Nothing
knitr::opts_chunk$set( collapse = TRUE, comment = "#>" )
library(rbioacc) # library(ggplot2)
Load the data set with the function data()
, define the duration of the exposure time_accumulation
, and check if the data set is correctly imported with the function modelData()
. Here the data set is called Male Gammarus Single
data("Male_Gammarus_Single")
The function fitTK()
performs the inference process.
modelData_MGS <- modelData(Male_Gammarus_Single, time_accumulation = 4) fit_MGS <- fitTK(modelData_MGS, iter = 10000)
The 4 MCMC are stored in the object fitMCMC
. The quantiles for each TK parameter can be obtained with the quantile()
function.
quantile_table(fit_MGS)
plot(fit_MGS)
ppc(fit_MGS)
data("Male_Gammarus_Merged") data_MGM708 <- Male_Gammarus_Merged[Male_Gammarus_Merged$expw == 7.08021e-05, ] modelData_MGM708 <- modelData(data_MGM708, time_accumulation = 4) fit_MGM708 <- fitTK(modelData_MGM708, iter = 10000)
quantile_table(fit_MGM708)
plot(fit_MGM708)
ppc(fit_MGM708)
data_MGM141 <- Male_Gammarus_Merged[Male_Gammarus_Merged$expw == 1.41604e-04, ] modelData_MGM141 <- modelData(data_MGM141, time_accumulation = 7) fit_MGM141 <- fitTK(modelData_MGM141, iter = 20000)
quantile_table(fit_MGM141)
plot(fit_MGM141)
ppc(fit_MGM141)
data_MGM283 <- Male_Gammarus_Merged[Male_Gammarus_Merged$expw == 2.83208e-04, ] modelData_MGM283 <- modelData(data_MGM283, time_accumulation = 4) fit_MGM283 <- fitTK(modelData_MGM283, iter = 10000)
quantile_table(fit_MGM283)
plot(fit_MGM283)
ppc(fit_MGM283)
data("Male_Gammarus_seanine_growth") modelData_MGSG <- modelData(Male_Gammarus_seanine_growth, time_accumulation = 1.417) fit_MGSG <- fitTK(modelData_MGSG, iter = 10000)
quantile_table(fit_MGSG)
plot(fit_MGSG)
ppc(fit_MGSG)
data("Oncorhynchus_two") # Pimephales_two data_OT440 = Oncorhynchus_two[Oncorhynchus_two$expw == 0.00440,] modelData_OT440 <- modelData(data_OT440, time_accumulation = 49) fit_OT440 <- fitTK(modelData_OT440, iter = 10000)
quantile_table(fit_OT440)
plot(fit_OT440)
ppc(fit_OT440)
data_OT041 <- Oncorhynchus_two[Oncorhynchus_two$expw == 0.00041,] modelData_OT041 <- modelData(data_OT041, time_accumulation = 49) fit_OT041 <- fitTK(modelData_OT041, iter = 10000)
quantile_table(fit_OT041)
plot(fit_OT041)
ppc(fit_OT041)
data("Chironomus_benzoapyrene") modelData_CB <- modelData(Chironomus_benzoapyrene, time_accumulation = 3) modelData_CB$unifMax = modelData_CB$unifMax * 100 fit_CB <- fitTK(modelData_CB, iter = 10000)
quantile_table(fit_CB)
plot(fit_CB)
ppc(fit_CB)
data("Male_Gammarus_Single") modelData_MGS <- modelData(Male_Gammarus_Single, time_accumulation = 4) fit_MGS <- fitTK(modelData_MGS, iter = 5000, chains = 3) # Data 4 prediction should respect the exposure routes data_4pred <- data.frame( time = 1:25, expw = 4e-5 ) predict_MGS <- predict(fit_MGS, data_4pred) plot(predict_MGS)
# data("Male_Gammarus_seanine_growth") # modelData_MGSG <- modelData(Male_Gammarus_seanine_growth, time_accumulation = 4) # fit_MGSG <- fitTK(modelData_MGSG, iter = 5000, chains = 3) # # # Data 4 prediction should respect the exposure routes # data_4pred <- data.frame( time = 1:25, expw = 18 ) # predict_MGSG <- predict(fit_MGSG, data_4pred) # plot(predict_MGSG)
data("Chiro_Creuzot") Chiro_Creuzot <- Chiro_Creuzot[Chiro_Creuzot$replicate == 1,] modelData_CC <- modelData(Chiro_Creuzot, time_accumulation = 1.0) fit_CC <- fitTK(modelData_CC, iter = 5000, chains = 3) # -------- quantile_table(fit_CC) # Data 4 prediction should respect the exposure routes data_4pred <- data.frame( time = 1:25, expw = 18, exps = 1200, exppw = 15 ) predict_CC <- predict(fit_CC, data_4pred) plot(predict_CC)
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