# 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: