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Diffstat (limited to 'tests/testthat/runit.mkinfit.R')
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1 files changed, 227 insertions, 0 deletions
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:
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