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-rw-r--r--DESCRIPTION2
-rw-r--r--R/endpoints.R2
-rw-r--r--R/mkinerrmin.R2
-rw-r--r--R/mkinfit.R22
-rw-r--r--R/plot.mkinfit.R5
-rw-r--r--inst/unitTests/runit.mkinfit.R44
-rw-r--r--vignettes/examples-L1_SFO_plots.pdfbin6265 -> 0 bytes
-rw-r--r--vignettes/examples.Rnw106
-rw-r--r--vignettes/examples.pdfbin176985 -> 281843 bytes
-rw-r--r--vignettes/mkin.pdfbin162839 -> 162843 bytes
-rw-r--r--vignettes/run.bat5
11 files changed, 100 insertions, 88 deletions
diff --git a/DESCRIPTION b/DESCRIPTION
index 8db0fc85..808458cb 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -2,7 +2,7 @@ Package: mkin
Type: Package
Title: Routines for fitting kinetic models with one or more state
variables to chemical degradation data
-Version: 0.9-12
+Version: 0.9-13
Date: 2013-02-18
Author: Johannes Ranke, Katrin Lindenberger, René Lehmann
Maintainer: Johannes Ranke <jranke@uni-bremen.de>
diff --git a/R/endpoints.R b/R/endpoints.R
index 98e290d0..163dc8d4 100644
--- a/R/endpoints.R
+++ b/R/endpoints.R
@@ -3,7 +3,7 @@ endpoints <- function(fit, pseudoDT50 = FALSE) {
# fractions and SFORB eigenvalues from optimised parameters
ep <- list()
obs_vars <- fit$obs_vars
- parms.all <- fit$parms.all
+ parms.all <- fit$bparms.ode
ep$distimes <- data.frame(DT50 = rep(NA, length(obs_vars)),
DT90 = rep(NA, length(obs_vars)),
row.names = obs_vars)
diff --git a/R/mkinerrmin.R b/R/mkinerrmin.R
index 49538e37..8b1e9b2e 100644
--- a/R/mkinerrmin.R
+++ b/R/mkinerrmin.R
@@ -20,7 +20,7 @@
utils::globalVariables(c("name"))
mkinerrmin <- function(fit, alpha = 0.05)
{
- parms.optim <- fit$parms.all
+ parms.optim <- fit$par
kinerrmin <- function(errdata, n.parms) {
means.mean <- mean(errdata$value_mean, na.rm=TRUE)
df = length(errdata$value_mean) - n.parms
diff --git a/R/mkinfit.R b/R/mkinfit.R
index e084cf1a..3ca38990 100644
--- a/R/mkinfit.R
+++ b/R/mkinfit.R
@@ -191,7 +191,9 @@ mkinfit <- function(mkinmod, observed,
value = c(state.ini.fixed, parms.fixed))
fit$fixed$type = c(rep("state", length(state.ini.fixed)), rep("deparm", length(parms.fixed)))
- parms.all = backtransform_odeparms(c(fit$par, parms.fixed), mod_vars)
+ bparms.optim = backtransform_odeparms(fit$par, mod_vars)
+ bparms.fixed = backtransform_odeparms(c(state.ini.fixed, parms.fixed), mod_vars)
+ bparms.all = c(bparms.optim, bparms.fixed)
# Collect observed, predicted and residuals
data <- merge(fit$observed, fit$predicted, by = c("time", "name"))
@@ -201,8 +203,10 @@ mkinfit <- function(mkinmod, observed,
fit$data <- data[order(data$variable, data$time), ]
fit$atol <- atol
fit$rtol <- rtol
- fit$parms.all <- parms.all # Return all backtransformed parameters for summary
- fit$odeparms.final <- parms.all[mkinmod$parms] # Return ode parameters for further fitting
+ # Return all backtransformed parameters for summary
+ fit$bparms.optim <- bparms.optim
+ fit$bparms.fixed <- bparms.fixed
+ fit$bparms.ode <- bparms.all[mkinmod$parms] # Return ode parameters for further fitting
fit$date <- date()
class(fit) <- c("mkinfit", "modFit")
@@ -231,7 +235,7 @@ summary.mkinfit <- function(object, data = TRUE, distimes = TRUE, ...) {
param <- cbind(param, se)
dimnames(param) <- list(pnames, c("Estimate", "Std. Error"))
- bparam <- as.matrix(object$parms.all)
+ bparam <- as.matrix(object$bparms.optim)
dimnames(bparam) <- list(pnames, c("Estimate"))
ans <- list(
@@ -259,9 +263,9 @@ summary.mkinfit <- function(object, data = TRUE, distimes = TRUE, ...) {
ans$errmin <- mkinerrmin(object, alpha = 0.05)
- ans$parms.all <- object$parms.all
+ ans$bparms.ode <- object$bparms.ode
ep <- endpoints(object)
- if (!is.null(ep$ff))
+ if (length(ep$ff) != 0)
ans$ff <- ep$ff
if(distimes) ans$distimes <- ep$distimes
if(length(ep$SFORB) != 0) ans$SFORB <- ep$SFORB
@@ -305,19 +309,19 @@ print.summary.mkinfit <- function(x, digits = max(3, getOption("digits") - 3), .
if(printdistimes){
cat("\nEstimated disappearance times:\n")
print(x$distimes, digits=digits,...)
- }
+ }
printff <- !is.null(x$ff)
if(printff & x$use_of_ff == "min"){
cat("\nEstimated formation fractions:\n")
print(data.frame(ff = x$ff), digits=digits,...)
- }
+ }
printSFORB <- !is.null(x$SFORB)
if(printSFORB){
cat("\nEstimated Eigenvalues of SFORB model(s):\n")
print(x$SFORB, digits=digits,...)
- }
+ }
printcor <- is.numeric(x$cov.unscaled)
if (printcor){
diff --git a/R/plot.mkinfit.R b/R/plot.mkinfit.R
index fc8ecf74..59ef861e 100644
--- a/R/plot.mkinfit.R
+++ b/R/plot.mkinfit.R
@@ -30,9 +30,8 @@ plot.mkinfit <- function(x, fit = x,
add = FALSE, legend = !add, ...)
{
solution_type = fit$solution_type
- fixed <- fit$fixed$value
- names(fixed) <- rownames(fit$fixed)
- parms.all <- c(fit$parms.all, fixed)
+ parms.all <- c(fit$bparms.optim, fit$bparms.fixed)
+
ininames <- c(
rownames(subset(fit$start, type == "state")),
rownames(subset(fit$fixed, type == "state")))
diff --git a/inst/unitTests/runit.mkinfit.R b/inst/unitTests/runit.mkinfit.R
index 26007e78..784054b8 100644
--- a/inst/unitTests/runit.mkinfit.R
+++ b/inst/unitTests/runit.mkinfit.R
@@ -30,11 +30,11 @@ test.FOCUS_2006_SFO <- function()
median.A.SFO <- as.numeric(lapply(subset(FOCUS_2006_SFO_ref_A_to_F, dataset == "A",
c(M0, k, DT50, DT90)), "median"))
- fit.A.SFO.1.r <- as.numeric(c(fit.A.SFO.1$parms.all, endpoints(fit.A.SFO.1)$distimes))
+ fit.A.SFO.1.r <- as.numeric(c(fit.A.SFO.1$bparms.optim, endpoints(fit.A.SFO.1)$distimes))
dev.A.SFO.1 <- abs(round(100 * ((median.A.SFO - fit.A.SFO.1.r)/median.A.SFO), digits=1))
checkIdentical(dev.A.SFO.1 < 1, rep(TRUE, length(dev.A.SFO.1)))
- fit.A.SFO.2.r <- as.numeric(c(fit.A.SFO.2$parms.all, endpoints(fit.A.SFO.2)$distimes))
+ fit.A.SFO.2.r <- as.numeric(c(fit.A.SFO.2$bparms.optim, endpoints(fit.A.SFO.2)$distimes))
dev.A.SFO.2 <- abs(round(100 * ((median.A.SFO - fit.A.SFO.2.r)/median.A.SFO), digits=1))
checkIdentical(dev.A.SFO.2 < 1, rep(TRUE, length(dev.A.SFO.2)))
@@ -44,11 +44,11 @@ test.FOCUS_2006_SFO <- function()
median.C.SFO <- as.numeric(lapply(subset(FOCUS_2006_SFO_ref_A_to_F, dataset == "C",
c(M0, k, DT50, DT90)), "median"))
- fit.C.SFO.1.r <- as.numeric(c(fit.C.SFO.1$parms.all, endpoints(fit.C.SFO.1)$distimes))
+ fit.C.SFO.1.r <- as.numeric(c(fit.C.SFO.1$bparms.optim, endpoints(fit.C.SFO.1)$distimes))
dev.C.SFO.1 <- abs(round(100 * ((median.C.SFO - fit.C.SFO.1.r)/median.C.SFO), digits=1))
checkIdentical(dev.C.SFO.1 < 1, rep(TRUE, length(dev.C.SFO.1)))
- fit.C.SFO.2.r <- as.numeric(c(fit.C.SFO.2$parms.all, endpoints(fit.C.SFO.2)$distimes))
+ fit.C.SFO.2.r <- as.numeric(c(fit.C.SFO.2$bparms.optim, endpoints(fit.C.SFO.2)$distimes))
dev.C.SFO.2 <- abs(round(100 * ((median.C.SFO - fit.C.SFO.2.r)/median.C.SFO), digits=1))
checkIdentical(dev.C.SFO.2 < 1, rep(TRUE, length(dev.C.SFO.2)))
} # }}}
@@ -66,7 +66,7 @@ test.FOCUS_2006_FOMC <- function()
median.A.FOMC <- as.numeric(lapply(subset(FOCUS_2006_FOMC_ref_A_to_F, dataset == "A",
c(M0, alpha, beta, DT50, DT90)), "median"))
- fit.A.FOMC.r <- as.numeric(c(fit.A.FOMC$parms.all, endpoints(fit.A.FOMC)$distimes))
+ fit.A.FOMC.r <- as.numeric(c(fit.A.FOMC$bparms.optim, endpoints(fit.A.FOMC)$distimes))
dev.A.FOMC <- abs(round(100 * ((median.A.FOMC - fit.A.FOMC.r)/median.A.FOMC), digits=1))
dev.A.FOMC <- dev.A.FOMC[c(1, 4, 5)]
checkIdentical(dev.A.FOMC < 1, rep(TRUE, length(dev.A.FOMC)))
@@ -77,7 +77,7 @@ test.FOCUS_2006_FOMC <- function()
median.B.FOMC <- as.numeric(lapply(subset(FOCUS_2006_FOMC_ref_A_to_F, dataset == "B",
c(M0, alpha, beta, DT50, DT90)), "median"))
- fit.B.FOMC.r <- as.numeric(c(fit.B.FOMC$parms.all, endpoints(fit.B.FOMC)$distimes))
+ fit.B.FOMC.r <- as.numeric(c(fit.B.FOMC$bparms.optim, endpoints(fit.B.FOMC)$distimes))
dev.B.FOMC <- abs(round(100 * ((median.B.FOMC - fit.B.FOMC.r)/median.B.FOMC), digits=1))
dev.B.FOMC <- dev.B.FOMC[c(1, 4, 5)]
checkIdentical(dev.B.FOMC < 1, rep(TRUE, length(dev.B.FOMC)))
@@ -88,7 +88,7 @@ test.FOCUS_2006_FOMC <- function()
median.C.FOMC <- as.numeric(lapply(subset(FOCUS_2006_FOMC_ref_A_to_F, dataset == "C",
c(M0, alpha, beta, DT50, DT90)), "median"))
- fit.C.FOMC.r <- as.numeric(c(fit.C.FOMC$parms.all, endpoints(fit.C.FOMC)$distimes))
+ fit.C.FOMC.r <- as.numeric(c(fit.C.FOMC$bparms.optim, endpoints(fit.C.FOMC)$distimes))
dev.C.FOMC <- abs(round(100 * ((median.C.FOMC - fit.C.FOMC.r)/median.C.FOMC), digits=1))
dev.C.FOMC <- dev.C.FOMC[c(1, 4, 5)]
checkIdentical(dev.C.FOMC < 1, rep(TRUE, length(dev.C.FOMC)))
@@ -106,7 +106,7 @@ test.FOCUS_2006_DFOP <- function()
median.A.DFOP <- as.numeric(lapply(subset(FOCUS_2006_DFOP_ref_A_to_B, dataset == "A",
c(M0, k1, k2, f, DT50, DT90)), "median"))
- fit.A.DFOP.r <- as.numeric(c(fit.A.DFOP$parms.all, endpoints(fit.A.DFOP)$distimes))
+ fit.A.DFOP.r <- as.numeric(c(fit.A.DFOP$bparms.optim, endpoints(fit.A.DFOP)$distimes))
dev.A.DFOP <- abs(round(100 * ((median.A.DFOP - fit.A.DFOP.r)/median.A.DFOP), digits=1))
# about 6.7% deviation for parameter f, the others are < 0.1%
checkIdentical(dev.A.DFOP < c(1, 1, 1, 10, 1, 1), rep(TRUE, length(dev.A.DFOP)))
@@ -117,7 +117,7 @@ test.FOCUS_2006_DFOP <- function()
median.B.DFOP <- as.numeric(lapply(subset(FOCUS_2006_DFOP_ref_A_to_B, dataset == "B",
c(M0, k1, k2, f, DT50, DT90)), "median"))
- fit.B.DFOP.r <- as.numeric(c(fit.B.DFOP$parms.all, endpoints(fit.B.DFOP)$distimes))
+ fit.B.DFOP.r <- as.numeric(c(fit.B.DFOP$bparms.optim, endpoints(fit.B.DFOP)$distimes))
dev.B.DFOP <- abs(round(100 * ((median.B.DFOP - fit.B.DFOP.r)/median.B.DFOP), digits=1))
# about 0.6% deviation for parameter f, the others are <= 0.1%
checkIdentical(dev.B.DFOP < 1, rep(TRUE, length(dev.B.DFOP)))
@@ -135,7 +135,7 @@ test.FOCUS_2006_HS <- function()
median.A.HS <- as.numeric(lapply(subset(FOCUS_2006_HS_ref_A_to_F, dataset == "A",
c(M0, k1, k2, tb, DT50, DT90)), "median"))
- fit.A.HS.r <- as.numeric(c(fit.A.HS$parms.all, endpoints(fit.A.HS)$distimes))
+ fit.A.HS.r <- as.numeric(c(fit.A.HS$bparms.optim, endpoints(fit.A.HS)$distimes))
dev.A.HS <- abs(round(100 * ((median.A.HS - fit.A.HS.r)/median.A.HS), digits=1))
# about 6.7% deviation for parameter f, the others are < 0.1%
checkIdentical(dev.A.HS < 1, rep(TRUE, length(dev.A.HS)))
@@ -146,7 +146,7 @@ test.FOCUS_2006_HS <- function()
median.B.HS <- as.numeric(lapply(subset(FOCUS_2006_HS_ref_A_to_F, dataset == "B",
c(M0, k1, k2, tb, DT50, DT90)), "median"))
- fit.B.HS.r <- as.numeric(c(fit.B.HS$parms.all, endpoints(fit.B.HS)$distimes))
+ fit.B.HS.r <- as.numeric(c(fit.B.HS$bparms.optim, endpoints(fit.B.HS)$distimes))
dev.B.HS <- abs(round(100 * ((median.B.HS - fit.B.HS.r)/median.B.HS), digits=1))
# < 10% deviation for M0, k1, DT50 and DT90, others are problematic
dev.B.HS <- dev.B.HS[c(1, 2, 5, 6)]
@@ -158,7 +158,7 @@ test.FOCUS_2006_HS <- function()
median.C.HS <- as.numeric(lapply(subset(FOCUS_2006_HS_ref_A_to_F, dataset == "C",
c(M0, k1, k2, tb, DT50, DT90)), "median"))
- fit.A.HS.r <- as.numeric(c(fit.A.HS$parms.all, endpoints(fit.A.HS)$distimes))
+ fit.A.HS.r <- as.numeric(c(fit.A.HS$bparms.optim, endpoints(fit.A.HS)$distimes))
dev.A.HS <- abs(round(100 * ((median.A.HS - fit.A.HS.r)/median.A.HS), digits=1))
# deviation <= 0.1%
checkIdentical(dev.A.HS < 1, rep(TRUE, length(dev.A.HS)))
@@ -177,7 +177,7 @@ test.FOCUS_2006_SFORB <- function()
c(M0, k1, k2, DT50, DT90)), "median"))
fit.A.SFORB.1.r <- as.numeric(c(
- parent_0 = fit.A.SFORB.1$parms.all[[1]],
+ 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))
@@ -188,7 +188,7 @@ test.FOCUS_2006_SFORB <- function()
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$parms.all[[1]],
+ parent_0 = fit.A.SFORB.2$bparms.optim[[1]],
k1 = endpoints(fit.A.SFORB.2)$SFORB[[1]],
k2 = endpoints(fit.A.SFORB.2)$SFORB[[2]],
endpoints(fit.A.SFORB.2)$distimes))
@@ -206,7 +206,7 @@ test.FOCUS_2006_SFORB <- function()
c(M0, k1, k2, DT50, DT90)), "median"))
fit.B.SFORB.1.r <- as.numeric(c(
- parent_0 = fit.B.SFORB.1$parms.all[[1]],
+ 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))
@@ -214,7 +214,7 @@ test.FOCUS_2006_SFORB <- function()
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$parms.all[[1]],
+ parent_0 = fit.B.SFORB.2$bparms.optim[[1]],
k1 = endpoints(fit.B.SFORB.2)$SFORB[[1]],
k2 = endpoints(fit.B.SFORB.2)$SFORB[[2]],
endpoints(fit.B.SFORB.2)$distimes))
@@ -239,15 +239,15 @@ test.FOCUS_2006_D_SFO_SFO <- function()
fit.2.d <- mkinfit(SFO_SFO.2, solution_type = "deSolve", FOCUS_2006_D)
# Eigenvalue based solution with maximum use of formation fractions only
# works correctly with initial parameters very close to final parameters!
- fit.2.e <- mkinfit(SFO_SFO.2, parms.ini = fit.2.d$odeparms.final, FOCUS_2006_D)
+ fit.2.e <- mkinfit(SFO_SFO.2, parms.ini = fit.2.d$bparms.ode, FOCUS_2006_D)
FOCUS_2006_D_results_schaefer07_means <- c(
parent_0 = 99.65, DT50_parent = 7.04, DT50_m1 = 131.34)
- r.1.e <- c(fit.1.e$parms.all[[1]], endpoints(fit.1.e)$distimes[[1]])
- r.1.d <- c(fit.1.d$parms.all[[1]], endpoints(fit.1.d)$distimes[[1]])
- r.2.e <- c(fit.2.e$parms.all[[1]], endpoints(fit.2.e)$distimes[[1]])
- r.2.d <- c(fit.2.d$parms.all[[1]], endpoints(fit.2.d)$distimes[[1]])
+ r.1.e <- c(fit.1.e$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)$distimes[[1]])
dev.1.e <- 100 * (r.1.e - FOCUS_2006_D_results_schaefer07_means)/r.1.e
checkIdentical(as.numeric(abs(dev.1.e)) < 1, rep(TRUE, 3))
@@ -273,7 +273,7 @@ test.mkinfit.schaefer07_complex_example <- function()
mkin_wide_to_long(schaefer07_complex_case, time = "time"))
s <- summary(fit)
r <- schaefer07_complex_results
- attach(as.list(fit$parms.all))
+ attach(as.list(fit$bparms.optim))
k_parent <- sum(k_parent_A1, k_parent_B1, k_parent_C1)
r$mkin <- c(
k_parent,
diff --git a/vignettes/examples-L1_SFO_plots.pdf b/vignettes/examples-L1_SFO_plots.pdf
deleted file mode 100644
index d34116fe..00000000
--- a/vignettes/examples-L1_SFO_plots.pdf
+++ /dev/null
Binary files differ
diff --git a/vignettes/examples.Rnw b/vignettes/examples.Rnw
index 340d75f7..6f3cfc9d 100644
--- a/vignettes/examples.Rnw
+++ b/vignettes/examples.Rnw
@@ -108,8 +108,7 @@ is checked.
<<L1_FOMC, echo=TRUE>>=
m.L1.FOMC <- mkinfit(FOMC, FOCUS_2006_L1_mkin, quiet=TRUE)
-s.m.L1.FOMC <- summary(m.L1.FOMC)
-s.m.L1.FOMC$errmin
+summary(m.L1.FOMC)
@
Due to the higher number of parameters, and the lower number of degrees of freedom
@@ -196,7 +195,6 @@ m.L2.DFOP <- mkinfit(DFOP, FOCUS_2006_L2_mkin,
parms.ini = c(k1 = 1, k2 = 0.01, g = 0.8),
quiet=TRUE)
plot(m.L2.DFOP)
-summary(m.L2.DFOP)
s.m.L2.DFOP <- summary(m.L2.DFOP)
s.m.L2.DFOP$errmin
@
@@ -219,7 +217,7 @@ FOCUS_2006_L3_mkin <- mkin_wide_to_long(FOCUS_2006_L3)
SFO model, summary and plot:
<<L3_SFO, echo=TRUE, fig=TRUE>>=
-m.L3.SFO <- mkinfit(SFO, FOCUS_2006_L3_mkin, quiet=TRUE)
+m.L3.SFO <- mkinfit(SFO, FOCUS_2006_L3_mkin, quiet = TRUE)
summary(m.L3.SFO)
plot(m.L3.SFO)
@
@@ -230,7 +228,7 @@ does not fit very well.
The FOMC model performs better:
<<L3_FOMC, echo=TRUE, fig=TRUE>>=
-m.L3.FOMC <- mkinfit(FOMC, FOCUS_2006_L3_mkin, quiet=TRUE)
+m.L3.FOMC <- mkinfit(FOMC, FOCUS_2006_L3_mkin, quiet = TRUE)
plot(m.L3.FOMC)
s.m.L3.FOMC <- summary(m.L3.FOMC)
s.m.L3.FOMC$errmin
@@ -243,7 +241,7 @@ Fitting the four parameter DFOP model further reduces the $\chi^2$ error level
considerably:
<<L3_DFOP, echo=TRUE, fig=TRUE>>=
-m.L3.DFOP <- mkinfit(DFOP, FOCUS_2006_L3_mkin, quiet=TRUE)
+m.L3.DFOP <- mkinfit(DFOP, FOCUS_2006_L3_mkin, quiet = TRUE)
plot(m.L3.DFOP)
s.m.L3.DFOP <- summary(m.L3.DFOP)
s.m.L3.DFOP$errmin
@@ -267,7 +265,7 @@ FOCUS_2006_L4_mkin <- mkin_wide_to_long(FOCUS_2006_L4)
SFO model, summary and plot:
<<L4_SFO, echo=TRUE, fig=TRUE>>=
-m.L4.SFO <- mkinfit(SFO, FOCUS_2006_L4_mkin, quiet=TRUE)
+m.L4.SFO <- mkinfit(SFO, FOCUS_2006_L4_mkin, quiet = TRUE)
summary(m.L4.SFO)
plot(m.L4.SFO)
@
@@ -278,7 +276,7 @@ fits very well.
The FOMC model for comparison
<<L4_FOMC, echo=TRUE, fig=TRUE>>=
-m.L4.FOMC <- mkinfit(FOMC, FOCUS_2006_L4_mkin, quiet=TRUE)
+m.L4.FOMC <- mkinfit(FOMC, FOCUS_2006_L4_mkin, quiet = TRUE)
plot(m.L4.FOMC)
s.m.L4.FOMC <- summary(m.L4.FOMC)
s.m.L4.FOMC$errmin
@@ -287,9 +285,6 @@ s.m.L4.FOMC$errmin
The error level at which the $\chi^2$ test passes is slightly lower for the FOMC
model. However, the difference appears negligible.
-\bibliographystyle{plainnat}
-\bibliography{references}
-
\section{Kinetic evaluations for parent and metabolites}
\subsection{Laboratory Data for example compound Z}
@@ -300,15 +295,16 @@ report, p.350
<<FOCUS_2006_Z_data, echo=TRUE, eval=TRUE>>=
LOD = 0.5
FOCUS_2006_Z = data.frame(
- t = c(0, 0.04, 0.125, 0.29, 0.54, 1, 2, 3, 4, 7, 10, 14, 21, 42, 61, 96, 124),
- Z0 = c(100, 81.7, 70.4, 51.1, 41.2, 6.6, 4.6, 3.9, 4.6, 4.3, 6.8, 2.9, 3.5,
- 5.3, 4.4, 1.2, 0.7),
- Z1 = c(0, 18.3, 29.6, 46.3, 55.1, 65.7, 39.1, 36, 15.3, 5.6, 1.1, 1.6, 0.6,
- 0.5 * LOD, NA, NA, NA),
- Z2 = c(0, NA, 0.5 * LOD, 2.6, 3.8, 15.3, 37.2, 31.7, 35.6, 14.5, 0.8, 2.1,
- 1.9, 0.5 * LOD, NA, NA, NA),
- Z3 = c(0, NA, NA, NA, NA, 0.5 * LOD, 9.2, 13.1, 22.3, 28.4, 32.5, 25.2, 17.2,
- 4.8, 4.5, 2.8, 4.4))
+ t = c(0, 0.04, 0.125, 0.29, 0.54, 1, 2, 3, 4, 7, 10, 14, 21,
+ 42, 61, 96, 124),
+ Z0 = c(100, 81.7, 70.4, 51.1, 41.2, 6.6, 4.6, 3.9, 4.6, 4.3, 6.8,
+ 2.9, 3.5, 5.3, 4.4, 1.2, 0.7),
+ Z1 = c(0, 18.3, 29.6, 46.3, 55.1, 65.7, 39.1, 36, 15.3, 5.6, 1.1,
+ 1.6, 0.6, 0.5 * LOD, NA, NA, NA),
+ Z2 = c(0, NA, 0.5 * LOD, 2.6, 3.8, 15.3, 37.2, 31.7, 35.6, 14.5,
+ 0.8, 2.1, 1.9, 0.5 * LOD, NA, NA, NA),
+ Z3 = c(0, NA, NA, NA, NA, 0.5 * LOD, 9.2, 13.1, 22.3, 28.4, 32.5,
+ 25.2, 17.2, 4.8, 4.5, 2.8, 4.4))
FOCUS_2006_Z_mkin <- mkin_wide_to_long(FOCUS_2006_Z)
@
@@ -320,10 +316,9 @@ with formation and decline of metabolite Z1 and the pathway from parent
directly to sink included (default in mkin).
<<FOCUS_2006_Z_fits_1, echo=TRUE, fig=TRUE>>=
-debug(mkinmod)
Z.2a <- mkinmod(Z0 = list(type = "SFO", to = "Z1"),
Z1 = list(type = "SFO"))
-m.Z.2a <- mkinfit(Z.2a, FOCUS_2006_Z_mkin)
+m.Z.2a <- mkinfit(Z.2a, FOCUS_2006_Z_mkin, quiet = TRUE)
summary(m.Z.2a, data = FALSE)
plot(m.Z.2a)
@
@@ -340,7 +335,7 @@ A similar result can be obtained when formation fractions are used in the model
Z.2a.ff <- mkinmod(Z0 = list(type = "SFO", to = "Z1"),
Z1 = list(type = "SFO"), use_of_ff = "max")
-m.Z.2a.ff <- mkinfit(Z.2a.ff, FOCUS_2006_Z_mkin)
+m.Z.2a.ff <- mkinfit(Z.2a.ff, FOCUS_2006_Z_mkin, quiet = TRUE)
summary(m.Z.2a.ff, data = FALSE)
plot(m.Z.2a.ff)
@
@@ -355,9 +350,11 @@ are used.
<<FOCUS_2006_Z_fits_3, echo=TRUE, fig=TRUE>>=
Z.3 <- mkinmod(Z0 = list(type = "SFO", to = "Z1", sink = FALSE),
- Z1 = list(type = "SFO"))
-m.Z.3 <- mkinfit(Z.3, FOCUS_2006_Z_mkin, parms.ini = c(k_Z0_Z1 = 0.5))
-m.Z.3 <- mkinfit(Z.3, FOCUS_2006_Z_mkin, solution_type = "deSolve")
+ Z1 = list(type = "SFO"))
+m.Z.3 <- mkinfit(Z.3, FOCUS_2006_Z_mkin, parms.ini = c(k_Z0_Z1 = 0.5),
+ quiet = TRUE)
+m.Z.3 <- mkinfit(Z.3, FOCUS_2006_Z_mkin, solution_type = "deSolve",
+ quiet = TRUE)
summary(m.Z.3, data = FALSE)
plot(m.Z.3)
@
@@ -374,7 +371,8 @@ solution can be chosen, at the cost of a bit more computing time.
Z.4a <- mkinmod(Z0 = list(type = "SFO", to = "Z1", sink = FALSE),
Z1 = list(type = "SFO", to = "Z2"),
Z2 = list(type = "SFO"))
-m.Z.4a <- mkinfit(Z.4a, FOCUS_2006_Z_mkin, parms.ini = c(k_Z0_Z1 = 0.5))
+m.Z.4a <- mkinfit(Z.4a, FOCUS_2006_Z_mkin, parms.ini = c(k_Z0_Z1 = 0.5),
+ quiet = TRUE)
summary(m.Z.4a, data = FALSE)
plot(m.Z.4a)
@
@@ -391,7 +389,7 @@ Z.5 <- mkinmod(Z0 = list(type = "SFO", to = "Z1", sink = FALSE),
Z1 = list(type = "SFO", to = "Z2", sink = FALSE),
Z2 = list(type = "SFO"))
m.Z.5 <- mkinfit(Z.5, FOCUS_2006_Z_mkin,
- parms.ini = c(k_Z0_Z1 = 0.5, k_Z1_Z2 = 0.2))
+ parms.ini = c(k_Z0_Z1 = 0.5, k_Z1_Z2 = 0.2), quiet = TRUE)
summary(m.Z.5, data = FALSE)
plot(m.Z.5)
@
@@ -404,7 +402,8 @@ Z.FOCUS <- mkinmod(Z0 = list(type = "SFO", to = "Z1", sink = FALSE),
Z2 = list(type = "SFO", to = "Z3"),
Z3 = list(type = "SFO"))
m.Z.FOCUS <- mkinfit(Z.FOCUS, FOCUS_2006_Z_mkin,
- parms.ini = c(k_Z0_Z1 = 0.5, k_Z1_Z2 = 0.2, k_Z2_Z3 = 0.3))
+ parms.ini = c(k_Z0_Z1 = 0.5, k_Z1_Z2 = 0.2, k_Z2_Z3 = 0.3),
+ quiet = TRUE)
summary(m.Z.FOCUS, data = FALSE)
plot(m.Z.FOCUS)
@
@@ -420,16 +419,14 @@ mkinresplot(m.Z.FOCUS, "Z2", lpos = "bottomright")
mkinresplot(m.Z.FOCUS, "Z3", lpos = "bottomright")
@
-
As the FOCUS report states, there is a certain tailing of the time course of metabolite
Z3. Also, the time course of the parent compound is not fitted very well using the
SFO model, as residues at a certain low level remain.
Therefore, an additional model is offered here, using the single first-order
-reversible binding (SFORB) model for metabolite Z3. However, the $\chi^2$ error
-level is higher for metabolite Z3 using this model, the covariance matrix is
-not returned, and graphically the fit is not significantly improved. Therefore,
-this appears to be a case of overparamterisation.
+reversible binding (SFORB) model for metabolite Z3. As expected, the $\chi^2$
+error level is lower for metabolite Z3 using this model and the graphical
+fit for Z3 is improved. However, the covariance matrix is not returned.
<<FOCUS_2006_Z_fits_7, echo=TRUE, fig=TRUE>>=
Z.mkin.1 <- mkinmod(Z0 = list(type = "SFO", to = "Z1", sink = FALSE),
@@ -437,18 +434,20 @@ Z.mkin.1 <- mkinmod(Z0 = list(type = "SFO", to = "Z1", sink = FALSE),
Z2 = list(type = "SFO", to = "Z3"),
Z3 = list(type = "SFORB"))
m.Z.mkin.1 <- mkinfit(Z.mkin.1, FOCUS_2006_Z_mkin,
- parms.ini = c(k_Z0_Z1 = 0.5, k_Z1_Z2 = 0.3, k_Z2_Z3 = 0.2))
+ parms.ini = c(k_Z0_Z1 = 0.5, k_Z1_Z2 = 0.3, k_Z2_Z3 = 0.2),
+ quiet = TRUE)
summary(m.Z.mkin.1, data = FALSE)
plot(m.Z.mkin.1)
@
-On the other hand, the model fit for the parent compound can be improved by
-using the SFORB model.
+Therefore, a further stepwise model building is performed starting from the
+stage of parent and one metabolite, starting from the assumption that the model
+fit for the parent compound can be improved by using the SFORB model.
<<FOCUS_2006_Z_fits_8, echo=TRUE, fig=TRUE>>=
Z.mkin.2 <- mkinmod(Z0 = list(type = "SFORB", to = "Z1", sink = FALSE),
Z1 = list(type = "SFO"))
-m.Z.mkin.2 <- mkinfit(Z.mkin.2, FOCUS_2006_Z_mkin)
+m.Z.mkin.2 <- mkinfit(Z.mkin.2, FOCUS_2006_Z_mkin, quiet = TRUE)
summary(m.Z.mkin.2, data = FALSE)
plot(m.Z.mkin.2)
@
@@ -460,7 +459,7 @@ Then, metabolite Z2 is added.
Z.mkin.3 <- mkinmod(Z0 = list(type = "SFORB", to = "Z1", sink = FALSE),
Z1 = list(type = "SFO", to = "Z2"),
Z2 = list(type = "SFO"))
-m.Z.mkin.3 <- mkinfit(Z.mkin.3, FOCUS_2006_Z_mkin)
+m.Z.mkin.3 <- mkinfit(Z.mkin.3, FOCUS_2006_Z_mkin, quiet = TRUE)
summary(m.Z.mkin.3, data = FALSE)
plot(m.Z.mkin.3)
@
@@ -474,18 +473,33 @@ Z.mkin.4 <- mkinmod(Z0 = list(type = "SFORB", to = "Z1", sink = FALSE),
Z2 = list(type = "SFO", to = "Z3"),
Z3 = list(type = "SFO"))
m.Z.mkin.4 <- mkinfit(Z.mkin.4, FOCUS_2006_Z_mkin,
- parms.ini = c(k_Z1_Z2 = 0.05))
+ parms.ini = c(k_Z1_Z2 = 0.05), quiet = TRUE)
summary(m.Z.mkin.4, data = FALSE)
plot(m.Z.mkin.4)
@
-The error level of the fit, but especially of metabolite Z3, can
-be improved if the SFORB model is chosen for this metabolite, as this
-model is capable of representing the tailing of the metabolite
-decline phase.
+The error level of the fit, but especially of metabolite Z3, can be improved if
+the SFORB model is chosen for this metabolite, as this model is capable of
+representing the tailing of the metabolite decline phase.
+
+Using the SFORB additionally for Z1 or Z2 did not further improve the result.
+Therefore, the model \texttt{Z.mkin.5} is proposed as the best-fit model
+for the dataset from Appendix 7 of the FOCUS report.
+
+<<FOCUS_2006_Z_fits_11, echo=TRUE, fig=TRUE>>=
+Z.mkin.5 <- mkinmod(Z0 = list(type = "SFORB", to = "Z1", sink = FALSE),
+ Z1 = list(type = "SFO", to = "Z2", sink = FALSE),
+ Z2 = list(type = "SFO", to = "Z3"),
+ Z3 = list(type = "SFORB"))
+m.Z.mkin.5 <- mkinfit(Z.mkin.5, FOCUS_2006_Z_mkin,
+ parms.ini = c(k_Z1_Z2 = 0.2), quiet = TRUE)
+summary(m.Z.mkin.5, data = FALSE)
+plot(m.Z.mkin.5)
+@
+
+\bibliographystyle{plainnat}
+\bibliography{references}
-Using the SFORB additionally for Z1 or Z2 did not further improve
-the result.
\end{document}
% vim: set foldmethod=syntax:
diff --git a/vignettes/examples.pdf b/vignettes/examples.pdf
index 92375460..3bd0c37e 100644
--- a/vignettes/examples.pdf
+++ b/vignettes/examples.pdf
Binary files differ
diff --git a/vignettes/mkin.pdf b/vignettes/mkin.pdf
index 9738230e..073e3610 100644
--- a/vignettes/mkin.pdf
+++ b/vignettes/mkin.pdf
Binary files differ
diff --git a/vignettes/run.bat b/vignettes/run.bat
deleted file mode 100644
index c28c6663..00000000
--- a/vignettes/run.bat
+++ /dev/null
@@ -1,5 +0,0 @@
-R.exe -e "Sweave('mkin.Rnw', stylepath=FALSE)"
-pdflatex.exe mkin
-bibtex.exe mkin
-pdflatex.exe mkin
-pdflatex.exe mkin

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