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-rw-r--r--tests/testthat/runit.mkinerrmin.R62
-rw-r--r--tests/testthat/runit.mkinfit.R227
-rw-r--r--tests/testthat/runit.mkinpredict.R86
-rw-r--r--tests/testthat/test_parent_only.R48
4 files changed, 423 insertions, 0 deletions
diff --git a/tests/testthat/runit.mkinerrmin.R b/tests/testthat/runit.mkinerrmin.R
new file mode 100644
index 00000000..56a33ff9
--- /dev/null
+++ b/tests/testthat/runit.mkinerrmin.R
@@ -0,0 +1,62 @@
+# Test SFO_SFO model with FOCUS_2006_D against Schaefer 2007 paper, tolerance = 1% # {{{
+# and check chi2 error values against values obtained with mkin 0.33
+test.FOCUS_2006_D_SFO_SFO <- function()
+{
+ SFO_SFO.1 <- mkinmod(parent = list(type = "SFO", to = "m1"),
+ m1 = list(type = "SFO"), use_of_ff = "min")
+ SFO_SFO.2 <- mkinmod(parent = list(type = "SFO", to = "m1"),
+ m1 = list(type = "SFO"), use_of_ff = "max")
+
+ fit.1.e <- mkinfit(SFO_SFO.1, FOCUS_2006_D)
+ fit.1.d <- mkinfit(SFO_SFO.1, solution_type = "deSolve", FOCUS_2006_D)
+ fit.2.e <- mkinfit(SFO_SFO.2, FOCUS_2006_D)
+ fit.2.d <- mkinfit(SFO_SFO.2, solution_type = "deSolve", FOCUS_2006_D)
+
+ FOCUS_2006_D_results_schaefer07_means <- c(
+ parent_0 = 99.65, DT50_parent = 7.04, DT50_m1 = 131.34)
+
+ r.1.e <- c(fit.1.e$bparms.optim[[1]], endpoints(fit.1.e)$distimes[[1]])
+ r.1.d <- c(fit.1.d$bparms.optim[[1]], endpoints(fit.1.d)$distimes[[1]])
+ r.2.e <- c(fit.2.e$bparms.optim[[1]], endpoints(fit.2.e)$distimes[[1]])
+ r.2.d <- c(fit.2.d$bparms.optim[[1]], endpoints(fit.2.d)$distimes[[1]])
+
+ dev.1.e <- 100 * (r.1.e - FOCUS_2006_D_results_schaefer07_means)/r.1.e
+ checkIdentical(as.numeric(abs(dev.1.e)) < 1, rep(TRUE, 3))
+ dev.1.d <- 100 * (r.1.d - FOCUS_2006_D_results_schaefer07_means)/r.1.d
+ checkIdentical(as.numeric(abs(dev.1.d)) < 1, rep(TRUE, 3))
+ dev.2.e <- 100 * (r.2.e - FOCUS_2006_D_results_schaefer07_means)/r.2.e
+ checkIdentical(as.numeric(abs(dev.2.e)) < 1, rep(TRUE, 3))
+ dev.2.d <- 100 * (r.2.d - FOCUS_2006_D_results_schaefer07_means)/r.2.d
+ checkIdentical(as.numeric(abs(dev.2.d)) < 1, rep(TRUE, 3))
+
+ round(mkinerrmin(fit.2.e), 4)
+ round(mkinerrmin(fit.2.d), 4)
+
+ errmin.FOCUS_2006_D_rounded = data.frame(
+ err.min = c(0.0640, 0.0646, 0.0469),
+ n.optim = c(4, 2, 2),
+ df = c(15, 7, 8),
+ row.names = c("All data", "parent", "m1"))
+ checkEqualsNumeric(round(mkinerrmin(fit.2.e), 4),
+ errmin.FOCUS_2006_D_rounded)
+} # }}}
+
+# Test SFO_SFO model with FOCUS_2006_E against values obtained with mkin 0.33 {{{
+test.FOCUS_2006_E_SFO_SFO <- function()
+{
+ SFO_SFO.2 <- mkinmod(parent = list(type = "SFO", to = "m1"),
+ m1 = list(type = "SFO"), use_of_ff = "max")
+
+ fit.2.e <- mkinfit(SFO_SFO.2, FOCUS_2006_E)
+
+ round(mkinerrmin(fit.2.e), 4)
+ errmin.FOCUS_2006_E_rounded = data.frame(
+ err.min = c(0.1544, 0.1659, 0.1095),
+ n.optim = c(4, 2, 2),
+ df = c(13, 7, 6),
+ row.names = c("All data", "parent", "m1"))
+ checkEqualsNumeric(round(mkinerrmin(fit.2.e), 4),
+ errmin.FOCUS_2006_E_rounded)
+} # }}}
+
+
diff --git a/tests/testthat/runit.mkinfit.R b/tests/testthat/runit.mkinfit.R
new file mode 100644
index 00000000..8eefb995
--- /dev/null
+++ b/tests/testthat/runit.mkinfit.R
@@ -0,0 +1,227 @@
+# Copyright (C) 2010-2014 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/>
+
+# Test SFO model to a relative tolerance of 1% # {{{
+test.FOCUS_2006_SFO <- function()
+{
+ SFO.1 <- mkinmod(parent = list(type = "SFO"))
+ SFO.2 <- mkinmod(parent = list(type = "SFO"), use_of_ff = "max")
+
+ fit.A.SFO.1 <- mkinfit(SFO.1, FOCUS_2006_A, quiet=TRUE)
+ fit.A.SFO.2 <- mkinfit(SFO.2, FOCUS_2006_A, quiet=TRUE)
+
+ median.A.SFO <- as.numeric(lapply(subset(FOCUS_2006_SFO_ref_A_to_F, dataset == "A",
+ c(M0, k, DT50, DT90)), "median"))
+
+ fit.A.SFO.1.r <- as.numeric(c(fit.A.SFO.1$bparms.optim, endpoints(fit.A.SFO.1)$distimes))
+ dev.A.SFO.1 <- abs(round(100 * ((median.A.SFO - fit.A.SFO.1.r)/median.A.SFO), digits=1))
+ checkIdentical(dev.A.SFO.1 < 1, rep(TRUE, length(dev.A.SFO.1)))
+
+ fit.A.SFO.2.r <- as.numeric(c(fit.A.SFO.2$bparms.optim, endpoints(fit.A.SFO.2)$distimes))
+ dev.A.SFO.2 <- abs(round(100 * ((median.A.SFO - fit.A.SFO.2.r)/median.A.SFO), digits=1))
+ checkIdentical(dev.A.SFO.2 < 1, rep(TRUE, length(dev.A.SFO.2)))
+
+ fit.C.SFO.1 <- mkinfit(SFO.1, FOCUS_2006_C, quiet=TRUE)
+ fit.C.SFO.2 <- mkinfit(SFO.2, FOCUS_2006_C, quiet=TRUE)
+
+ median.C.SFO <- as.numeric(lapply(subset(FOCUS_2006_SFO_ref_A_to_F, dataset == "C",
+ c(M0, k, DT50, DT90)), "median"))
+
+ fit.C.SFO.1.r <- as.numeric(c(fit.C.SFO.1$bparms.optim, endpoints(fit.C.SFO.1)$distimes))
+ dev.C.SFO.1 <- abs(round(100 * ((median.C.SFO - fit.C.SFO.1.r)/median.C.SFO), digits=1))
+ checkIdentical(dev.C.SFO.1 < 1, rep(TRUE, length(dev.C.SFO.1)))
+
+ fit.C.SFO.2.r <- as.numeric(c(fit.C.SFO.2$bparms.optim, endpoints(fit.C.SFO.2)$distimes))
+ dev.C.SFO.2 <- abs(round(100 * ((median.C.SFO - fit.C.SFO.2.r)/median.C.SFO), digits=1))
+ checkIdentical(dev.C.SFO.2 < 1, rep(TRUE, length(dev.C.SFO.2)))
+} # }}}
+
+# Test FOMC model to a relative tolerance of 1% {{{
+# See kinfit vignette for a discussion of FOMC fits to FOCUS_2006_A
+# In this case, only M0, DT50 and DT90 are checked
+test.FOCUS_2006_FOMC <- function()
+{
+ FOMC <- mkinmod(parent = list(type = "FOMC"))
+
+ # FOCUS_2006_A (compare kinfit vignette for discussion)
+ fit.A.FOMC <- mkinfit(FOMC, FOCUS_2006_A, quiet=TRUE)
+
+ median.A.FOMC <- as.numeric(lapply(subset(FOCUS_2006_FOMC_ref_A_to_F, dataset == "A",
+ c(M0, alpha, beta, DT50, DT90)), "median"))
+
+ fit.A.FOMC.r <- as.numeric(c(fit.A.FOMC$bparms.optim, endpoints(fit.A.FOMC)$distimes[c("DT50", "DT90")]))
+ dev.A.FOMC <- abs(round(100 * ((median.A.FOMC - fit.A.FOMC.r)/median.A.FOMC), digits=1))
+ dev.A.FOMC <- dev.A.FOMC[c(1, 4, 5)]
+ checkIdentical(dev.A.FOMC < 1, rep(TRUE, length(dev.A.FOMC)))
+
+ # FOCUS_2006_B
+ fit.B.FOMC <- mkinfit(FOMC, FOCUS_2006_B, quiet=TRUE)
+
+ median.B.FOMC <- as.numeric(lapply(subset(FOCUS_2006_FOMC_ref_A_to_F, dataset == "B",
+ c(M0, alpha, beta, DT50, DT90)), "median"))
+
+ fit.B.FOMC.r <- as.numeric(c(fit.B.FOMC$bparms.optim, endpoints(fit.B.FOMC)$distimes[c("DT50", "DT90")]))
+ dev.B.FOMC <- abs(round(100 * ((median.B.FOMC - fit.B.FOMC.r)/median.B.FOMC), digits=1))
+ dev.B.FOMC <- dev.B.FOMC[c(1, 4, 5)]
+ checkIdentical(dev.B.FOMC < 1, rep(TRUE, length(dev.B.FOMC)))
+
+ # FOCUS_2006_C
+ fit.C.FOMC <- mkinfit(FOMC, FOCUS_2006_C, quiet=TRUE)
+
+ median.C.FOMC <- as.numeric(lapply(subset(FOCUS_2006_FOMC_ref_A_to_F, dataset == "C",
+ c(M0, alpha, beta, DT50, DT90)), "median"))
+
+ fit.C.FOMC.r <- as.numeric(c(fit.C.FOMC$bparms.optim, endpoints(fit.C.FOMC)$distimes[c("DT50", "DT90")]))
+ dev.C.FOMC <- abs(round(100 * ((median.C.FOMC - fit.C.FOMC.r)/median.C.FOMC), digits=1))
+ dev.C.FOMC <- dev.C.FOMC[c(1, 4, 5)]
+ checkIdentical(dev.C.FOMC < 1, rep(TRUE, length(dev.C.FOMC)))
+} # }}}
+
+# Test DFOP model, tolerance of 1% with the exception of f parameter for A {{{
+test.FOCUS_2006_DFOP <- function()
+{
+ DFOP <- mkinmod(parent = list(type = "DFOP"))
+
+ # FOCUS_2006_A
+ # Results were too much dependent on algorithm, as this dataset
+ # is pretty much SFO. "Port" gave a lower deviance, but deviated from the
+ # median of FOCUS_2006 solutions
+
+ # FOCUS_2006_B
+ fit.B.DFOP <- mkinfit(DFOP, FOCUS_2006_B, quiet=TRUE)
+
+ median.B.DFOP <- as.numeric(lapply(subset(FOCUS_2006_DFOP_ref_A_to_B, dataset == "B",
+ c(M0, k1, k2, f, DT50, DT90)), "median"))
+
+ fit.B.DFOP.r <- as.numeric(c(fit.B.DFOP$bparms.optim, endpoints(fit.B.DFOP)$distimes[c("DT50", "DT90")]))
+ dev.B.DFOP <- abs(round(100 * ((median.B.DFOP - fit.B.DFOP.r)/median.B.DFOP), digits=1))
+ # about 0.6% deviation for parameter f, the others are <= 0.1%
+ checkIdentical(dev.B.DFOP < 1, rep(TRUE, length(dev.B.DFOP)))
+} # }}}
+
+# Test HS model to a relative tolerance of 1% excluding Mathematica values {{{
+# as they are unreliable
+test.FOCUS_2006_HS <- function()
+{
+ HS <- mkinmod(parent = list(type = "HS"))
+
+ # FOCUS_2006_A
+ fit.A.HS <- mkinfit(HS, FOCUS_2006_A, quiet=TRUE)
+
+ median.A.HS <- as.numeric(lapply(subset(FOCUS_2006_HS_ref_A_to_F, dataset == "A",
+ c(M0, k1, k2, tb, DT50, DT90)), "median"))
+
+ fit.A.HS.r <- as.numeric(c(fit.A.HS$bparms.optim, endpoints(fit.A.HS)$distimes[c("DT50", "DT90")]))
+ dev.A.HS <- abs(round(100 * ((median.A.HS - fit.A.HS.r)/median.A.HS), digits=1))
+ # about 6.7% deviation for parameter f, the others are < 0.1%
+ checkIdentical(dev.A.HS < 1, rep(TRUE, length(dev.A.HS)))
+
+ # FOCUS_2006_B
+ fit.B.HS <- mkinfit(HS, FOCUS_2006_B, quiet=TRUE)
+
+ median.B.HS <- as.numeric(lapply(subset(FOCUS_2006_HS_ref_A_to_F, dataset == "B",
+ c(M0, k1, k2, tb, DT50, DT90)), "median"))
+
+ fit.B.HS.r <- as.numeric(c(fit.B.HS$bparms.optim, endpoints(fit.B.HS)$distimes[c("DT50", "DT90")]))
+ dev.B.HS <- abs(round(100 * ((median.B.HS - fit.B.HS.r)/median.B.HS), digits=1))
+ # < 10% deviation for M0, k1, DT50 and DT90, others are problematic
+ dev.B.HS <- dev.B.HS[c(1, 2, 5, 6)]
+ checkIdentical(dev.B.HS < 10, rep(TRUE, length(dev.B.HS)))
+
+ # FOCUS_2006_C
+ fit.C.HS <- mkinfit(HS, FOCUS_2006_C, quiet=TRUE)
+
+ median.C.HS <- as.numeric(lapply(subset(FOCUS_2006_HS_ref_A_to_F, dataset == "C",
+ c(M0, k1, k2, tb, DT50, DT90)), "median"))
+
+ fit.C.HS.r <- as.numeric(c(fit.C.HS$bparms.optim, endpoints(fit.C.HS)$distimes[c("DT50", "DT90")]))
+ dev.C.HS <- abs(round(100 * ((median.C.HS - fit.C.HS.r)/median.C.HS), digits=1))
+ # deviation <= 0.1%
+ checkIdentical(dev.C.HS < 1, rep(TRUE, length(dev.C.HS)))
+} # }}}
+
+# Test SFORB model against DFOP solutions to a relative tolerance of 1% # {{{
+test.FOCUS_2006_SFORB <- function()
+{
+ SFORB <- mkinmod(parent = list(type = "SFORB"))
+
+ # FOCUS_2006_A
+ # Again it does not make a lot of sense to use a SFO dataset for this
+
+ # FOCUS_2006_B
+ fit.B.SFORB.1 <- mkinfit(SFORB, FOCUS_2006_B, quiet=TRUE)
+ fit.B.SFORB.2 <- mkinfit(SFORB, FOCUS_2006_B, solution_type = "deSolve", quiet=TRUE)
+
+ median.B.SFORB <- as.numeric(lapply(subset(FOCUS_2006_DFOP_ref_A_to_B, dataset == "B",
+ c(M0, k1, k2, DT50, DT90)), "median"))
+
+ fit.B.SFORB.1.r <- as.numeric(c(
+ parent_0 = fit.B.SFORB.1$bparms.optim[[1]],
+ k1 = endpoints(fit.B.SFORB.1)$SFORB[[1]],
+ k2 = endpoints(fit.B.SFORB.1)$SFORB[[2]],
+ endpoints(fit.B.SFORB.1)$distimes[c("DT50", "DT90")]))
+ dev.B.SFORB.1 <- abs(round(100 * ((median.B.SFORB - fit.B.SFORB.1.r)/median.B.SFORB), digits=1))
+ checkIdentical(dev.B.SFORB.1 < 1, rep(TRUE, length(dev.B.SFORB.1)))
+
+ fit.B.SFORB.2.r <- as.numeric(c(
+ parent_0 = fit.B.SFORB.2$bparms.optim[[1]],
+ k1 = endpoints(fit.B.SFORB.2)$SFORB[[1]],
+ k2 = endpoints(fit.B.SFORB.2)$SFORB[[2]],
+ endpoints(fit.B.SFORB.2)$distimes[c("DT50", "DT90")]))
+ dev.B.SFORB.2 <- abs(round(100 * ((median.B.SFORB - fit.B.SFORB.2.r)/median.B.SFORB), digits=1))
+ checkIdentical(dev.B.SFORB.2 < 1, rep(TRUE, length(dev.B.SFORB.2)))
+} # }}}
+
+# Test eigenvalue based fit to Schaefer 2007 data against solution from conference paper {{{
+test.mkinfit.schaefer07_complex_example <- function()
+{
+ 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")
+
+ # If we use the default algorithm 'Marq' we need to give a good starting
+ # estimate for k_A2 in order to find the solution published by Schaefer et al.
+ fit <- mkinfit(schaefer07_complex_model, method.modFit = "Port",
+ mkin_wide_to_long(schaefer07_complex_case, time = "time"))
+ s <- summary(fit)
+ r <- schaefer07_complex_results
+ attach(as.list(fit$bparms.optim))
+ r$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))
+ checkIdentical(r$mkin.deviation < 10, rep(TRUE, 14))
+} # }}}
+
+# vim: set foldmethod=marker ts=2 sw=2 expandtab:
diff --git a/tests/testthat/runit.mkinpredict.R b/tests/testthat/runit.mkinpredict.R
new file mode 100644
index 00000000..997857ce
--- /dev/null
+++ b/tests/testthat/runit.mkinpredict.R
@@ -0,0 +1,86 @@
+# $Id: jranke $
+
+# Copyright (C) 2012 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/>
+
+# Check solution types for SFO {{{
+test.SFO_solution_types <- function()
+{
+ ot = seq(0, 100, by = 1)
+ SFO <- mkinmod(parent = list(type = "SFO"))
+ SFO.analytical <- round(subset(mkinpredict(SFO, c(k_parent_sink = 0.1),
+ c(parent = 100), ot, solution_type = "analytical"), time == 100), digits=5)
+ SFO.deSolve <- round(subset(mkinpredict(SFO, c(k_parent_sink = 0.1),
+ c(parent = 100), ot, solution_type = "deSolve"), time == 100), digits=5)
+ SFO.eigen <- round(subset(mkinpredict(SFO, c(k_parent_sink = 0.1),
+ c(parent = 100), ot, solution_type = "eigen"), time == 100), digits=5)
+
+ checkEquals(SFO.analytical, SFO.deSolve)
+ checkEquals(SFO.analytical, SFO.eigen)
+} # }}}
+
+# Check model specification and solution types for SFO_SFO {{{
+# Relative Tolerance is 0.01%
+# Do not use time 0, as eigenvalue based solution does not give 0 at time 0 for metabolites
+# and relative tolerance is thus not met
+test.SFO_solution_types <- function()
+{
+ tol = 0.01
+ SFO_SFO.1 <- mkinmod(parent = list(type = "SFO", to = "m1"),
+ m1 = list(type = "SFO"), use_of_ff = "min")
+ SFO_SFO.2 <- mkinmod(parent = list(type = "SFO", to = "m1"),
+ m1 = list(type = "SFO"), use_of_ff = "max")
+
+ ot = seq(0, 100, by = 1)
+ r.1.e <- subset(mkinpredict(SFO_SFO.1,
+ c(k_parent_m1 = 0.1, k_parent_sink = 0.1, k_m1_sink = 0.1),
+ c(parent = 100, m1 = 0), ot, solution_type = "eigen"),
+ time %in% c(1, 10, 50, 100))
+ r.1.d <- subset(mkinpredict(SFO_SFO.1,
+ c(k_parent_m1 = 0.1, k_parent_sink = 0.1, k_m1_sink = 0.1),
+ c(parent = 100, m1 = 0), ot, solution_type = "deSolve"),
+ time %in% c(1, 10, 50, 100))
+
+ r.2.e <- subset(mkinpredict(SFO_SFO.2, c(k_parent = 0.2, f_parent_to_m1 = 0.5, k_m1 = 0.1),
+ c(parent = 100, m1 = 0), ot, solution_type = "eigen"),
+ time %in% c(1, 10, 50, 100))
+ r.2.d <- subset(mkinpredict(SFO_SFO.2, c(k_parent = 0.2, f_parent_to_m1 = 0.5, k_m1 = 0.1),
+ c(parent = 100, m1 = 0), ot, solution_type = "deSolve"),
+ time %in% c(1, 10, 50, 100))
+
+ # Compare eigen and deSolve for minimum use of formation fractions
+ dev.1.e_d.percent = 100 * (r.1.e[-1] - r.1.d[-1])/r.1.e[-1]
+ dev.1.e_d.percent = as.numeric(unlist((dev.1.e_d.percent)))
+ dev.1.e_d.percent = ifelse(is.na(dev.1.e_d.percent), 0, dev.1.e_d.percent)
+ checkIdentical(dev.1.e_d.percent < tol, rep(TRUE, length(dev.1.e_d.percent)))
+
+ # Compare eigen and deSolve for maximum use of formation fractions
+ dev.2.e_d.percent = 100 * (r.1.e[-1] - r.1.d[-1])/r.1.e[-1]
+ dev.2.e_d.percent = as.numeric(unlist((dev.2.e_d.percent)))
+ dev.2.e_d.percent = ifelse(is.na(dev.2.e_d.percent), 0, dev.2.e_d.percent)
+ checkIdentical(dev.2.e_d.percent < tol, rep(TRUE, length(dev.2.e_d.percent)))
+
+ # Compare minimum and maximum use of formation fractions
+ dev.1_2.e.percent = 100 * (r.1.e[-1] - r.2.e[-1])/r.1.e[-1]
+ dev.1_2.e.percent = as.numeric(unlist((dev.1_2.e.percent)))
+ dev.1_2.e.percent = ifelse(is.na(dev.1_2.e.percent), 0, dev.1_2.e.percent)
+ checkIdentical(dev.1_2.e.percent < tol, rep(TRUE, length(dev.1_2.e.percent)))
+
+} # }}}
+
+# vim: set foldmethod=marker ts=2 sw=2 expandtab:
diff --git a/tests/testthat/test_parent_only.R b/tests/testthat/test_parent_only.R
new file mode 100644
index 00000000..16fc5131
--- /dev/null
+++ b/tests/testthat/test_parent_only.R
@@ -0,0 +1,48 @@
+context("Fitting of parent only models")
+
+calc_dev.percent <- function(fitlist, reference) {
+ for (i in 1:length(fitlist)) {
+ fit <- fitlist[[i]]
+ results <- c(fit$bparms.optim,
+ endpoints(fit)$distimes$DT50,
+ endpoints(fit)$distimes$DT90)
+ dev.percent[[i]] <- abs(100 * ((reference - results)/reference))
+ }
+ return(dev.percent)
+}
+
+SFO <- mkinmod(parent = list(type = "SFO"))
+FOMC <- mkinmod(parent = list(type = "FOMC"))
+
+test_that("SFO fit for FOCUS A deviates less than 0.1% from median of values from FOCUS report", {
+ fits.A.SFO <- list()
+ fits.A.SFO[[1]] <- mkinfit("SFO", FOCUS_2006_A, quiet=TRUE)
+ fits.A.SFO[[2]] <- mkinfit(SFO, FOCUS_2006_A, quiet=TRUE)
+ fits.A.SFO[[3]] <- mkinfit(SFO, FOCUS_2006_A, quiet=TRUE, solution_type = "eigen")
+ fits.A.SFO[[4]] <- mkinfit(SFO, FOCUS_2006_A, quiet=TRUE, solution_type = "deSolve")
+
+ median.A.SFO <- as.numeric(lapply(subset(FOCUS_2006_SFO_ref_A_to_F,
+ dataset == "A",
+ c(M0, k, DT50, DT90)), "median"))
+
+ dev.percent <- calc_dev.percent(fits.A.SFO, median.A.SFO)
+ expect_equivalent(dev.percent[[1]] < 0.1, rep(TRUE, 4))
+ expect_equivalent(dev.percent[[2]] < 0.1, rep(TRUE, 4))
+ expect_equivalent(dev.percent[[3]] < 0.1, rep(TRUE, 4))
+ expect_equivalent(dev.percent[[4]] < 0.1, rep(TRUE, 4))
+})
+
+test_that("SFO fit for FOCUS C deviates less than 0.1% from median of values from FOCUS report", {
+ fits.C.SFO <- list()
+ fits.C.SFO[[1]] <- mkinfit("SFO", FOCUS_2006_C, quiet=TRUE)
+ fits.C.SFO[[2]] <- mkinfit(SFO, FOCUS_2006_C, quiet=TRUE)
+ fits.C.SFO[[3]] <- mkinfit(SFO, FOCUS_2006_C, quiet=TRUE, solution_type = "deSolve")
+
+ median.C.SFO <- as.numeric(lapply(subset(FOCUS_2006_SFO_ref_A_to_F,
+ dataset == "C",
+ c(M0, k, DT50, DT90)), "median"))
+ dev.percent <- calc_dev.percent(fits.C.SFO, median.C.SFO)
+ expect_equivalent(dev.percent[[1]] < 0.1, rep(TRUE, 4))
+ expect_equivalent(dev.percent[[2]] < 0.1, rep(TRUE, 4))
+ expect_equivalent(dev.percent[[3]] < 0.1, rep(TRUE, 4))
+})

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