From e959fde98f95f3595e01490b67892678bbcd1b27 Mon Sep 17 00:00:00 2001 From: Johannes Ranke Date: Wed, 7 May 2014 14:47:28 +0200 Subject: Fork the gmkin GUI from mkin. See ChangeLog for details --- inst/unitTests/Makefile | 15 -- inst/unitTests/runit.mkinfit.R | 294 ------------------------------------- inst/unitTests/runit.mkinpredict.R | 86 ----------- 3 files changed, 395 deletions(-) delete mode 100644 inst/unitTests/Makefile delete mode 100644 inst/unitTests/runit.mkinfit.R delete mode 100644 inst/unitTests/runit.mkinpredict.R (limited to 'inst/unitTests') diff --git a/inst/unitTests/Makefile b/inst/unitTests/Makefile deleted file mode 100644 index 8d13225..0000000 --- a/inst/unitTests/Makefile +++ /dev/null @@ -1,15 +0,0 @@ -TOP=../.. -PKG=${shell cd ${TOP};pwd} -SUITE=doRUnit.R -R=R - -all: inst test - -inst: # Install package - cd ${TOP}/..;\ - ${R} CMD INSTALL ${PKG} - -test: # Run unit tests - export RCMDCHECK=FALSE;\ - cd ${TOP}/tests;\ - ${R} --vanilla --slave < ${SUITE} diff --git a/inst/unitTests/runit.mkinfit.R b/inst/unitTests/runit.mkinfit.R deleted file mode 100644 index 9a6bd72..0000000 --- a/inst/unitTests/runit.mkinfit.R +++ /dev/null @@ -1,294 +0,0 @@ -# $Id: runit.mkinfit.R 68 2010-09-09 22:40:04Z jranke $ - -# Copyright (C) 2010-2013 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 - -# 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)) - 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)) - 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)) - 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 - fit.A.DFOP <- mkinfit(DFOP, FOCUS_2006_A, quiet=TRUE) - - median.A.DFOP <- as.numeric(lapply(subset(FOCUS_2006_DFOP_ref_A_to_B, dataset == "A", - c(M0, k1, k2, f, DT50, DT90)), "median")) - - fit.A.DFOP.r <- as.numeric(c(fit.A.DFOP$bparms.optim, endpoints(fit.A.DFOP)$distimes)) - dev.A.DFOP <- abs(round(100 * ((median.A.DFOP - fit.A.DFOP.r)/median.A.DFOP), digits=1)) - # about 6.7% deviation for parameter f, the others are < 0.1% - checkIdentical(dev.A.DFOP < c(1, 1, 1, 10, 1, 1), rep(TRUE, length(dev.A.DFOP))) - - # 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)) - 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)) - 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)) - 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.A.HS.r <- as.numeric(c(fit.A.HS$bparms.optim, endpoints(fit.A.HS)$distimes)) - dev.A.HS <- abs(round(100 * ((median.A.HS - fit.A.HS.r)/median.A.HS), digits=1)) - # deviation <= 0.1% - checkIdentical(dev.A.HS < 1, rep(TRUE, length(dev.A.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 - fit.A.SFORB.1 <- mkinfit(SFORB, FOCUS_2006_A, quiet=TRUE) - fit.A.SFORB.2 <- mkinfit(SFORB, FOCUS_2006_A, solution_type = "deSolve", quiet=TRUE) - - median.A.SFORB <- as.numeric(lapply(subset(FOCUS_2006_DFOP_ref_A_to_B, dataset == "A", - c(M0, k1, k2, DT50, DT90)), "median")) - - fit.A.SFORB.1.r <- as.numeric(c( - parent_0 = fit.A.SFORB.1$bparms.optim[[1]], - k1 = endpoints(fit.A.SFORB.1)$SFORB[[1]], - k2 = endpoints(fit.A.SFORB.1)$SFORB[[2]], - endpoints(fit.A.SFORB.1)$distimes)) - dev.A.SFORB.1 <- abs(round(100 * ((median.A.SFORB - fit.A.SFORB.1.r)/median.A.SFORB), digits=1)) - # The first Eigenvalue is a lot different from k1 in the DFOP fit - # The explanation is that the dataset is simply SFO - dev.A.SFORB.1 <- dev.A.SFORB.1[c(1, 3, 4, 5)] - checkIdentical(dev.A.SFORB.1 < 1, rep(TRUE, length(dev.A.SFORB.1))) - - fit.A.SFORB.2.r <- as.numeric(c( - parent_0 = fit.A.SFORB.2$bparms.optim[[1]], - k1 = endpoints(fit.A.SFORB.2)$SFORB[[1]], - k2 = endpoints(fit.A.SFORB.2)$SFORB[[2]], - endpoints(fit.A.SFORB.2)$distimes)) - dev.A.SFORB.2 <- abs(round(100 * ((median.A.SFORB - fit.A.SFORB.2.r)/median.A.SFORB), digits=1)) - # The first Eigenvalue is a lot different from k1 in the DFOP fit - # The explanation is that the dataset is simply SFO - dev.A.SFORB.2 <- dev.A.SFORB.2[c(1, 3, 4, 5)] - checkIdentical(dev.A.SFORB.2 < 1, rep(TRUE, length(dev.A.SFORB.2))) - - # 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)) - 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)) - 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 SFO_SFO model with FOCUS_2006_D against Schaefer 2007 paper, tolerance = 1% # {{{ -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) - SFO <- mkinmod(parent = list(type = "SFO")) - f.SFO <- mkinfit(SFO, FOCUS_2006_D) - fit.2.d <- mkinfit(SFO_SFO.2, solution_type = "deSolve", FOCUS_2006_D) - fit.2.e <- mkinfit(SFO_SFO.2, 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)) -} # }}} - -# 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")) - - fit <- mkinfit(schaefer07_complex_model, - mkin_wide_to_long(schaefer07_complex_case, time = "time")) - s <- summary(fit) - r <- schaefer07_complex_results - attach(as.list(fit$bparms.optim)) - k_parent <- sum(k_parent_A1, k_parent_B1, k_parent_C1) - r$mkin <- c( - k_parent, - s$distimes["parent", "DT50"], - s$ff["parent_A1"], - sum(k_A1_sink, k_A1_A2), - s$distimes["A1", "DT50"], - s$ff["parent_B1"], - k_B1_sink, - s$distimes["B1", "DT50"], - s$ff["parent_C1"], - k_C1_sink, - s$distimes["C1", "DT50"], - s$ff["A1_A2"], - k_A2_sink, - 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[1:11] < 10, rep(TRUE, 11)) -} # }}} - -# vim: set foldmethod=marker ts=2 sw=2 expandtab: diff --git a/inst/unitTests/runit.mkinpredict.R b/inst/unitTests/runit.mkinpredict.R deleted file mode 100644 index 997857c..0000000 --- a/inst/unitTests/runit.mkinpredict.R +++ /dev/null @@ -1,86 +0,0 @@ -# $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 - -# 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: -- cgit v1.2.1