Possibility to fit without parameter transformation

1 files changed, 26 insertions, 8 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"))