Nothing
test_that("predict", {
skip_on_cran()
# small test to run fast
data("Male_Gammarus_Single")
Male_Gammarus_Single <- Male_Gammarus_Single[Male_Gammarus_Single$replicate == 1, ]
modelData_MGS <- modelData(Male_Gammarus_Single, time_accumulation = 4)
fit_MGS <- fitTK(modelData_MGS, iter = 10, chains = 2)
data("Male_Gammarus_seanine_growth")
modelData_MGSG <- modelData(Male_Gammarus_seanine_growth, time_accumulation = 1.417)
fit_MGSG <- fitTK(modelData_MGSG, iter = 10, chains=2)
# reduce to have only one metabolite
MSGG_1met <- Male_Gammarus_seanine_growth[, c("time", "expw","conc","concm1","replicate","growth")]
modelData_MGSG_1met <- modelData(MSGG_1met, time_accumulation = 1.417)
fit_MGSG_1met <- fitTK(modelData_MGSG_1met, iter = 10, chains=2)
data("Chiro_Creuzot")
Chiro_Creuzot <- Chiro_Creuzot[Chiro_Creuzot$replicate == 1,]
modelData_CC <- modelData(Chiro_Creuzot, time_accumulation = 1.0)
fit_CC <- fitTK(modelData_CC, iter = 10, chains=2)
# SAME DATA for test
# Data 4 prediction should respect the exposure routes
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)
predict_MGS <- predict(fit_MGS, data_4pred_MGS, fixed_init = FALSE)
plot(fit_MGS)
plot(predict_MGS)
###############
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)
###############
predict_MGSG_1met <- predict(fit_MGSG_1met, data_4pred_MGSG)
plot(fit_MGSG_1met)
plot(predict_MGSG_1met)
############
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)
expect_true(all(class(plot(predict_MGS)) == c("gg","ggplot")))
expect_true(all(class(plot(predict_MGSG)) == c("gg", "ggplot")))
expect_true(all(class(plot(predict_CC)) == c("gg", "ggplot")))
})
test_that( "predict_stan", {
skip_on_cran()
# small test to run fast
data("Male_Gammarus_Single")
Male_Gammarus_Single <- Male_Gammarus_Single[Male_Gammarus_Single$replicate == 1, ]
modelData_MGS <- modelData(Male_Gammarus_Single, time_accumulation = 4)
fit_MGS <- fitTK(modelData_MGS, iter = 100, chains = 2)
data_4pred_MGS <- data.frame( time = 0:25, expw = 7.08e-05)
data("Male_Gammarus_seanine_growth")
modelData_MGSG <- modelData(Male_Gammarus_seanine_growth, time_accumulation = 1.417)
fit_MGSG <- fitTK(modelData_MGSG, iter = 100, chains=2)
data_4pred_MGSG <- data.frame(time = sort(c(0:6,1.417)), expw = 15.533)
# Classical
predict_MGS <- predict(fit_MGS, data_4pred_MGS, fixed_init = TRUE)
plot(fit_MGS)
plot(predict_MGS)
predict_MGS <- predict(fit_MGS, fit_MGS$stanTKdata$origin_data, fixed_init = TRUE)
plot(fit_MGS)
plot(predict_MGS)
# Same timeline
predict_MGS_data <- modelData_predictStan(fit_MGS, data_4pred_MGS, fixed_init = TRUE)
predict_MGS_stan <- predict_stan(fit_MGS, data_4pred_MGS, iter = 1000, chains = 1)
plot(predict_MGS_stan, add_data = TRUE)
predict_MGSG_data <- modelData_predictStan(fit_MGSG, data_4pred_MGSG, fixed_init = TRUE)
predict_MGSG_stan <- predict_stan(fit_MGSG, data_4pred_MGSG, iter = 1000, chains = 1)
plot(predict_MGSG_stan)
plot(predict_MGSG_stan, add_data = TRUE)
plot(fit_MGSG)
# Extended timeline
fit_pred_extend <- predict_stan(fit_MGS, data_4pred_MGS, iter = 1000, chains = 3, time_interp = seq(0,25,0.1))
plot(fit_pred_extend)
predict_MGSG_data <- modelData_predictStan(fit_MGSG, data_4pred_MGSG, time_interp = seq(0,6,0.1))
predict_MGSG_stan_ext <- predict_stan(fit_MGSG, data_4pred_MGSG, iter = 1000, chains = 1, time_interp = seq(0,6,0.1))
plot(predict_MGSG_stan_ext)
plot(predict_MGSG_stan_ext, add_data = TRUE)
plot(fit_MGSG)
plot(predict_MGSG_stan)
plot(fit_MGSG, time_interp = seq(0,6,0.1))
})
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