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diff --git a/CakeErrMin.R b/CakeErrMin.R
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+## -----------------------------------------------------------------------------
+## Chi squared errmin function.
+## -----------------------------------------------------------------------------
+# Some of the CAKE R modules are based on mkin.
+#
+# Modifications developed by Tessella for Syngenta, Copyright (C) 2011-2016 Syngenta
+# Tessella Project Reference: 6245, 7247, 8361, 7414
+#
+# The CAKE R modules are free software: you can
+# redistribute them and/or modify them 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"))
+
+CakeErrMin <- 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$name), ]
+  
+  # Remove values at time zero for variables whose value for state.ini is fixed,
+  # as these will not have any effect in the optimization and should therefore not
+  # be counted as degrees of freedom.
+  fixed_initials = gsub("_0$", "", rownames(subset(fit$fixed, type = "state")))
+  errdata <- subset(errdata, !(time == 0 & name %in% fixed_initials))
+  
+  n.optim.overall <- length(parms.optim)
+
+  errmin.overall <- kinerrmin(errdata, n.optim.overall)
+  errmin <- data.frame(err.min = errmin.overall$err.min,
+    n.optim = errmin.overall$n.optim, df = errmin.overall$df)
+  rownames(errmin) <- "All data"
+
+  for (obs_var in fit$obs_vars)
+  {
+    errdata.var <- subset(errdata, name == obs_var)
+
+    # Check if initial value is optimised
+    n.initials.optim <- length(grep(paste(obs_var, ".*", "_0", sep=""), names(parms.optim)))
+
+    # Rate constants and DFOP parameters are attributed to the source variable
+    n.k.optim <- length(grep(paste("^k", obs_var, sep="_"), names(parms.optim)))
+    n.k1.dfop.optim <- length(grep(paste("^k1", obs_var, sep="_"), names(parms.optim)))
+    n.k2.dfop.optim <- length(grep(paste("^k2", obs_var, sep="_"), names(parms.optim)))
+    n.g.dfop.optim <- length(grep(paste("^g", obs_var, sep="_"), names(parms.optim)))
+
+    # Formation fractions are attributed to the target variable
+    n.ff.optim <- length(grep(paste("^f", ".*", obs_var, "$", sep=""), names(parms.optim)))
+
+    n.optim <- n.k.optim + n.k1.dfop.optim + n.k2.dfop.optim + n.g.dfop.optim + n.initials.optim + n.ff.optim
+
+    # FOMC, HS and IORE 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 ("tb" %in% names(parms.optim)) n.optim <- n.optim + 1
+      if ("N" %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)
+}