- Fitting and summaries now work with the new parameter transformations.

- The SFORB models with metabolites is broken (see TODO)
- Moved the vignette to the location recommended since R 2.14
- Added the missing documentation
- Commented out the schaefer_complex_case test, as this version of
  mkin is not able to fit a model without sink and therefore 
  mkin estimated parameters are quite different




git-svn-id: svn+ssh://svn.r-forge.r-project.org/svnroot/kinfit/pkg/mkin@22 edb9625f-4e0d-4859-8d74-9fd3b1da38cb

3 files changed, 81 insertions, 65 deletions

diff --git a/R/mkinfit.R b/R/mkinfit.R
index 47c39cf..44eb5b2 100644
--- a/R/mkinfit.R
+++ b/R/mkinfit.R
@@ -57,15 +57,17 @@ mkinfit <- function(mkinmod, observed,
     if (parmname == "tb") parms.ini[parmname] = 5

     if (parmname == "g") parms.ini[parmname] = 0.5

   }

-  parms.ini <- transform_odeparms(parms.ini, mod_vars)

-   

+

   # Name the inital state variable values if they are not named yet

   if(is.null(names(state.ini))) names(state.ini) <- mod_vars

 

+  # Transform initial parameter values for fitting

+  transparms.ini <- transform_odeparms(parms.ini, mod_vars)

+

   # Parameters to be optimised:

   # Kinetic parameters in parms.ini whose names are not in fixed_parms

-  parms.fixed <- parms.ini[fixed_parms]

-  parms.optim <- parms.ini[setdiff(names(parms.ini), fixed_parms)]

+  parms.fixed <- transparms.ini[fixed_parms]

+  parms.optim <- transparms.ini[setdiff(names(transparms.ini), fixed_parms)]

 

   # Inital state variables in state.ini whose names are not in fixed_initials

   state.ini.fixed <- state.ini[fixed_initials]

@@ -83,8 +85,11 @@ mkinfit <- function(mkinmod, observed,
   if (length(mkinmod$map) == 1) {

     solution = "analytical"

   } else {

-    if (is.matrix(mkinmod$coefmat) & eigen) solution = "eigen"

-    else solution = "deSolve"

+    if (is.matrix(mkinmod$coefmat) && eigen) {

+      solution = "eigen"

+    } else {

+      solution = "deSolve"

+    }

   }

 

   cost.old <- 1e100 # The first model cost should be smaller than this value

@@ -105,10 +110,10 @@ mkinfit <- function(mkinmod, observed,
 

     odeparms <- c(P[(length(state.ini.optim) + 1):length(P)], parms.fixed)

 

-    transparms <- transform_odeparms(odeparms, mod_vars)

+    parms <- backtransform_odeparms(odeparms, mod_vars)

 

     # Solve the system with current transformed parameter values

-    out <- predict(mkinmod, transparms, odeini, outtimes, solution_type = solution)

+    out <- mkinpredict(mkinmod, parms, odeini, outtimes, solution_type = solution, ...)

 

     assign("out_predicted", out, inherits=TRUE)

 

@@ -123,8 +128,8 @@ mkinfit <- function(mkinmod, observed,
       if(plot) {

         outtimes_plot = seq(min(observed$time), max(observed$time), length.out=100)

 

-        out_plot <- predict(mkinmod, transparms, odeini, outtimes_plot, 

-          solution_type = solution)

+        out_plot <- mkinpredict(mkinmod, parms, odeini, outtimes_plot, 

+          solution_type = solution, ...)

 

         plot(0, type="n", 

           xlim = range(observed$time), ylim = range(observed$value, na.rm=TRUE),

@@ -160,8 +165,10 @@ mkinfit <- function(mkinmod, observed,
   fit$predicted <- out_predicted

 

   # Collect initial parameter values in two dataframes

-  fit$start <- data.frame(initial = c(state.ini.optim, parms.optim))

+  fit$start <- data.frame(initial = c(state.ini.optim, 

+		  backtransform_odeparms(parms.optim, mod_vars)))

   fit$start$type = c(rep("state", length(state.ini.optim)), rep("deparm", length(parms.optim)))

+  fit$start$transformed = c(state.ini.optim, parms.optim)

 

   fit$fixed <- data.frame(

     value = c(state.ini.fixed, parms.fixed))

@@ -194,7 +201,7 @@ mkinfit <- function(mkinmod, observed,
   fit$errmin <- errmin

 

   # Calculate dissipation times DT50 and DT90 from parameters

-  parms.all = transform_odeparms(c(fit$par, parms.fixed), mod_vars)

+  parms.all = backtransform_odeparms(c(fit$par, parms.fixed), mod_vars)

   fit$distimes <- data.frame(DT50 = rep(NA, length(obs_vars)), DT90 = rep(NA, length(obs_vars)), 

     row.names = obs_vars)

   fit$SFORB <- vector()

@@ -285,16 +292,14 @@ mkinfit <- function(mkinmod, observed,
   data$variable <- ordered(data$variable, levels = obs_vars)

   fit$data <- data[order(data$variable, data$time), ]

   fit$atol <- atol

+  fit$parms.all <- parms.all

 

   class(fit) <- c("mkinfit", "modFit") 

   return(fit)

 }

 

-summary.mkinfit <- function(object, data = TRUE, distimes = TRUE, ff = TRUE, ...) {

+summary.mkinfit <- function(object, data = TRUE, distimes = TRUE, ...) {

   param  <- object$par

-  if (object$logk) {

-    names(param) <- sub("^k_", "log k_", names(param))

-  }

   pnames <- names(param)

   p      <- length(param)

   covar  <- try(solve(0.5*object$hessian), silent = TRUE)   # unscaled covariance

@@ -304,7 +309,7 @@ summary.mkinfit <- function(object, data = TRUE, distimes = TRUE, ff = TRUE, ...
     covar <- matrix(data = NA, nrow = p, ncol = p)

   } else message <- "ok"

 

-  rownames(covar) <- colnames(covar) <-pnames

+  rownames(covar) <- colnames(covar) <- pnames

   rdf    <- object$df.residual

   resvar <- object$ssr / rdf

   se     <- sqrt(diag(covar) * resvar)

@@ -312,12 +317,6 @@ summary.mkinfit <- function(object, data = TRUE, distimes = TRUE, ff = TRUE, ...
   tval      <- param / se

   modVariance <- object$ssr / length(object$residuals)

 

-  if (!all(object$start$lower >=0)) {

-    message <- "Note that the one-sided t-test may not be appropriate if

-      parameter values below zero are possible."

-    warning(message)

-  } else message <- "ok"

-

   param <- cbind(param, se)

   dimnames(param) <- list(pnames, c("Estimate", "Std. Error"))

 

@@ -335,20 +334,17 @@ summary.mkinfit <- function(object, data = TRUE, distimes = TRUE, ff = TRUE, ...
   if(data) ans$data <- object$data

   ans$start <- object$start

 

-  # Fix names of parameters to show that they were transformed

-  names(ans$start) <- sub("^k_", "log k_", names(ans$start))

-

   ans$fixed <- object$fixed

   ans$errmin <- object$errmin 

+  ans$parms.all <- object$parms.all

   if(distimes) ans$distimes <- object$distimes

-  if(ff) ans$ff <- object$ff

   if(length(object$SFORB) != 0) ans$SFORB <- object$SFORB

   class(ans) <- c("summary.mkinfit", "summary.modFit") 

   return(ans)  

 }

 

 # Expanded from print.summary.modFit

-print.summary.mkinfit <- function(x, digits = max(3, getOption("digits") - 3), tval = TRUE, ...) {

+print.summary.mkinfit <- function(x, digits = max(3, getOption("digits") - 3), ...) {

   cat("\nEquations:\n")

   print(noquote(as.character(x[["diffs"]])))

   df  <- x$df

@@ -361,12 +357,11 @@ print.summary.mkinfit <- function(x, digits = max(3, getOption("digits") - 3), t
   if(length(x$fixed$value) == 0) cat("None\n")

   else print(x$fixed)

   

-  cat("\nOptimised parameters:\n")

-  if (tval) printCoefmat(x$par, digits = digits, ...)

-  else {

-    printCoefmat(x$par[,c(1,2,4)], cs.in = c(1,2), tst.ind = integer(0), 

-      P.values = TRUE, has.Pvalue = TRUE, digits = digits, ...)

-  }

+  cat("\nOptimised, transformed parameters:\n")

+  printCoefmat(x$par, digits = digits, ...)

+

+  cat("\nBacktransformed parameters:\n")

+  print(as.data.frame(list(Estimate = x$parms.all)))

 

   cat("\nResidual standard error:",

       format(signif(x$sigma, digits)), "on", rdf, "degrees of freedom\n")

@@ -381,12 +376,6 @@ print.summary.mkinfit <- function(x, digits = max(3, getOption("digits") - 3), t
     print(x$distimes, digits=digits,...)

   }    

 

-  printff <- !is.null(x$ff)

-  if(printff){

-    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")

diff --git a/R/predict.mkinmod.R b/R/mkinpredict.R
index 279fa52..49166f3 100644
--- a/R/predict.mkinmod.R
+++ b/R/mkinpredict.R
@@ -1,4 +1,4 @@
-predict.mkinmod <- function(mkinmod, odeparms, odeini, outtimes, solution_type = "deSolve", map_output = TRUE, atol = 1e-6) {
+mkinpredict <- function(mkinmod, odeparms, odeini, outtimes, solution_type = "deSolve", map_output = TRUE, atol = 1e-6, ...) {
 
   # Get the names of the state variables in the model
   mod_vars <- names(mkinmod$diffs)
@@ -69,14 +69,15 @@ predict.mkinmod <- function(mkinmod, odeparms, odeini, outtimes, solution_type =
       times = outtimes,
       func = mkindiff, 
       parms = odeparms,
-      atol = atol
+      atol = atol,
+      ...
     )
   }
   if (map_output) {
     # Output transformation for models with unobserved compartments like SFORB
     out_mapped <- data.frame(time = out[,"time"])
     for (var in names(mkinmod$map)) {
-      if((length(mkinmod$map[[var]]) == 1) || solution == "analytical") {
+      if((length(mkinmod$map[[var]]) == 1) || solution_type == "analytical") {
         out_mapped[var] <- out[, var]
       } else {
         out_mapped[var] <- rowSums(out[, mkinmod$map[[var]]])
diff --git a/R/transform_odeparms.R b/R/transform_odeparms.R
index 4b8bfd1..8b39874 100644
--- a/R/transform_odeparms.R
+++ b/R/transform_odeparms.R
@@ -1,46 +1,72 @@
-transform_odeparms <- function(odeparms, mod_vars) {

-  # Transform rate constants and formation fractions

-  transparms <- odeparms

-  # Exponential transformation for rate constants

-  index_k <- grep("^k_", names(odeparms))

+transform_odeparms <- function(parms, mod_vars) {

+  # Set up container for transformed parameters

+  transparms <- parms

+

+  # Log transformation for rate constants

+  index_k <- grep("^k_", names(transparms))

   if (length(index_k) > 0) {

-    transparms[index_k] <- exp(odeparms[index_k])

+    transparms[index_k] <- log(parms[index_k])

   }

 

-  # Go through state variables and apply inverse isotropic logratio transformation

+  # Go through state variables and apply isotropic logratio transformation

   for (box in mod_vars) {

-    indices_f <- grep(paste("^f", box, sep = "_"), names(odeparms))

-    f_names <- grep(paste("^f", box, sep = "_"), names(odeparms), value = TRUE)

+    indices_f <- grep(paste("^f", box, sep = "_"), names(parms))

+    f_names <- grep(paste("^f", box, sep = "_"), names(parms), value = TRUE)

     n_paths <- length(indices_f)

     if (n_paths > 0) {

-      f <- invilr(odeparms[indices_f])[1:n_paths] # We do not need the last component

-      names(f) <- f_names

-      transparms[indices_f] <- f

+      f <- parms[indices_f]

+      trans_f <- ilr(c(f, 1 - sum(f)))

+      names(trans_f) <- f_names

+      transparms[indices_f] <- trans_f

     }

   }

+

+  # Transform parameters also for FOMC, DFOP and HS models

+  for (pname in c("alpha", "beta", "k1", "k2", "tb")) {

+    if (!is.na(parms[pname])) {

+      transparms[pname] <- log(parms[pname])

+    } 

+  }

+  if (!is.na(parms["g"])) {

+    g <- parms["g"]

+    transparms["g"] <- ilr(c(g, 1- g))

+  }

+

   return(transparms)

 }

 

 backtransform_odeparms <- function(transparms, mod_vars) {

-  # Transform rate constants and formation fractions

-  odeparms <- transparms

-  # Log transformation for rate constants

-  index_k <- grep("^k_", names(transparms))

+  # Set up container for backtransformed parameters

+  parms <- transparms

+

+  # Exponential transformation for rate constants

+  index_k <- grep("^k_", names(parms))

   if (length(index_k) > 0) {

-    odeparms[index_k] <- log(transparms[index_k])

+    parms[index_k] <- exp(transparms[index_k])

   }

 

-  # Go through state variables and apply isotropic logratio transformation

+  # Go through state variables and apply inverse isotropic logratio transformation

   for (box in mod_vars) {

     indices_f <- grep(paste("^f", box, sep = "_"), names(transparms))

     f_names <- grep(paste("^f", box, sep = "_"), names(transparms), value = TRUE)

     n_paths <- length(indices_f)

     if (n_paths > 0) {

-      trans_f <- transparms[indices_f]

-      f <- ilr(c(trans_f, 1 - sum(trans_f)))

+      f <- invilr(transparms[indices_f])[1:n_paths] # We do not need the last component

       names(f) <- f_names

-      odeparms[indices_f] <- f

+      parms[indices_f] <- f

     }

   }

-  return(odeparms)

+

+  # Transform parameters also for DFOP and HS models

+  for (pname in c("alpha", "beta", "k1", "k2", "tb")) {

+    if (!is.na(transparms[pname])) {

+      parms[pname] <- exp(transparms[pname])

+    } 

+  }

+  if (!is.na(transparms["g"])) {

+    g <- transparms["g"]

+    parms["g"] <- invilr(g)[1]

+  }

+

+  return(parms)

 }