Nothing
test_that("predict", {
skip_on_cran()
# TEST 1
data("Male_Gammarus_Single")
Male_Gammarus_Single <- Male_Gammarus_Single[Male_Gammarus_Single$replicate == 1, ]
modelData_MGS <- modelData(Male_Gammarus_Single, time_accumulation = 4)
fit_MGS <- fitTK(modelData_MGS, iter = 1000, chains = 2)
data_4pred_MGS <- data.frame( time = 0:25, expw = 7.08e-05)
predict_MGS <- predict(fit_MGS, data_4pred_MGS, fixed_init = TRUE)
plot(fit_MGS)
plot(predict_MGS)
parfit_MGS <- rstan::extract(fit_MGS[["stanfit"]])
# SEE quantile_table(fit_MGS)
manual_MGS = data.frame(
kee = parfit_MGS$ke[,1],
kuw = parfit_MGS$ku[,1],
sigmaConc = parfit_MGS$sigmaCGpred[,1]
)
predict_MGS_m <- predict_manual(manual_MGS, data_4pred_MGS, C0 = 0.023, time_accumulation = 4)
plot(predict_MGS_m)
mcmc_MGS_m1 = data.frame(
kee = 0.4,
kuw = 592.024
)
predict_MGS_mcmc_1 <- predict_manual(mcmc_MGS_m1, data_4pred_MGS, C0 = 0.023, time_accumulation = 4)
plot(predict_MGS_mcmc_1)
### TEST 2
data("Male_Gammarus_seanine_growth")
modelData_MGSG <- modelData(Male_Gammarus_seanine_growth, time_accumulation = 1.417)
fit_MGSG <- fitTK(modelData_MGSG, iter = 1000, chains=2)
data_4pred_MGSG <- data.frame(time = sort(c(0:6,1.417)), expw = 15.533)
predict_MGSG <- predict(fit_MGSG, data_4pred_MGSG)
plot(fit_MGSG)
plot(predict_MGSG)
parfit_MGSG <- rstan::extract(fit_MGSG[["stanfit"]])
# quantile_table(fit_MGSG)
manual_MGSG = data.frame(
kee = parfit_MGSG$ke[,1],
keg = parfit_MGSG$ke[,2],
kuw = parfit_MGSG$ku[,1],
sigmaConc = parfit_MGSG$sigmaCGpred[,1],
sigmaGrowth = parfit_MGSG$sigmaCGpred[,2],
km1 = parfit_MGSG$km[,1],
km2 = parfit_MGSG$km[,2],
km3 = parfit_MGSG$km[,3],
kem1 = parfit_MGSG$kem[,1],
kem2 = parfit_MGSG$kem[,2],
kem3 = parfit_MGSG$kem[,3],
sigmaCmet1 = parfit_MGSG$sigmaCmetpred[,1],
sigmaCmet2 = parfit_MGSG$sigmaCmetpred[,2],
sigmaCmet2 = parfit_MGSG$sigmaCmetpred[,3]
)
predict_MGSG_m <- predict_manual(
manual_MGSG, data_4pred_MGSG, C0 = 0, time_accumulation = 1.417, G0 = 2e-1, gmax=4.5e-1
)
plot(predict_MGSG_m)
predict_MGSG_m1 <- predict_manual(
manual_MGSG[1,], data_4pred_MGSG, C0 = 0, time_accumulation = 1.417, G0 = 2e-1, gmax=4.5e-1
)
plot(predict_MGSG_m1)
### TEST 3
data("Chiro_Creuzot")
Chiro_Creuzot <- Chiro_Creuzot[Chiro_Creuzot$replicate == 1,]
modelData_CC <- modelData(Chiro_Creuzot, time_accumulation = 1.0)
fit_CC <- fitTK(modelData_CC, iter = 1000, chains=2)
data_4pred_CC <- data.frame(time = seq(0,4,0.5), expw = 22.9, exps = 1315.7, exppw = 16.24)
predict_CC <- predict(fit_CC, data_4pred_CC)
plot(fit_CC)
plot(predict_CC)
parfit_CC <- rstan::extract(fit_CC[["stanfit"]])
# quantile_table(fit_CC)
manual_CC = data.frame(
kee = parfit_CC$ke[,1],
kuw = parfit_CC$ku[,1],
kus = parfit_CC$ku[,2],
kupw = parfit_CC$ku[,3],
sigmaConc = parfit_CC$sigmaCGpred[,1],
km1 = parfit_CC$km[,1],
km2 = parfit_CC$km[,2],
kem1 = parfit_CC$kem[,1],
kem2 = parfit_CC$kem[,2],
sigmaCmet1 = parfit_CC$sigmaCmetpred[,1],
sigmaCmet2 = parfit_CC$sigmaCmetpred[,2]
)
predict_CC_m <- predict_manual(manual_CC, data_4pred_CC, C0 = 371.9, time_accumulation = 1.0)
plot(predict_CC_m)
predict_CC_m1 <- predict_manual(manual_CC[1,], data_4pred_CC, C0 = 371.9, time_accumulation = 1.0)
plot(predict_CC_m1)
})
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