Start of migration from RUnit to testthat

4 files changed, 0 insertions, 390 deletions

diff --git a/inst/unitTests/Makefile b/inst/unitTests/Makefile
deleted file mode 100644
index 8d132253..00000000
--- 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 < ${SUITE}
diff --git a/inst/unitTests/runit.mkinerrmin.R b/inst/unitTests/runit.mkinerrmin.R
deleted file mode 100644
index 56a33ff9..00000000
--- a/inst/unitTests/runit.mkinerrmin.R
+++ /dev/null
@@ -1,62 +0,0 @@
-# Test SFO_SFO model with FOCUS_2006_D against Schaefer 2007 paper, tolerance = 1% # {{{
-# and check chi2 error values against values obtained with mkin 0.33
-test.FOCUS_2006_D_SFO_SFO <- function()
-{
-  SFO_SFO.1 <- mkinmod(parent = list(type = "SFO", to = "m1"),
-         m1 = list(type = "SFO"), use_of_ff = "min")
-  SFO_SFO.2 <- mkinmod(parent = list(type = "SFO", to = "m1"),
-         m1 = list(type = "SFO"), use_of_ff = "max")
-
-  fit.1.e <- mkinfit(SFO_SFO.1, FOCUS_2006_D)
-  fit.1.d <- mkinfit(SFO_SFO.1, solution_type = "deSolve", FOCUS_2006_D)
-  fit.2.e <- mkinfit(SFO_SFO.2, FOCUS_2006_D)
-  fit.2.d <- mkinfit(SFO_SFO.2, solution_type = "deSolve", FOCUS_2006_D)
-
-  FOCUS_2006_D_results_schaefer07_means <- c(
-    parent_0 = 99.65, DT50_parent = 7.04, DT50_m1 = 131.34)
-
-  r.1.e <- c(fit.1.e$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/inst/unitTests/runit.mkinfit.R b/inst/unitTests/runit.mkinfit.R
deleted file mode 100644
index 8eefb995..00000000
--- a/inst/unitTests/runit.mkinfit.R
+++ /dev/null
@@ -1,227 +0,0 @@
-# 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/inst/unitTests/runit.mkinpredict.R b/inst/unitTests/runit.mkinpredict.R
deleted file mode 100644
index 997857ce..00000000
--- 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 <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: