", MSY.est,", 95% CL =", lcl.MSY.est, "-", ucl.MSY.est,"\n")
|
|
||||||
cat("Predicted biomass in last year =", lastyr.bio, "2.5th perc =", lcl.lastyr.bio,
|
|
||||||
"97.5th perc =", ucl.lastyr.bio,"\n")
|
|
||||||
cat("Predicted biomass in next year =", nextyr.bio, "2.5th perc =", lcl.nextyr.bio,
|
|
||||||
"97.5th perc =", ucl.nextyr.bio,"\n")
|
|
||||||
cat("----------------------------------------------------------\n")
|
|
||||||
|
|
||||||
## Write some results into outfile
|
|
||||||
if(write.output == TRUE) {
|
|
||||||
# write data into csv file
|
|
||||||
output = data.frame(cinfo$ScientificName[cinfo$stock==stock], stock, StartYear, EndYear, mean(ct)*1000,
|
|
||||||
ifelse(Btype=="observed"|Btype=="simulate",bio[length(bio)],NA), # last biomass on record
|
|
||||||
ifelse(Btype == "observed"|Btype=="simulated",gm.MSY.jags,NA), # full Schaefer
|
|
||||||
ifelse(Btype == "observed"|Btype=="simulated",lcl.MSY.jags,NA),
|
|
||||||
ifelse(Btype == "observed"|Btype=="simulated",ucl.MSY.jags,NA),
|
|
||||||
ifelse(Btype == "observed"|Btype=="simulated",gm.r.jags,NA),
|
|
||||||
ifelse(Btype == "observed"|Btype=="simulated",lcl.r.jags,NA),
|
|
||||||
ifelse(Btype == "observed"|Btype=="simulated",ucl.r.jags,NA),
|
|
||||||
ifelse(Btype == "observed"|Btype=="simulated",gm.k.jags,NA),
|
|
||||||
ifelse(Btype == "observed"|Btype=="simulated",lcl.k.jags,NA),
|
|
||||||
ifelse(Btype == "observed"|Btype=="simulated",ucl.k.jags,NA),
|
|
||||||
r.est, lcl.r.est, ucl.r.est, # CMSY r
|
|
||||||
k.est, lcl.k.est, ucl.k.est, # CMSY k
|
|
||||||
MSY.est, lcl.MSY.est, ucl.MSY.est, # CMSY r
|
|
||||||
lastyr.bio, lcl.lastyr.bio, ucl.lastyr.bio, # last year bio
|
|
||||||
nextyr.bio, lcl.nextyr.bio, ucl.nextyr.bio)# last year + 1 bio
|
|
||||||
|
|
||||||
write.table(output, file=outfile, append = T, sep = ",",
|
|
||||||
dec = ".", row.names = FALSE, col.names = FALSE)
|
|
||||||
|
|
||||||
# write some text into text outfile.txt
|
|
||||||
|
|
||||||
cat("Species:", cinfo$ScientificName[cinfo$stock==stock], "\n",
|
|
||||||
"Name:", cinfo$EnglishName[cinfo$stock==stock], "\n",
|
|
||||||
"Region:", cinfo$Name[cinfo$stock==stock], "\n",
|
|
||||||
"Stock:",stock,"\n",
|
|
||||||
"Catch data used from years", min(yr),"-", max(yr),", biomass =", Btype, "\n",
|
|
||||||
"Prior initial relative biomass =", startbio[1], "-", startbio[2], "\n",
|
|
||||||
"Prior intermediate rel. biomass=", intbio[1], "-", intbio[2], "in year", intyr, "\n",
|
|
||||||
"Prior final relative biomass =", endbio[1], "-", endbio[2], "\n",
|
|
||||||
"Future crash with current catches?", FutureCrash, "\n",
|
|
||||||
"Prior range for r =", format(start_r[1],digits=2), "-", format(start_r[2],digits=2),
|
|
||||||
", prior range for k =", start_k[1], "-", start_k[2],"\n",
|
|
||||||
file=outfile.txt,append=T)
|
|
||||||
|
|
||||||
if(filename_1=="SimCatch.csv") {
|
|
||||||
cat(" True r =", r.sim, "(because input data were simulated with Schaefer model)\n",
|
|
||||||
"True k = 1000, true MSY =", 1000*r.sim/4,"\n",
|
|
||||||
"True biomass in last year =",bio[length(bio)],"or",bio[length(bio)]/1000,"k \n",
|
|
||||||
"True mean catch / MSY ratio =", mean(ct)/(1000*r.sim/4),"\n",
|
|
||||||
file=outfile.txt,append=T)
|
|
||||||
}
|
|
||||||
if(Btype == "observed"|Btype=="simulated") {
|
|
||||||
cat(" Results from Bayesian Schaefer model using catch & biomass \n",
|
|
||||||
"r =", gm.r.jags,", 95% CL =", lcl.r.jags, "-", ucl.r.jags,"\n",
|
|
||||||
"k =", gm.k.jags,", 95% CL =", lcl.k.jags, "-", ucl.k.jags,"\n",
|
|
||||||
"MSY =", gm.MSY.jags,", 95% CL =", lcl.MSY.jags, "-", ucl.MSY.jags,"\n",
|
|
||||||
"Mean catch / MSY =", mean(ct)/gm.MSY.jags,"\n",
|
|
||||||
file=outfile.txt,append=T)
|
|
||||||
}
|
|
||||||
cat(" Results of CMSY analysis with altogether", nviablepoints,"unique viable r-k pairs \n",
|
|
||||||
nviablepoints-length(rem.log.r),"r-k pairs above the initial geometric mean of r =", gm.rv, "were analysed\n",
|
|
||||||
"r =", r.est,", 95% CL =", lcl.r.est, "-", ucl.r.est,"\n",
|
|
||||||
"k =", k.est,", 95% CL =", lcl.k.est, "-", ucl.k.est,"\n",
|
|
||||||
"MSY =", MSY.est,", 95% CL =", lcl.MSY.est, "-", ucl.MSY.est,"\n",
|
|
||||||
"Predicted biomass last year b/k =", lastyr.bio, "2.5th perc b/k =", lcl.lastyr.bio,
|
|
||||||
"97.5th perc b/k =", ucl.lastyr.bio,"\n",
|
|
||||||
"Precautionary 25th percentile b/k =",q.btv[yr==EndYear],"\n",
|
|
||||||
"----------------------------------------------------------\n",
|
|
||||||
file=outfile.txt,append=T)
|
|
||||||
|
|
||||||
}
|
|
||||||
|
|
||||||
} # end of stocks loop
|
|
||||||
|
|
@ -1,17 +0,0 @@
|
||||||
<?xml version='1.0' encoding='UTF-8'?>
|
|
||||||
<hibernate-configuration>
|
|
||||||
<session-factory>
|
|
||||||
<property name="connection.driver_class">org.postgresql.Driver</property>
|
|
||||||
<property name="connection.provider_class">org.hibernate.connection.C3P0ConnectionProvider</property>
|
|
||||||
<property name="connection.url">jdbc:postgresql://localhost/testdb</property>
|
|
||||||
<property name="connection.username">gcube</property>
|
|
||||||
<property name="connection.password">d4science2</property>
|
|
||||||
<property name="dialect">org.hibernate.dialect.PostgreSQLDialect</property>
|
|
||||||
<property name="transaction.factory_class">org.hibernate.transaction.JDBCTransactionFactory</property>
|
|
||||||
<property name="c3p0.timeout">0</property>
|
|
||||||
<property name="c3p0.max_size">1</property>
|
|
||||||
<property name="c3p0.max_statements">0</property>
|
|
||||||
<property name="c3p0.min_size">1</property>
|
|
||||||
<property name="current_session_context_class">thread</property>
|
|
||||||
</session-factory>
|
|
||||||
</hibernate-configuration>
|
|
||||||
Binary file not shown.
|
|
@ -1,530 +0,0 @@
|
||||||
#### R and JAGS code for estimating LWR-parameters from previous studies
|
|
||||||
#### Meant for updating the ESTIMATE table in FishBase
|
|
||||||
#### Created by Rainer Froese in March 2013, including JAGS models by James Thorston
|
|
||||||
#### Modified in June 2013 to include subfamilies
|
|
||||||
|
|
||||||
rm(list=ls(all=TRUE)) # remove previous variables and data
|
|
||||||
options(digits=3) # 3 significant digits as default
|
|
||||||
library(R2jags) # Interface with JAGS
|
|
||||||
runif(1) # sets random seed
|
|
||||||
|
|
||||||
#### Read in data
|
|
||||||
DataFile = "RF_LWR2.csv" # RF_LWR4 was extracted from FishBase in June 2013
|
|
||||||
Data = read.csv(DataFile, header=TRUE)
|
|
||||||
cat("Start", date(), "\n")
|
|
||||||
cat("Data file =", DataFile, "\n")
|
|
||||||
# Get unique, sorted list of Families
|
|
||||||
Fam.All <- sort(unique(as.character(Data$Family)))
|
|
||||||
Families <- Fam.All[Fam.All== "Acanthuridae" | Fam.All == "Achiridae"]
|
|
||||||
|
|
||||||
OutFile = "LWR_Test1.csv"
|
|
||||||
JAGSFILE = "dmnorm_0.bug"
|
|
||||||
|
|
||||||
# Get unique, sorted list of body shapes
|
|
||||||
Bshape <- sort(unique(as.character(Data$BodyShapeI)))
|
|
||||||
|
|
||||||
#------------------------------------------
|
|
||||||
# Functions
|
|
||||||
#------------------------------------------
|
|
||||||
|
|
||||||
#---------------------------------------------------------
|
|
||||||
# Function to get the priors for the respective body shape
|
|
||||||
#---------------------------------------------------------
|
|
||||||
Get.BS.pr <- function(BS) {
|
|
||||||
### Assignment of priors based on available body shape information
|
|
||||||
# priors derived from 5150 LWR studies in FishBase 02/2013
|
|
||||||
|
|
||||||
if (BS == "eel-like") { # eel-like prior for log(a) and b
|
|
||||||
prior_mean_log10a = -2.99
|
|
||||||
prior_sd_log10a = 0.175
|
|
||||||
prior_tau_log10a = 1/prior_sd_log10a^2
|
|
||||||
prior_mean_b = 3.06
|
|
||||||
prior_sd_b = 0.0896
|
|
||||||
prior_tau_b = 1/prior_sd_b^2
|
|
||||||
} else
|
|
||||||
if (BS == "elongated") { # elongate prior for log(a) and b
|
|
||||||
prior_mean_log10a = -2.41
|
|
||||||
prior_sd_log10a = 0.171
|
|
||||||
prior_tau_log10a = 1/prior_sd_log10a^2
|
|
||||||
prior_mean_b = 3.12
|
|
||||||
prior_sd_b = 0.09
|
|
||||||
prior_tau_b = 1/prior_sd_b^2
|
|
||||||
} else
|
|
||||||
if (BS == "fusiform / normal") { # fusiform / normal prior for log(a) and b
|
|
||||||
prior_mean_log10a = -1.95
|
|
||||||
prior_sd_log10a = 0.173
|
|
||||||
prior_tau_log10a = 1/prior_sd_log10a^2
|
|
||||||
prior_mean_b = 3.04
|
|
||||||
prior_sd_b = 0.0857
|
|
||||||
prior_tau_b = 1/prior_sd_b^2
|
|
||||||
} else
|
|
||||||
if (BS == "short and / or deep") { # short and / or deep prior for log(a) and b
|
|
||||||
prior_mean_log10a = -1.7
|
|
||||||
prior_sd_log10a = 0.175
|
|
||||||
prior_tau_log10a = 1/prior_sd_log10a^2
|
|
||||||
prior_mean_b = 3.01
|
|
||||||
prior_sd_b = 0.0905
|
|
||||||
prior_tau_b = 1/prior_sd_b^2
|
|
||||||
} else
|
|
||||||
# priors across all shapes, used for missing or other BS
|
|
||||||
{
|
|
||||||
prior_mean_log10a = -2.0
|
|
||||||
prior_sd_log10a = 0.313
|
|
||||||
prior_tau_log10a = 1/prior_sd_log10a^2
|
|
||||||
prior_mean_b = 3.04
|
|
||||||
prior_sd_b = 0.119
|
|
||||||
prior_tau_b = 1/prior_sd_b^2
|
|
||||||
}
|
|
||||||
|
|
||||||
# Priors for measurement error (= sigma) based on 5150 studies
|
|
||||||
# given here as shape mu and rate r, for gamma distribution
|
|
||||||
SD_rObs_log10a = 6520
|
|
||||||
SD_muObs_log10a = 25076
|
|
||||||
SD_rObs_b = 6808
|
|
||||||
SD_muObs_b = 37001
|
|
||||||
# Priors for between species variability (= sigma) based on 5150 studies for 1821 species
|
|
||||||
SD_rGS_log10a = 1372
|
|
||||||
SD_muGS_log10a = 7933
|
|
||||||
SD_rGS_b = 572
|
|
||||||
SD_muGS_b = 6498
|
|
||||||
|
|
||||||
prior.list <- list(mean_log10a=prior_mean_log10a, sd_log10a=prior_sd_log10a,
|
|
||||||
tau_log10a=prior_tau_log10a, mean_b=prior_mean_b, sd_b=prior_sd_b,
|
|
||||||
tau_b=prior_tau_b, SD_rObs_log10a=SD_rObs_log10a, SD_muObs_log10a=SD_muObs_log10a,
|
|
||||||
SD_rObs_b=SD_rObs_b, SD_muObs_b=SD_muObs_b, SD_rGS_log10a=SD_rGS_log10a,
|
|
||||||
SD_muGS_log10a=SD_muGS_log10a, SD_rGS_b=SD_rGS_b, SD_muGS_b=SD_muGS_b)
|
|
||||||
return(prior.list)
|
|
||||||
}
|
|
||||||
|
|
||||||
#--------------------------------------------------------------------
|
|
||||||
# Function to do a Bayesian analysis including LWR from relatives
|
|
||||||
#--------------------------------------------------------------------
|
|
||||||
SpecRelLWR <- function(a, b, wts, GenusSpecies, Nspecies, prior_mean_b, prior_tau_b,
|
|
||||||
prior_mean_log10a, prior_tau_log10a, SD_rObs_log10a, SD_muObs_log10a,
|
|
||||||
SD_rObs_b, SD_muObs_b, SD_rGS_log10a, SD_muGS_log10a,
|
|
||||||
SD_rGS_b, SD_muGS_b){
|
|
||||||
### Define JAGS model
|
|
||||||
Model = "
|
|
||||||
model {
|
|
||||||
#### Process model -- effects of taxonomy
|
|
||||||
# given the likelihood distributions and the priors,
|
|
||||||
# create normal posterior distributions for log10a, b,
|
|
||||||
# and for the process error (=between species variability sigmaGS)
|
|
||||||
|
|
||||||
abTrue[1] ~ dnorm(prior_mean_log10a,prior_tau_log10a)
|
|
||||||
abTrue[2] ~ dnorm(prior_mean_b,prior_tau_b)
|
|
||||||
sigmaGSlog10a ~ dgamma( SD_rGS_log10a, SD_muGS_log10a)
|
|
||||||
sigmaGSb ~ dgamma( SD_rGS_b, SD_muGS_b)
|
|
||||||
|
|
||||||
# given the posterior distributions and the process errors,
|
|
||||||
# establish for every species the expected witin-species
|
|
||||||
# parameter distributions; no correlation roGS between species
|
|
||||||
|
|
||||||
roGS <- 0
|
|
||||||
tauGenusSpecies[1] <- pow(sigmaGSlog10a,-2)
|
|
||||||
tauGenusSpecies[2] <- pow(sigmaGSb,-2)
|
|
||||||
for(k in 1:Nspecies){
|
|
||||||
abGenusSpecies[k,1] ~ dnorm(abTrue[1],tauGenusSpecies[1])
|
|
||||||
abGenusSpecies[k,2] ~ dnorm(abTrue[2],tauGenusSpecies[2])
|
|
||||||
}
|
|
||||||
|
|
||||||
### Observation model
|
|
||||||
## Errors
|
|
||||||
# given the data and the priors, establish distributions
|
|
||||||
# for the observation errors sigmaObs
|
|
||||||
|
|
||||||
sigmaObslog10a ~ dgamma( SD_rObs_log10a, SD_muObs_log10a)
|
|
||||||
sigmaObsb ~ dgamma( SD_rObs_b, SD_muObs_b)
|
|
||||||
|
|
||||||
# create inverse covariance matrix, with negative parameter correlation roObs
|
|
||||||
roObs ~ dunif(-0.99,0)
|
|
||||||
CovObs[1,1] <- pow(sigmaObslog10a,2)
|
|
||||||
CovObs[2,2] <- pow(sigmaObsb,2)
|
|
||||||
CovObs[1,2] <- roObs * sigmaObslog10a * sigmaObsb
|
|
||||||
CovObs[2,1] <- CovObs[1,2]
|
|
||||||
TauObs[1:2,1:2] <- inverse(CovObs[1:2,1:2])
|
|
||||||
|
|
||||||
## likelihood
|
|
||||||
# given the data, the priors and the covariance,
|
|
||||||
# create multivariate likelihood distributions for log10(a) and b
|
|
||||||
|
|
||||||
for(i in 1:N){
|
|
||||||
TauObsI[i,1:2,1:2] <- TauObs[1:2,1:2] * pow(Weights[i],2) # weighted precision
|
|
||||||
ab[i,1:2] ~ dmnorm(abGenusSpecies[GenusSpecies[i],1:2],TauObsI[i,1:2,1:2])
|
|
||||||
}
|
|
||||||
}
|
|
||||||
"
|
|
||||||
|
|
||||||
# Write JAGS model
|
|
||||||
cat(Model, file=JAGSFILE)
|
|
||||||
# JAGS settings
|
|
||||||
Nchains = 3 # number of MCMC chains to be used in JAGS
|
|
||||||
Nburnin = 1e4 # number of burn-in iterations, to be discarded; 1e4 = 10000 iterations for burn-in
|
|
||||||
Niter = 3e4 # number of iterations after burn-in; 3e4 = 30000 iterations
|
|
||||||
Nthin = 1e1 # subset of iterations to be used for analysis; 1e1 = every 10th iteration
|
|
||||||
|
|
||||||
# Run JAGS: define data to be passed on in DataJags;
|
|
||||||
# determine parameters to be returned in Param2Save;
|
|
||||||
# call JAGS with function Jags()
|
|
||||||
DataJags = list(ab=cbind(log10(a),b), N=length(a), Weights=wts, Nspecies=Nspecies, GenusSpecies=GenusSpecies,
|
|
||||||
prior_mean_b=prior_mean_b, prior_tau_b=prior_tau_b,
|
|
||||||
prior_mean_log10a=prior_mean_log10a, prior_tau_log10a=prior_tau_log10a,
|
|
||||||
SD_rObs_log10a=SD_rObs_log10a, SD_muObs_log10a=SD_muObs_log10a,
|
|
||||||
SD_rObs_b=SD_rObs_b, SD_muObs_b=SD_muObs_b,
|
|
||||||
SD_rGS_log10a=SD_rGS_log10a, SD_muGS_log10a=SD_muGS_log10a,
|
|
||||||
SD_rGS_b=SD_rGS_b, SD_muGS_b=SD_muGS_b)
|
|
||||||
Params2Save = c("abTrue","abGenusSpecies","sigmaGSlog10a","sigmaGSb","sigmaObslog10a","sigmaObsb","roObs")
|
|
||||||
Jags <- jags(inits=NULL, model.file=JAGSFILE, working.directory=NULL, data=DataJags,
|
|
||||||
parameters.to.save=Params2Save, n.chains=Nchains, n.thin=Nthin, n.iter=Niter, n.burnin=Nburnin)
|
|
||||||
Jags$BUGSoutput # contains the results from the JAGS run
|
|
||||||
|
|
||||||
# Analyze output for the relatives
|
|
||||||
abTrue <- Jags$BUGSoutput$sims.list$abTrue
|
|
||||||
R_mean_log10a <- mean(abTrue[,1]) # true mean of log10(a)
|
|
||||||
R_sd_log10a <- sd(abTrue[,1]) # true SE of log10(a)
|
|
||||||
R_mean_b <- mean(abTrue[,2]) # true mean of b
|
|
||||||
R_sd_b <- sd(abTrue[,2]) # true SE of b
|
|
||||||
|
|
||||||
# Analyze output for the target species
|
|
||||||
abGenusSpecies <- Jags$BUGSoutput$sims.list$abGenusSpecies
|
|
||||||
mean_log10a <- mean(abGenusSpecies[,1,1]) # true mean of log10(a) for the first species= target species
|
|
||||||
sd_log10a <- sd(abGenusSpecies[,1,1]) # true SE of log10(a)
|
|
||||||
mean_b <- mean(abGenusSpecies[,1,2]) # true mean of b
|
|
||||||
sd_b <- sd(abGenusSpecies[,1,2]) # true SE of b
|
|
||||||
mean_sigma_log10a <- mean(Jags$BUGSoutput$sims.list$sigmaObslog10a) # measurement error of log10(a)
|
|
||||||
sd_sigma_log10a <- apply(as.matrix(Jags$BUGSoutput$sims.list$sigmaObslog10a), 2, sd)
|
|
||||||
mean_sigma_b <- mean(Jags$BUGSoutput$sims.list$sigmaObsb) # measurement error of b
|
|
||||||
sd_sigma_b <- apply(as.matrix(Jags$BUGSoutput$sims.list$sigmaObsb), 2, sd)
|
|
||||||
ro_ab <- mean(Jags$BUGSoutput$sims.list$roObs) # measurement correlation of log10(a),b
|
|
||||||
|
|
||||||
out.list <- list(N=length(a), mean_log10a=mean_log10a, sd_log10a=sd_log10a, mean_b=mean_b, sd_b=sd_b,
|
|
||||||
R_mean_log10a=R_mean_log10a, R_sd_log10a=R_sd_log10a, R_mean_b=R_mean_b, R_sd_b=R_sd_b)
|
|
||||||
return(out.list)
|
|
||||||
}
|
|
||||||
|
|
||||||
#-----------------------------------------------------------------------------
|
|
||||||
# Function to do a Bayesian LWR analysis with studies for target species only
|
|
||||||
#-----------------------------------------------------------------------------
|
|
||||||
SpecLWR <- function(a, b, wts, prior_mean_b, prior_tau_b,
|
|
||||||
prior_mean_log10a, prior_tau_log10a, SD_rObs_log10a, SD_muObs_log10a,
|
|
||||||
SD_rObs_b, SD_muObs_b, SD_rGS_log10a, SD_muGS_log10a,
|
|
||||||
SD_rGS_b, SD_muGS_b){
|
|
||||||
|
|
||||||
# Define JAGS model
|
|
||||||
Model = "
|
|
||||||
model {
|
|
||||||
sigma1 ~ dgamma( SD_rObs_log10a, SD_muObs_log10a) # posterior distribution for measurement error in log10a
|
|
||||||
sigma2 ~ dgamma( SD_rObs_b, SD_muObs_b) # posterior distribution for measurement error in log10a
|
|
||||||
|
|
||||||
ro ~ dunif(-0.99,0) # uniform prior for negative correlation between log10a and b
|
|
||||||
abTrue[1] ~ dnorm(prior_mean_log10a,prior_tau_log10a) # normal posterior distribution for log10a
|
|
||||||
abTrue[2] ~ dnorm(prior_mean_b,prior_tau_b) # normal posterior distribution for b
|
|
||||||
CovObs[1,1] <- pow(sigma1,2)
|
|
||||||
CovObs[2,2] <- pow(sigma2,2)
|
|
||||||
CovObs[1,2] <- ro * sigma1 * sigma2
|
|
||||||
CovObs[2,1] <- CovObs[1,2]
|
|
||||||
TauObs[1:2,1:2] <- inverse(CovObs[1:2,1:2]) # create inverse covariance matrix
|
|
||||||
for(i in 1:N){
|
|
||||||
TauObsI[i,1:2,1:2] <- TauObs[1:2,1:2] * pow(Weights[i],2) # converts prior SD into prior weighted precision
|
|
||||||
|
|
||||||
# given the data, the priors and the covariance, create multivariate normal posteriors for log(a) and b
|
|
||||||
ab[i,1:2] ~ dmnorm(abTrue[1:2],TauObsI[i,1:2,1:2])
|
|
||||||
}
|
|
||||||
}
|
|
||||||
"
|
|
||||||
|
|
||||||
# Write JAGS model
|
|
||||||
cat(Model, file=JAGSFILE)
|
|
||||||
# JAGS settings
|
|
||||||
Nchains = 3 # number of MCMC chains to be used in JAGS
|
|
||||||
Nburnin = 1e4 # number of burn-in runs, to be discarded; 10000 iterations for burn-in
|
|
||||||
Niter = 3e4 # number of iterations after burn-in; 3e4 = 30000 iterations
|
|
||||||
Nthin = 1e1 # subset of iterations to be used for analysis; 1e1 = every 10th iteration
|
|
||||||
# Run JAGS: define data to be passed on in DataJags; determine parameters to be returned in Param2Save; call JAGS with function Jags()
|
|
||||||
DataJags = list(ab=cbind(log10(a),b), N=length(a), Weights=wts, prior_mean_b=prior_mean_b,
|
|
||||||
prior_tau_b=prior_tau_b, prior_mean_log10a=prior_mean_log10a, prior_tau_log10a=prior_tau_log10a,
|
|
||||||
SD_rObs_log10a=SD_rObs_log10a, SD_muObs_log10a=SD_muObs_log10a,
|
|
||||||
SD_rObs_b=SD_rObs_b, SD_muObs_b=SD_muObs_b)
|
|
||||||
Params2Save = c("abTrue","sigma1","sigma2","ro")
|
|
||||||
Jags <- jags(inits=NULL, model.file=JAGSFILE, working.directory=NULL, data=DataJags, parameters.to.save=Params2Save, n.chains=Nchains, n.thin=Nthin, n.iter=Niter, n.burnin=Nburnin)
|
|
||||||
Jags$BUGSoutput # contains the results from the JAGS run
|
|
||||||
# Analyze output
|
|
||||||
abTrue <- Jags$BUGSoutput$sims.list$abTrue
|
|
||||||
mean_log10a <- mean(abTrue[,1]) # true mean of log10(a)
|
|
||||||
sd_log10a <- sd(abTrue[,1]) # true SE of log10(a)
|
|
||||||
mean_b <- mean(abTrue[,2]) # true mean of b
|
|
||||||
sd_b <- sd(abTrue[,2]) # true SE of b
|
|
||||||
mean_sigma_log10a <- mean(Jags$BUGSoutput$sims.list$sigma1) # measurement error of log10(a)
|
|
||||||
sd_sigma_log10a <- apply(as.matrix(Jags$BUGSoutput$sims.list$sigma1), 2, sd)
|
|
||||||
mean_sigma_b <- mean(Jags$BUGSoutput$sims.list$sigma2) # measurement error of b
|
|
||||||
sd_sigma_b <- apply(as.matrix(Jags$BUGSoutput$sims.list$sigma2), 2, sd)
|
|
||||||
ro_ab <- mean(Jags$BUGSoutput$sims.list$ro) # measurement correlation of log10(a),b
|
|
||||||
|
|
||||||
out.list <- list(N=length(a), mean_log10a=mean_log10a, sd_log10a=sd_log10a, mean_b=mean_b, sd_b=sd_b)
|
|
||||||
return(out.list)
|
|
||||||
|
|
||||||
} # End of Functions section
|
|
||||||
|
|
||||||
#--------------------------------
|
|
||||||
# Analysis by Family
|
|
||||||
#--------------------------------
|
|
||||||
# Do LWR analysis by Family, Subfamily and Body shape, depending on available LWR studies
|
|
||||||
# for(Fam in "Acanthuridae") {
|
|
||||||
for(Fam in Families) {
|
|
||||||
Subfamilies <- sort(unique(Data$Subfamily[Data$Family==Fam]))
|
|
||||||
for(SF in Subfamilies) {
|
|
||||||
for(BS in Bshape) {
|
|
||||||
# get species (SpecCodes) in this Subfamily and with this body shape
|
|
||||||
SpecCode.SF.BS <- unique(Data$SpecCode[Data$Family==Fam & Data$Subfamily==SF & Data$BodyShapeI==BS])
|
|
||||||
# if there are species with this body shape
|
|
||||||
if(length(SpecCode.SF.BS > 0)) {
|
|
||||||
# get priors for this body shape
|
|
||||||
prior <- Get.BS.pr(BS)
|
|
||||||
# get LWR data for this body shape
|
|
||||||
b_raw <- Data$b[Data$Family==Fam & Data$Subfamily==SF & Data$BodyShapeI==BS]
|
|
||||||
cat("\n")
|
|
||||||
cat("Family =", Fam, ", Subfamily =", SF, ", Body shape =", BS, ", Species =", length(SpecCode.SF.BS), ", LWR =",
|
|
||||||
length(b_raw[is.na(b_raw)==F]), "\n")
|
|
||||||
# if no LWR studies exist for this body shape, assign the respective priors to all species
|
|
||||||
if(length(b_raw[is.na(b_raw)==F])==0) {
|
|
||||||
# assign priors to species with no LWR in this Subfamily with this body shape
|
|
||||||
cat("Assigning overall body shape prior to", length(SpecCode.SF.BS), " species \n")
|
|
||||||
for(SpC in SpecCode.SF.BS) {
|
|
||||||
out.prior <- data.frame(Fam, SF, BS, SpC, 0, prior$mean_log10a, prior$sd_log10a, prior$mean_b, prior$sd_b,
|
|
||||||
paste("all LWR estimates for this BS"))
|
|
||||||
write.table(out.prior, file=OutFile, append = T, sep=",", dec=".", row.names=F, col.names=F)
|
|
||||||
}
|
|
||||||
} else {
|
|
||||||
|
|
||||||
# Update priors for this body shape using existing LWR studies
|
|
||||||
# get LWR data for this Subfamily and body shape
|
|
||||||
Keep <- which(Data$Family==Fam & Data$Subfamily==SF & Data$BodyShapeI==BS & is.na(Data$b)==F & Data$Score>0)
|
|
||||||
wts <- Data$Score[Keep] # Un-normalized weights (so that Cov is comparable among analyses)
|
|
||||||
a <- Data$a[Keep]
|
|
||||||
b <- Data$b[Keep]
|
|
||||||
GenSpec <- paste(Data$Genus[Keep],Data$Species[Keep])
|
|
||||||
# add a first dummy record with prior LWR and low score = 0.3, as pseudo target species
|
|
||||||
# Name of dummy target species is Dum1 dum1
|
|
||||||
TargetSpec = paste("Dum1", "dum1")
|
|
||||||
wts <- c(0.3, wts)
|
|
||||||
a <- c(10^(prior$mean_log10a), a)
|
|
||||||
b <- c(prior$mean_b, b)
|
|
||||||
GenSpec <- c(TargetSpec, GenSpec)
|
|
||||||
# Relabel GenSpec so that TargetSpec = level 1
|
|
||||||
OtherSpecies = unique(GenSpec[GenSpec != TargetSpec])
|
|
||||||
GenusSpecies = factor(GenSpec, levels=c(TargetSpec, OtherSpecies))
|
|
||||||
Nspecies = nlevels(GenusSpecies) # number of species
|
|
||||||
# run Bayesian analysis for pseudo target species with Subfamily members
|
|
||||||
# The resulting R_mean_log10a, R_sd_log10a, R_mean_b, R_sd_b will be used for species without LWR
|
|
||||||
cat("Updating Subfamily-Bodyshape prior using", Nspecies-1, "species with LWR studies \n")
|
|
||||||
prior.SFam.BS <- SpecRelLWR(a, b, wts, GenusSpecies, Nspecies, prior_mean_b=prior$mean_b,
|
|
||||||
prior_tau_b=prior$tau_b, prior_mean_log10a=prior$mean_log10a,
|
|
||||||
prior_tau_log10a=prior$tau_log10a, SD_rObs_log10a=prior$SD_rObs_log10a,
|
|
||||||
SD_muObs_log10a=prior$SD_muObs_log10a, SD_rObs_b=prior$SD_rObs_b,
|
|
||||||
SD_muObs_b=prior$SD_muObs_b, SD_rGS_log10a=prior$SD_rGS_log10a,
|
|
||||||
SD_muGS_log10a=prior$SD_muGS_log10a, SD_rGS_b=prior$SD_rGS_b,
|
|
||||||
SD_muGS_b=prior$SD_muGS_b)
|
|
||||||
|
|
||||||
#------------------------------------------------------------------------------------------
|
|
||||||
# if there are Genera with >= 5 species with LWR, update body shape priors for these Genera
|
|
||||||
#------------------------------------------------------------------------------------------
|
|
||||||
Genera <- unique(as.character(Data$Genus[Keep]))
|
|
||||||
# create empty list of lists for storage of generic priors
|
|
||||||
prior.Gen.BS <- rep(list(list()),length(Genera)) # create a list of empty lists
|
|
||||||
names(prior.Gen.BS) <- Genera # name the list elements according to the Genera
|
|
||||||
for(Genus in Genera){
|
|
||||||
# check if Genus contains >= 5 species with LWR data
|
|
||||||
if(length(unique(Data$SpecCode[Data$Family==Fam & Data$Subfamily==SF & Data$BodyShapeI==BS & is.na(Data$b)==F &
|
|
||||||
Data$Score>0 & Data$Genus==Genus]))>=5) {
|
|
||||||
# run Subfamily analysis with only data for this genus
|
|
||||||
Keep <- which(Data$Family==Fam & Data$Subfamily==SF & Data$BodyShapeI==BS & is.na(Data$b)==F & Data$Score>0 &
|
|
||||||
Data$Genus==Genus)
|
|
||||||
wts <- Data$Score[Keep] # Un-normalized weights (so that Cov is comparable among analyses)
|
|
||||||
a <- Data$a[Keep]
|
|
||||||
b <- Data$b[Keep]
|
|
||||||
GenSpec <- paste(Data$Genus[Keep],Data$Species[Keep])
|
|
||||||
# add a first dummy record with prior LWR and low score = 0.3, as pseudo target species
|
|
||||||
# Name of dummy target species is Dum1 dum1
|
|
||||||
TargetSpec = paste("Dum1", "dum1")
|
|
||||||
wts <- c(0.3, wts)
|
|
||||||
a <- c(10^(prior$mean_log10a), a)
|
|
||||||
b <- c(prior$mean_b, b)
|
|
||||||
GenSpec <- c(TargetSpec, GenSpec)
|
|
||||||
# Relabel GenSpec so that TargetSpec = level 1
|
|
||||||
OtherSpecies = unique(GenSpec[GenSpec != TargetSpec])
|
|
||||||
GenusSpecies = factor(GenSpec, levels=c(TargetSpec, OtherSpecies))
|
|
||||||
Nspecies = nlevels(GenusSpecies) # number of species
|
|
||||||
# run Bayesian analysis for pseudo target species with Genus members
|
|
||||||
# R_mean_log10a, R_sd_log10a, R_mean_b, R_sd_b will be used for species without LWR
|
|
||||||
cat("Updating prior for Genus =", Genus, ", with", Nspecies -1, "LWR Species \n")
|
|
||||||
prior.Gen.BS[[Genus]] <- SpecRelLWR(a, b, wts, GenusSpecies, Nspecies,
|
|
||||||
prior_mean_b=prior.SFam.BS$R_mean_b,
|
|
||||||
prior_tau_b=1/prior.SFam.BS$R_sd_b^2,
|
|
||||||
prior_mean_log10a=prior.SFam.BS$R_mean_log10a,
|
|
||||||
prior_tau_log10a=1/prior.SFam.BS$R_sd_log10a,
|
|
||||||
SD_rObs_log10a=prior$SD_rObs_log10a,
|
|
||||||
SD_muObs_log10a=prior$SD_muObs_log10a, SD_rObs_b=prior$SD_rObs_b,
|
|
||||||
SD_muObs_b=prior$SD_muObs_b, SD_rGS_log10a=prior$SD_rGS_log10a,
|
|
||||||
SD_muGS_log10a=prior$SD_muGS_log10a, SD_rGS_b=prior$SD_rGS_b,
|
|
||||||
SD_muGS_b=prior$SD_muGS_b)
|
|
||||||
}
|
|
||||||
}
|
|
||||||
# new Subfamily-BS priors have been generated
|
|
||||||
# for some genera, new Genus-BS priors have been generated
|
|
||||||
# ---------------------------------------------------------------------
|
|
||||||
# Loop through all species in this Subfamily-BS; assign LWR as appropriate
|
|
||||||
# ---------------------------------------------------------------------
|
|
||||||
for(SpC in SpecCode.SF.BS) {
|
|
||||||
Genus <- as.character(unique(Data$Genus[Data$SpecCode==SpC]))
|
|
||||||
Species <- as.character(unique(Data$Species[Data$SpecCode==SpC]))
|
|
||||||
TargetSpec = paste(Genus, Species)
|
|
||||||
LWR <- length(Data$b[Data$SpecCode==SpC & is.na(Data$b)==F & Data$Score>0])
|
|
||||||
LWRGenspec <- length(unique(Data$SpecCode[Data$BodyShapeI==BS & is.na(Data$b)==F &
|
|
||||||
Data$Score>0 & Data$Genus==Genus]))
|
|
||||||
LWRSFamspec <- length(unique(Data$SpecCode[Data$BodyShapeI==BS & is.na(Data$b)==F &
|
|
||||||
Data$Score>0 & Data$Family==Fam & Data$Subfamily==SF]))
|
|
||||||
#---------------------------------------------------------
|
|
||||||
# >= 5 LWR in target species, run single species analysis
|
|
||||||
if(LWR >= 5) {
|
|
||||||
# Run analysis with data only for this species
|
|
||||||
Keep <- which(Data$SpecCode==SpC & is.na(Data$b)==F & Data$Score>0)
|
|
||||||
wts = Data$Score[Keep] # Un-normalized weights (so that Cov is comparable among analyses)
|
|
||||||
a = Data$a[Keep]
|
|
||||||
b = Data$b[Keep]
|
|
||||||
|
|
||||||
# determine priors to be used
|
|
||||||
if(LWRGenspec >= 5) {
|
|
||||||
prior_mean_b=prior.Gen.BS[[Genus]]$R_mean_b
|
|
||||||
prior_tau_b=1/prior.Gen.BS[[Genus]]$R_sd_b^2
|
|
||||||
prior_mean_log10a=prior.Gen.BS[[Genus]]$R_mean_log10a
|
|
||||||
prior_tau_log10a=1/prior.Gen.BS[[Genus]]$R_sd_log10a^2
|
|
||||||
} else
|
|
||||||
if (LWRSFamspec > 0) {
|
|
||||||
prior_mean_b=prior.SFam.BS$R_mean_b
|
|
||||||
prior_tau_b=1/prior.SFam.BS$R_sd_b^2
|
|
||||||
prior_mean_log10a=prior.SFam.BS$R_mean_log10a
|
|
||||||
prior_tau_log10a=1/prior.SFam.BS$R_sd_log10a^2
|
|
||||||
} else {
|
|
||||||
prior_mean_b=prior$mean_b
|
|
||||||
prior_tau_b=prior$tau_b
|
|
||||||
prior_mean_log10a=prior$mean_log10a
|
|
||||||
prior_tau_log10a=prior$tau_log10a
|
|
||||||
}
|
|
||||||
cat("Running single species analysis for", TargetSpec, "LWR =", LWR, ", LWR species in Genus=",LWRGenspec,"\n" )
|
|
||||||
# call function for single species analysis
|
|
||||||
post <- SpecLWR(a, b, wts, prior_mean_b=prior_mean_b,
|
|
||||||
prior_tau_b=prior_tau_b, prior_mean_log10a=prior_mean_log10a,
|
|
||||||
prior_tau_log10a=prior_tau_log10a, SD_rObs_log10a=prior$SD_rObs_log10a,
|
|
||||||
SD_muObs_log10a=prior$SD_muObs_log10a, SD_rObs_b=prior$SD_rObs_b,
|
|
||||||
SD_muObs_b=prior$SD_muObs_b, SD_rGS_log10a=prior$SD_rGS_log10a,
|
|
||||||
SD_muGS_log10a=prior$SD_muGS_log10a, SD_rGS_b=prior$SD_rGS_b,
|
|
||||||
SD_muGS_b=prior$SD_muGS_b)
|
|
||||||
out.SpC <- data.frame(Fam, SF, BS, SpC, LWR, format(post$mean_log10a, digits=3), format(post$sd_log10a, digits=3), format(post$mean_b, disgits=3), format(post$sd_b, digits=3),
|
|
||||||
paste("LWR estimates for this species"))
|
|
||||||
write.table(out.SpC, file=OutFile, append = T, sep=",", dec=".", row.names=F, col.names=F)
|
|
||||||
|
|
||||||
} else
|
|
||||||
#--------------------------------------------------------
|
|
||||||
# 1-4 LWR in target species and >= 5 LWR species in Genus
|
|
||||||
# run hierarchical analysis for genus members, with Subfamily-BS prior
|
|
||||||
if(LWR >= 1 & LWRGenspec >=5) {
|
|
||||||
# run Subfamily analysis with only data for this genus
|
|
||||||
Keep <- which(Data$Family==Fam & Data$Subfamily==SF & Data$BodyShapeI==BS & is.na(Data$b)==F & Data$Score>0 &
|
|
||||||
Data$Genus==Genus)
|
|
||||||
wts <- Data$Score[Keep] # Un-normalized weights (so that Cov is comparable among analyses)
|
|
||||||
a <- Data$a[Keep]
|
|
||||||
b <- Data$b[Keep]
|
|
||||||
GenSpec <- paste(Data$Genus[Keep],Data$Species[Keep])
|
|
||||||
|
|
||||||
# Relabel GenSpec so that TargetSpec = level 1
|
|
||||||
OtherSpecies = unique(GenSpec[GenSpec != TargetSpec])
|
|
||||||
GenusSpecies = factor(GenSpec, levels=c(TargetSpec, OtherSpecies))
|
|
||||||
Nspecies = nlevels(GenusSpecies) # number of species
|
|
||||||
# run Bayesian analysis for target species with Genus members
|
|
||||||
cat("Running analysis with congeners for", TargetSpec, ", LWR =", LWR,", LWR species in Genus =", LWRGenspec,"\n")
|
|
||||||
post <- SpecRelLWR(a, b, wts, GenusSpecies, Nspecies,
|
|
||||||
prior_mean_b=prior.SFam.BS$R_mean_b,
|
|
||||||
prior_tau_b=1/prior.SFam.BS$R_sd_b^2,
|
|
||||||
prior_mean_log10a=prior.SFam.BS$R_mean_log10a,
|
|
||||||
prior_tau_log10a=1/prior.SFam.BS$R_sd_log10a^2,
|
|
||||||
SD_rObs_log10a=prior$SD_rObs_log10a,
|
|
||||||
SD_muObs_log10a=prior$SD_muObs_log10a, SD_rObs_b=prior$SD_rObs_b,
|
|
||||||
SD_muObs_b=prior$SD_muObs_b, SD_rGS_log10a=prior$SD_rGS_log10a,
|
|
||||||
SD_muGS_log10a=prior$SD_muGS_log10a, SD_rGS_b=prior$SD_rGS_b,
|
|
||||||
SD_muGS_b=prior$SD_muGS_b)
|
|
||||||
out.SpC <- data.frame(Fam, SF, BS, SpC, LWR, format(post$mean_log10a, digits=3), format(post$sd_log10a, digits=3), format(post$mean_b, disgits=3), format(post$sd_b, digits=3),
|
|
||||||
paste("LWR estimates for species & Genus-BS"))
|
|
||||||
write.table(out.SpC, file=OutFile, append = T, sep=",", dec=".", row.names=F, col.names=F)
|
|
||||||
} else
|
|
||||||
|
|
||||||
#-------------------------------------------------------
|
|
||||||
# 1-4 LWR in target species and < 5 LWR species in Genus
|
|
||||||
# run hierarchical analysis for Subfamily members, with bodyshape prior
|
|
||||||
|
|
||||||
if(LWR >= 1 & LWRSFamspec > 1) {
|
|
||||||
# run Subfamily analysis
|
|
||||||
Keep <- which(Data$Family==Fam & Data$Subfamily==SF & Data$BodyShapeI==BS & is.na(Data$b)==F & Data$Score>0)
|
|
||||||
wts <- Data$Score[Keep] # Un-normalized weights (so that Cov is comparable among analyses)
|
|
||||||
a <- Data$a[Keep]
|
|
||||||
b <- Data$b[Keep]
|
|
||||||
GenSpec <- paste(Data$Genus[Keep],Data$Species[Keep])
|
|
||||||
# Relabel GenSpec so that TargetSpec = level 1
|
|
||||||
OtherSpecies = unique(GenSpec[GenSpec != TargetSpec])
|
|
||||||
GenusSpecies = factor(GenSpec, levels=c(TargetSpec, OtherSpecies))
|
|
||||||
Nspecies = nlevels(GenusSpecies) # number of species
|
|
||||||
# run Bayesian analysis for target species with Subfamily members
|
|
||||||
cat("Running analysis with Subfamily members for", TargetSpec, ", LWR =", LWR,", LWR species in Subfamily-BS =",
|
|
||||||
LWRSFamspec, "\n")
|
|
||||||
post <- SpecRelLWR(a, b, wts, GenusSpecies, Nspecies,
|
|
||||||
prior_mean_b=prior$mean_b,
|
|
||||||
prior_tau_b=prior$tau_b,
|
|
||||||
prior_mean_log10a=prior$mean_log10a,
|
|
||||||
prior_tau_log10a=prior$tau_log10a,
|
|
||||||
SD_rObs_log10a=prior$SD_rObs_log10a,
|
|
||||||
SD_muObs_log10a=prior$SD_muObs_log10a, SD_rObs_b=prior$SD_rObs_b,
|
|
||||||
SD_muObs_b=prior$SD_muObs_b, SD_rGS_log10a=prior$SD_rGS_log10a,
|
|
||||||
SD_muGS_log10a=prior$SD_muGS_log10a, SD_rGS_b=prior$SD_rGS_b,
|
|
||||||
SD_muGS_b=prior$SD_muGS_b)
|
|
||||||
out.SpC <- data.frame(Fam, SF, BS, SpC, LWR, format(post$mean_log10a, digits=3), format(post$sd_log10a, digits=3),
|
|
||||||
format(post$mean_b, disgits=3), format(post$sd_b, digits=3),
|
|
||||||
paste("LWR estimates for species & Subfamily-BS"))
|
|
||||||
write.table(out.SpC, file=OutFile, append = T, sep=",", dec=".", row.names=F, col.names=F)
|
|
||||||
} else
|
|
||||||
#--------------------------------------------------
|
|
||||||
# assign Genus-BS priors to target species
|
|
||||||
if(LWRGenspec >= 5) {
|
|
||||||
cat("Assign Genus-BS prior for", TargetSpec, "\n")
|
|
||||||
out.SpC <- data.frame(Fam, SF, BS, SpC, LWR, format(prior.Gen.BS[[Genus]]$mean_log10a, digits=3),
|
|
||||||
format(prior.Gen.BS[[Genus]]$sd_log10a, digits=3),
|
|
||||||
format(prior.Gen.BS[[Genus]]$mean_b, digits=3), format(prior.Gen.BS[[Genus]]$sd_b, digits=3),
|
|
||||||
paste("LWR estimates for this Genus-BS"))
|
|
||||||
write.table(out.SpC, file=OutFile, append = T, sep=",", dec=".", row.names=F, col.names=F)
|
|
||||||
} else {
|
|
||||||
# -----------------------------------------------
|
|
||||||
# assign Subfamily-BS priors to target species
|
|
||||||
cat("Assign Subfamily-BS prior for", TargetSpec,"\n")
|
|
||||||
out.SpC <- data.frame(Fam, SF, BS, SpC, LWR, format(prior.SFam.BS$mean_log10a, digits=3), format(prior.SFam.BS$sd_log10a, digits=3),
|
|
||||||
format(prior.SFam.BS$mean_b, digits=3), format(prior.SFam.BS$sd_b, digits=3), paste("LWR estimates for this Subfamily-BS"))
|
|
||||||
write.table(out.SpC, file=OutFile, append = T, sep=",", dec=".", row.names=F, col.names=F)
|
|
||||||
}
|
|
||||||
} # end of species loop for this Subfamily and body shape
|
|
||||||
|
|
||||||
} # end of section dealing with Subfamily - body shapes that contain LWR estimates
|
|
||||||
|
|
||||||
} # end of section that deals with Subfamily - body shapes that contain species
|
|
||||||
|
|
||||||
} # end of body shape section
|
|
||||||
|
|
||||||
} # end of Subfamily section
|
|
||||||
|
|
||||||
} # end of Family section
|
|
||||||
cat("End", date(),"\n")
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
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Loading…
Reference in New Issue