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+# Copyright (C) 2014-2015 Johannes Ranke
+# Contact: jranke@uni-bremen.de
+
+# This file is part of the R package mkin
+
+# mkin is free software: you can redistribute it and/or modify it under the
+# terms of the GNU General Public License as published by the Free Software
+# Foundation, either version 3 of the License, or (at your option) any later
+# version.
+
+# This program is distributed in the hope that it will be useful, but WITHOUT
+# ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
+# FOR A PARTICULAR PURPOSE. See the GNU General Public License for more
+# details.
+
+# You should have received a copy of the GNU General Public License along with
+# this program. If not, see <http://www.gnu.org/licenses/>
+
+# This test was migrated from a RUnit test inst/unitTests/runit.mkinfit.R
+
+context("Complex test case from Schaefer et al. (2007) Piacenza paper")
+
+schaefer07_complex_model <- mkinmod(
+ parent = list(type = "SFO", to = c("A1", "B1", "C1"), sink = FALSE),
+ A1 = list(type = "SFO", to = "A2"),
+ B1 = list(type = "SFO"),
+ C1 = list(type = "SFO"),
+ A2 = list(type = "SFO"), use_of_ff = "max")
+
+schaefer07_long <- mkin_wide_to_long(schaefer07_complex_case, time = "time")
+
+fit.default <- mkinfit(schaefer07_complex_model, schaefer07_long, quiet = TRUE)
+
+test_that("Complex test case from Schaefer (2007) can be reproduced (10% tolerance)", {
+
+ s <- summary(fit.default)
+ r <- schaefer07_complex_results
+
+ with(as.list(fit.default$bparms.optim), {
+ r$mkin <<- c(
+ k_parent,
+ s$distimes["parent", "DT50"],
+ s$ff["parent_A1"],
+ k_A1,
+ s$distimes["A1", "DT50"],
+ s$ff["parent_B1"],
+ k_B1,
+ s$distimes["B1", "DT50"],
+ s$ff["parent_C1"],
+ k_C1,
+ s$distimes["C1", "DT50"],
+ s$ff["A1_A2"],
+ k_A2,
+ s$distimes["A2", "DT50"])
+ }
+ )
+ r$means <- (r$KinGUI + r$ModelMaker)/2
+ r$mkin.deviation <- abs(round(100 * ((r$mkin - r$means)/r$means), digits=1))
+ expect_equal(r$mkin.deviation < 10, rep(TRUE, 14))
+})
+
+test_that("We avoid the local minumum with default settings", {
+ # If we use optimisation algorithm 'Marq' we get a local minimum with a
+ # sum of squared residuals of 273.3707
+ # When using 'Marq', we need to give a good starting estimate e.g. for k_A2 in
+ # order to get the optimum with sum of squared residuals 240.5686
+ expect_equal(round(fit.default$ssr, 4), 240.5686)
+})

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