- Completion of the multicompartment part of the mkin examples vignette

- Fix to chi2 error level calculation by correctly returning backtransformed
  parameters as bparms.optim and bparms.fixed
- Adaptations of unit tests, summary and plot functions



git-svn-id: svn+ssh://svn.r-forge.r-project.org/svnroot/kinfit/pkg/mkin@67 edb9625f-4e0d-4859-8d74-9fd3b1da38cb

11 files changed, 100 insertions, 88 deletions

diff --git a/DESCRIPTION b/DESCRIPTION
index 8db0fc8..808458c 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 98e290d..163dc8d 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 49538e3..8b1e9b2 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 e084cf1..3ca3899 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 fc8ecf7..59ef861 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 26007e7..784054b 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 d34116f..0000000
--- a/vignettes/examples-L1_SFO_plots.pdf
+++ /dev/null
diff --git a/vignettes/examples.Rnw b/vignettes/examples.Rnw
index 340d75f..6f3cfc9 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 9237546..3bd0c37 100644
--- a/vignettes/examples.pdf
+++ b/vignettes/examples.pdf
diff --git a/vignettes/mkin.pdf b/vignettes/mkin.pdf
index 9738230..073e361 100644
--- a/vignettes/mkin.pdf
+++ b/vignettes/mkin.pdf
diff --git a/vignettes/run.bat b/vignettes/run.bat
deleted file mode 100644
index c28c666..0000000
--- 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