- Added a ChangeLog

- Do not use time zero values of 0 for chi2 error level calculations (see changelog)
- Show weighting method in summary
- Correct the output of the data in the case of manual weights
- Some reformatting in mkinfit.R
- GUI: First attempt at representing a fit in the GUI

git-svn-id: svn+ssh://svn.r-forge.r-project.org/svnroot/kinfit/pkg/mkin@108 edb9625f-4e0d-4859-8d74-9fd3b1da38cb

2 files changed, 34 insertions, 16 deletions

diff --git a/R/mkinerrmin.R b/R/mkinerrmin.R
index eb9d49c1..2b499d97 100644
--- a/R/mkinerrmin.R
+++ b/R/mkinerrmin.R
@@ -39,6 +39,7 @@ mkinerrmin <- function(fit, alpha = 0.05)
   errdata <- merge(means, fit$predicted, by = c("time", "name"), 

     suffixes = c("_mean", "_pred"))

   errdata <- errdata[order(errdata$time, errdata$name), ]

+  errdata <- subset(errdata, !(time == 0 & value_mean == 0))

   n.optim.overall <- length(parms.optim)

 

   errmin.overall <- kinerrmin(errdata, n.optim.overall)

diff --git a/R/mkinfit.R b/R/mkinfit.R
index cdf54291..e7b1fb0c 100644
--- a/R/mkinfit.R
+++ b/R/mkinfit.R
@@ -90,18 +90,18 @@ mkinfit <- function(mkinmod, observed,
   # Preserve names of state variables before renaming initial state variable parameters

   state.ini.optim.boxnames <- names(state.ini.optim)

   if(length(state.ini.optim) > 0) {

-      names(state.ini.optim) <- paste(names(state.ini.optim), "0", sep="_")

+    names(state.ini.optim) <- paste(names(state.ini.optim), "0", sep="_")

   }

 

   # Decide if the solution of the model can be based on a simple analytical

   # formula, the spectral decomposition of the matrix (fundamental system)

   # or a numeric ode solver from the deSolve package

   if (!solution_type %in% c("auto", "analytical", "eigen", "deSolve"))

-      stop("solution_type must be auto, analytical, eigen or de Solve")

+     stop("solution_type must be auto, analytical, eigen or de Solve")

   if (solution_type == "analytical" && length(mkinmod$map) > 1)

-      stop("Analytical solution not implemented for models with metabolites.")

+     stop("Analytical solution not implemented for models with metabolites.")

   if (solution_type == "eigen" && !is.matrix(mkinmod$coefmat))

-      stop("Eigenvalue based solution not possible, coefficient matrix not present.")

+     stop("Eigenvalue based solution not possible, coefficient matrix not present.")

   if (solution_type == "auto") {

     if (length(mkinmod$map) == 1) {

       solution_type = "analytical"

@@ -109,10 +109,10 @@ mkinfit <- function(mkinmod, observed,
       if (is.matrix(mkinmod$coefmat)) {

 	solution_type = "eigen"

         if (max(observed$value, na.rm = TRUE) < 0.1) {

-	  stop("The combination of small observed values (all < 0.1) and solution_type = eigen is error-prone")

-	}

+          stop("The combination of small observed values (all < 0.1) and solution_type = eigen is error-prone")

+        }

       } else {

-	solution_type = "deSolve"

+        solution_type = "deSolve"

       }

     }

   }

@@ -130,8 +130,9 @@ mkinfit <- function(mkinmod, observed,
 

     # Time points at which observed data are available

     # Make sure we include time 0, so initial values for state variables are for time 0

-    outtimes = sort(unique(c(observed$time,

-		      seq(min(observed$time), max(observed$time), length.out=n.outtimes))))

+    outtimes = sort(unique(c(observed$time, seq(min(observed$time),

+                                                max(observed$time),

+                                                length.out = n.outtimes))))

 

     if(length(state.ini.optim) > 0) {

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

@@ -143,7 +144,9 @@ mkinfit <- function(mkinmod, observed,
     parms <- backtransform_odeparms(odeparms, mod_vars)

 

     # Solve the system with current transformed parameter values

-    out <- mkinpredict(mkinmod, parms, odeini, outtimes, solution_type = solution_type, atol = atol, rtol = rtol, ...)

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

+                       solution_type = solution_type, 

+                       atol = atol, rtol = rtol, ...)

 

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

 

@@ -169,7 +172,7 @@ mkinfit <- function(mkinmod, observed,
         names(col_obs) <- names(pch_obs) <- names(lty_obs) <- obs_vars

         for (obs_var in obs_vars) {

           points(subset(observed, name == obs_var, c(time, value)), 

-            pch = pch_obs[obs_var], col = col_obs[obs_var])

+                 pch = pch_obs[obs_var], col = col_obs[obs_var])

         }

         matlines(out_plot$time, out_plot[-1], col = col_obs, lty = lty_obs)

         legend("topright", inset=c(0.05, 0.05), legend=obs_vars, 

@@ -180,7 +183,8 @@ mkinfit <- function(mkinmod, observed,
     }

     return(mC)

   }

-  fit <- modFit(cost, c(state.ini.optim, parms.optim), method = method.modFit, control = control.modFit, ...)

+  fit <- modFit(cost, c(state.ini.optim, parms.optim), 

+                method = method.modFit, control = control.modFit, ...)

 

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

   fit$solution_type <- solution_type

@@ -196,25 +200,33 @@ mkinfit <- function(mkinmod, observed,
   # Collect initial parameter values in two dataframes

   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$start$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))

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

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

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

 

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

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

-  names(data) <- c("time", "variable", "observed", "predicted")

+  if (is.null(err)) {

+    names(data) <- c("time", "variable", "observed", "predicted")

+  } else {

+    names(data) <- c("time", "variable", "observed", "err", "predicted")

+  }

   data$residual <- data$observed - data$predicted

   data$variable <- ordered(data$variable, levels = obs_vars)

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

   fit$atol <- atol

   fit$rtol <- rtol

+  fit$weight <- ifelse(is.null(err), weight, "manual")

 

   # Return all backtransformed parameters for summary

   fit$bparms.optim <- bparms.optim 

@@ -270,6 +282,7 @@ summary.mkinfit <- function(object, data = TRUE, distimes = TRUE, alpha = 0.05,
 	  date.summary = date(),

 	  solution_type = object$solution_type,

 	  use_of_ff = object$mkinmod$use_of_ff,

+    weight = object$weight,

     residuals = object$residuals,

     residualVariance = resvar,

     sigma = sqrt(resvar),

@@ -316,6 +329,9 @@ print.summary.mkinfit <- function(x, digits = max(3, getOption("digits") - 3), .
   cat("\nMethod used for solution of differential equation system:\n")

   cat(x$solution_type, "\n")

 

+  cat("\nWeighting method:\n")

+  cat(x$weight, "\n")

+

   cat("\nStarting values for optimised parameters:\n")

   print(x$start)

 

@@ -371,3 +387,4 @@ print.summary.mkinfit <- function(x, digits = max(3, getOption("digits") - 3), .
 

   invisible(x)

 }

+# vim: set ts=2 sw=2 expandtab: