Difference between revisions of "POPs in Baltic salmon"
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== Rationale == | == Rationale == | ||
− | <rcode graphics=1> | + | This model takes in measured congener concentrations of POPs in Baltic salmon in northern part of Baltic sea (Bothnian Bay, Bothnian Sea, Åland Sea, Gulf of Finland). Measured data is used for Bayesian model to produce posterior medians and sds for each congener and also to calculate TEQ values. Numerical results are saved as variables to Opasnetbase and result figures are presented above in the Answer section. |
+ | |||
+ | <rcode name="pop_bayes" label="Calculate (for developers only)" graphics=1 store=1> | ||
+ | ## This code is Op_en????/pop_bayes on page [[POPs_in_Baltic_salmon]] | ||
+ | |||
library(OpasnetUtils) | library(OpasnetUtils) | ||
library(ggplot2) | library(ggplot2) | ||
library(rjags) | library(rjags) | ||
+ | library(reshape2) | ||
dat <- opbase.data("Op_en3104", subset = "POPs") | dat <- opbase.data("Op_en3104", subset = "POPs") | ||
dat <- dat[dat$Fish_species == "Salmon" , ] | dat <- dat[dat$Fish_species == "Salmon" , ] | ||
+ | |||
+ | dat <- subset(dat,!(is.na(dat["Result"]))) | ||
+ | dat <- dropall(dat) | ||
+ | levels(dat$POP) <- gsub("HCDD", "HxCDD", levels(dat$POP)) | ||
+ | levels(dat$POP) <- gsub("HCDF", "HxCDF", levels(dat$POP)) | ||
+ | levels(dat$POP) <- gsub("CoPCB", "PCB", levels(dat$POP)) | ||
congeners <- levels(dat$POP) #names of different congeners in data | congeners <- levels(dat$POP) #names of different congeners in data | ||
Line 26: | Line 37: | ||
Y <- length(congeners) #number of congeners and j's in for loop | Y <- length(congeners) #number of congeners and j's in for loop | ||
− | + | compdat <- dat[dat$POP %in% congeners[1:Y] , ] #data for current congener | |
− | + | Compound <- log10(compdat$Result+1E-2) #+1E-2 because zero concentrations are not allowed | |
− | compdat <- dat[dat$POP %in% congeners[1: | ||
− | Compound <- | ||
mo <- textConnection("model{ | mo <- textConnection("model{ | ||
− | + | for (j in 1 : Y ) { | |
− | + | tau1[j] ~ dunif(0.001, 1000) | |
− | + | muOfCompound[j] ~ dnorm(0, 0.001) | |
− | + | } | |
− | + | for( i in 1 : N ) { | |
− | + | Compound[i] ~ dnorm(muOfCompound[POP[i]], tau2[i]) | |
− | + | tau2[i] <- tau1[POP[i]]*sqrt(n[i]) | |
− | + | } | |
− | + | } | |
− | ") | + | ") |
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dataList = list( | dataList = list( | ||
+ | Y = Y, | ||
Compound = Compound, | Compound = Compound, | ||
POP = as.numeric(compdat$POP), | POP = as.numeric(compdat$POP), | ||
Line 71: | Line 67: | ||
plot(out) | plot(out) | ||
− | + | Meanlogpost = c() | |
− | + | for (j in 1 : Y) { | |
+ | logmean <- mean(out[[4]][,j]) #calculate mean of logmu for posterior (test 4) | ||
+ | Meanlogpost = c(Meanlogpost, logmean) | ||
+ | } | ||
+ | Sdlogpost = c() | ||
+ | for (j in 1 : Y) { | ||
+ | logsd <- sqrt(1/(mean(out[[4]][,j+Y]))) | ||
+ | Sdlogpost = c(Sdlogpost, logsd) | ||
+ | } | ||
resultsall <- data.frame( | resultsall <- data.frame( | ||
Meanorig = aggregate(compdat$Result, compdat["POP"], mean), #calculate mean for original data | Meanorig = aggregate(compdat$Result, compdat["POP"], mean), #calculate mean for original data | ||
Sdorig = aggregate(compdat$Result, compdat["POP"], sd)$x, #calcaulte sd of original data | Sdorig = aggregate(compdat$Result, compdat["POP"], sd)$x, #calcaulte sd of original data | ||
+ | Medianorig = aggregate(compdat$Result, compdat["POP"], median)$x, #calculate median of original data | ||
Meanlog = aggregate(Compound, compdat["POP"], mean)$x, #calculate mean for logdata | Meanlog = aggregate(Compound, compdat["POP"], mean)$x, #calculate mean for logdata | ||
Sdlog = aggregate(Compound, compdat["POP"], sd)$x, #calculate sd for logdata | Sdlog = aggregate(Compound, compdat["POP"], sd)$x, #calculate sd for logdata | ||
− | + | Meanlogpost = Meanlogpost, | |
− | + | Sdlogpost = Sdlogpost, | |
− | + | Medianpost = 10^Meanlogpost-1E-02, | |
− | + | Sdpost = 10^Sdlogpost-1E-02 #this might be incorrect | |
− | + | ) | |
− | |||
− | |||
− | |||
− | ) | ||
oprint(resultsall) | oprint(resultsall) | ||
− | + | tef <- Ovariable("tef", ddata = "Op_en4017", subset = "TEF values") | |
+ | tef <- EvalOutput(tef) | ||
+ | |||
+ | colnames(resultsall)[1] <- "Congener" | ||
+ | resultsall <- melt(resultsall) | ||
+ | colnames(resultsall)[3] <- "Result" | ||
+ | resultsall <- Ovariable("resultsall", data = resultsall) | ||
+ | resultsall <- EvalOutput(resultsall) | ||
+ | teq = resultsall * tef | ||
+ | |||
+ | objects.store(resultsall, teq) | ||
+ | cat("resultsall and teq stored for later use:\n", paste(ls(), collapse = ", "), "\n") | ||
</rcode> | </rcode> |
Revision as of 10:29, 30 June 2016
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Contents
Question
What are the levels of persistent organic pollutants (POPs) in Baltic sea salmon?
Answer
Answer is under work and results are preliminary.
POP concentrations in Baltic sea fish have been measured from samples collected in EU-kalat project. The original data of individual fish samples is accessible through Opasnet base. This data is used here for a Bayesian model to calculate posterior concentration distributions (median and SD) for each congener. This data is then translated into TEQ, and can be used for health benefit assessment of Baltic salmon.
Posterior congener median concentrations are presented below for each compound group (PCDD/F, PCB, BDE) analysed in EU-kalat.
Rationale
This model takes in measured congener concentrations of POPs in Baltic salmon in northern part of Baltic sea (Bothnian Bay, Bothnian Sea, Åland Sea, Gulf of Finland). Measured data is used for Bayesian model to produce posterior medians and sds for each congener and also to calculate TEQ values. Numerical results are saved as variables to Opasnetbase and result figures are presented above in the Answer section.
Rationale
Dependencies
Formula
See also
Keywords
References
Related files
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