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