Fork the gmkin GUI from mkin. See ChangeLog for details

1 files changed, 0 insertions, 85 deletions

diff --git a/R/mkinerrmin.R b/R/mkinerrmin.R
deleted file mode 100644
index 074cc56..0000000
--- a/R/mkinerrmin.R
+++ /dev/null
@@ -1,85 +0,0 @@
-# $Id$

-

-# Copyright (C) 2010-2013 Johannes Ranke

-# Contact: jranke@uni-bremen.de

-

-# This file is part of the R package mkin

-

-# mkin is free software: you can redistribute it and/or modify it under the

-# terms of the GNU General Public License as published by the Free Software

-# Foundation, either version 3 of the License, or (at your option) any later

-# version.

-

-# This program is distributed in the hope that it will be useful, but WITHOUT

-# ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS

-# FOR A PARTICULAR PURPOSE.  See the GNU General Public License for more

-# details.

-

-# You should have received a copy of the GNU General Public License along with

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

-if(getRversion() >= '2.15.1') utils::globalVariables(c("name", "value_mean"))

-

-mkinerrmin <- function(fit, alpha = 0.05)

-{

-  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

-  

-    err.min <- sqrt((1 / qchisq(1 - alpha, df)) *

-               sum((errdata$value_mean - errdata$value_pred)^2)/(means.mean^2))

-

-    return(list(err.min = err.min, n.optim = n.parms, df = df))

-  }

-

-  means <- aggregate(value ~ time + name, data = fit$observed, mean, na.rm=TRUE)

-  errdata <- merge(means, fit$predicted, by = c("time", "name"), 

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

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

-

-  # Any value that is set to exactly zero is not really an observed value

-  # Remove those at time 0 - those are caused by the FOCUS recommendation

-  # to set metabolites occurring at time 0 to 0

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

-

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

-

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

-  errmin <- data.frame(err.min = errmin.overall$err.min,

-    n.optim = errmin.overall$n.optim, df = errmin.overall$df)

-  rownames(errmin) <- "All data"

-

-  for (obs_var in fit$obs_vars)

-  {

-    errdata.var <- subset(errdata, name == obs_var)

-

-    # Check if initial value is optimised

-    n.initials.optim <- length(grep(paste(obs_var, ".*", "_0", sep=""), names(parms.optim)))

-

-    # Rate constants are attributed to the source variable

-    n.k.optim <- length(grep(paste("^k", obs_var, sep="_"), names(parms.optim)))

-

-    # Formation fractions are attributed to the target variable

-    n.ff.optim <- length(grep(paste("^f", ".*", obs_var, "$", sep=""), names(parms.optim)))

-

-    n.optim <- n.k.optim + n.initials.optim + n.ff.optim

-

-    # FOMC, DFOP and HS parameters are only counted if we are looking at the

-    # first variable in the model which is always the source variable

-    if (obs_var == fit$obs_vars[[1]]) {

-      if ("alpha" %in% names(parms.optim)) n.optim <- n.optim + 1

-      if ("beta" %in% names(parms.optim)) n.optim <- n.optim + 1

-      if ("k1" %in% names(parms.optim)) n.optim <- n.optim + 1

-      if ("k2" %in% names(parms.optim)) n.optim <- n.optim + 1

-      if ("g" %in% names(parms.optim)) n.optim <- n.optim + 1

-      if ("tb" %in% names(parms.optim)) n.optim <- n.optim + 1

-    }

-

-    # Calculate and add a line to the results

-    errmin.tmp <- kinerrmin(errdata.var, n.optim)

-    errmin[obs_var, c("err.min", "n.optim", "df")] <- errmin.tmp

-  }

-

-  return(errmin)

-}