Possibility to fit without parameter transformation

2 files changed, 51 insertions, 24 deletions

diff --git a/R/mkinfit.R b/R/mkinfit.R
index 326f404..a623146 100644
--- a/R/mkinfit.R
+++ b/R/mkinfit.R
@@ -30,6 +30,8 @@ mkinfit <- function(mkinmod, observed,
   method.ode = "lsoda",

   method.modFit = "Marq",

   control.modFit = list(),

+  transform_rates = TRUE,

+  transform_fractions = TRUE,

   plot = FALSE, quiet = FALSE,

   err = NULL, weight = "none", scaleVar = FALSE,

   atol = 1e-8, rtol = 1e-10, n.outtimes = 100,

@@ -83,7 +85,9 @@ mkinfit <- function(mkinmod, observed,
   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)

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

+                                       transform_rates = transform_rates,

+                                       transform_fractions = transform_fractions)

 

   # Parameters to be optimised:

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

@@ -156,7 +160,9 @@ mkinfit <- function(mkinmod, observed,
 

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

 

-    parms <- backtransform_odeparms(odeparms, mod_vars)

+    parms <- backtransform_odeparms(odeparms, mod_vars,

+                                    transform_rates = transform_rates,

+                                    transform_fractions = transform_fractions)

 

     # Solve the system with current transformed parameter values

     out <- mkinpredict(mkinmod, parms, odeini, outtimes, 

@@ -241,6 +247,8 @@ mkinfit <- function(mkinmod, observed,
 

   # We need to return some more data for summary and plotting

   fit$solution_type <- solution_type

+  fit$transform_rates <- transform_rates

+  fit$transform_fractions <- transform_fractions

 

   # We also need the model for summary and plotting

   fit$mkinmod <- mkinmod

@@ -252,19 +260,27 @@ mkinfit <- function(mkinmod, observed,
 

   # Collect initial parameter values in two dataframes

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

-		  backtransform_odeparms(parms.optim, mod_vars)))

+		  backtransform_odeparms(parms.optim, mod_vars,

+                             transform_rates = transform_rates,

+                             transform_fractions = transform_fractions)))

   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, 

-      backtransform_odeparms(parms.fixed, mod_vars)))

+      backtransform_odeparms(parms.fixed, mod_vars,

+                             transform_rates = transform_rates,

+                             transform_fractions = transform_fractions)))

   fit$fixed$type = c(rep("state", length(state.ini.fixed)), 

                      rep("deparm", length(parms.fixed)))

 

-  bparms.optim = backtransform_odeparms(fit$par, mod_vars)

+  bparms.optim = backtransform_odeparms(fit$par, mod_vars,

+                                        transform_rates = transform_rates,

+                                        transform_fractions = transform_fractions)

   bparms.fixed = backtransform_odeparms(c(state.ini.fixed, parms.fixed), 

-                                        mod_vars)

+                                        mod_vars,

+                                        transform_rates = transform_rates,

+                                        transform_fractions = transform_fractions)

   bparms.all = c(bparms.optim, bparms.fixed)

 

   # Collect observed, predicted, residuals and weighting

@@ -328,8 +344,10 @@ summary.mkinfit <- function(object, data = TRUE, distimes = TRUE, alpha = 0.05,
     par.lower <- par.upper <- object$par

     par.lower[pname] <- param[pname, "Lower"]

     par.upper[pname] <- param[pname, "Upper"]

-    blci[pname] <- backtransform_odeparms(par.lower, mod_vars)[pname]

-    buci[pname] <- backtransform_odeparms(par.upper, mod_vars)[pname]

+    blci[pname] <- backtransform_odeparms(par.lower, mod_vars, 

+                                          object$transform_rates, object$transform_fractions)[pname]

+    buci[pname] <- backtransform_odeparms(par.upper, mod_vars,

+                                          object$transform_rates, object$transform_fractions)[pname]

   }

   bparam <- cbind(object$bparms.optim, blci, buci)

   dimnames(bparam) <- list(pnames, c("Estimate", "Lower", "Upper"))

diff --git a/R/transform_odeparms.R b/R/transform_odeparms.R
index f56478f..31200c7 100644
--- a/R/transform_odeparms.R
+++ b/R/transform_odeparms.R
@@ -1,6 +1,4 @@
-# $Id$

-

-# Copyright (C) 2010-2013 Johannes Ranke

+# Copyright (C) 2010-2014 Johannes Ranke

 # Contact: jranke@uni-bremen.de

 

 # This file is part of the R package mkin

@@ -18,17 +16,21 @@
 # You should have received a copy of the GNU General Public License along with

 # this program. If not, see <http://www.gnu.org/licenses/>

 

-transform_odeparms <- function(parms, mod_vars) {

+transform_odeparms <- function(parms, mod_vars, 

+                               transform_rates = TRUE, 

+                               transform_fractions = TRUE) 

+{

   # Set up container for transformed parameters

   transparms <- parms

 

-  # Log transformation for rate constants

+  # Log transformation for rate constants if requested

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

   if (length(index_k) > 0) {

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

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

+    else transparms[index_k] <- parms[index_k]

   }

 

-  # Go through state variables and apply isotropic logratio transformation

+  # Go through state variables and apply isotropic logratio transformation if requested

   for (box in mod_vars) {

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

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

@@ -37,32 +39,38 @@ transform_odeparms <- function(parms, mod_vars) {
       f <- parms[indices_f]

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

       names(trans_f) <- f_names

-      transparms[indices_f] <- trans_f

+      if(transform_fractions) transparms[indices_f] <- trans_f

+      else transparms[indices_f] <- f

     }

   }

 

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

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

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

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

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

+      transparms[pname] <- ifelse(transform_rates, log(parms[pname]), parms[pname])

+      transparms[pname] <- ifelse(transform_rates, log(parms[pname]), parms[pname])

     } 

   }

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

     g <- parms["g"]

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

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

   }

 

   return(transparms)

 }

 

-backtransform_odeparms <- function(transparms, mod_vars) {

+backtransform_odeparms <- function(transparms, mod_vars,

+                                   transform_rates = TRUE,

+                                   transform_fractions = TRUE)

+{

   # Set up container for backtransformed parameters

   parms <- transparms

 

   # Exponential transformation for rate constants

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

   if (length(index_k) > 0) {

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

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

+    else parms[index_k] <- transparms[index_k]

   }

 

   # Go through state variables and apply inverse isotropic logratio transformation

@@ -73,19 +81,20 @@ backtransform_odeparms <- function(transparms, mod_vars) {
     if (n_paths > 0) {

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

       names(f) <- f_names

-      parms[indices_f] <- f

+      if(transform_fractions) parms[indices_f] <- f

+      else parms[indices_f] <- transparms[indices_f]

     }

   }

 

   # 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])

+      parms[pname] <- ifelse(transform_rates, exp(transparms[pname]), transparms[pname])

     } 

   }

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

     g <- transparms["g"]

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

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

   }

 

   return(parms)