# 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", quiet = TRUE) 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) })