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context("Results for synthetic data established in expertise for UBA (Ranke 2014)")
test_that("Results are correct for SFO_lin_a", {
skip_on_cran()
m_synth_SFO_lin <- mkinmod(parent = mkinsub("SFO", "M1"),
M1 = mkinsub("SFO", "M2"),
M2 = mkinsub("SFO"),
use_of_ff = "max", quiet = TRUE)
fit_SFO_lin_a <- mkinfit(m_synth_SFO_lin,
synthetic_data_for_UBA_2014[[1]]$data,
quiet = TRUE)
# Results for SFO_lin_a from p. 48
parms <- round(fit_SFO_lin_a$bparms.optim, c(1, 4, 4, 4, 4, 4))
expect_equivalent(parms, c(102.1, 0.7393, 0.2992, 0.0202, 0.7687, 0.7229))
errmin <- round(100 * mkinerrmin(fit_SFO_lin_a)$err.min, 2)
expect_equivalent(errmin, c(8.45, 8.66, 10.58, 3.59))
})
# Results for DFOP_par_c from p. 54
test_that("Results are correct for DFOP_par_c", {
skip_on_cran()
# Supress warning about non-normal residuals, the data were generated
# using a different error model, so no wonder
suppressWarnings(
fit_DFOP_par_c <- mkinfit(m_synth_DFOP_par,
synthetic_data_for_UBA_2014[[12]]$data,
quiet = TRUE)
)
parms <- round(fit_DFOP_par_c$bparms.optim, c(1, 4, 4, 4, 4, 4, 4, 4))
expect_equal(parms, c(parent_0 = 103.0,
k_M1 = 0.0389, k_M2 = 0.0095,
f_parent_to_M1 = 0.5565, f_parent_to_M2 = 0.3784,
k1 = 0.3263, k2 = 0.0202, g = 0.7130))
errmin <- round(100 * mkinerrmin(fit_DFOP_par_c)$err.min, 2)
expect_equivalent(errmin, c(4.03, 3.05, 5.07, 3.17))
})
# References:
# Ranke (2014) Prüfung und Validierung von Modellierungssoftware als Alternative
# zu ModelMaker 4.0, Umweltbundesamt Projektnummer 27452
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