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-rw-r--r--man/mkinfit.Rd64
1 files changed, 56 insertions, 8 deletions
diff --git a/man/mkinfit.Rd b/man/mkinfit.Rd
index 445bce2e..a080f8ae 100644
--- a/man/mkinfit.Rd
+++ b/man/mkinfit.Rd
@@ -20,7 +20,9 @@ mkinfit(mkinmod, observed,
plot = FALSE, quiet = FALSE, err = NULL, weight = "none",
scaleVar = FALSE,
atol = 1e-8, rtol = 1e-10, n.outtimes = 100,
- trace_parms, ...)
+ reweight.method = NULL,
+ reweight.tol = 1e-8, reweight.max.iter = 10,
+ trace_parms = FALSE, ...)
}
\arguments{
\item{mkinmod}{
@@ -94,7 +96,7 @@ mkinfit(mkinmod, observed,
\emph{error} estimates, used to weigh the residuals (see details of
\code{\link{modCost}}); if \code{NULL}, then the residuals are not weighed.
}
- \item{weight}{only if \code{err}=\code{NULL}: how to weigh the
+ \item{weight}{only if \code{err}=\code{NULL}: how to weight the
residuals, one of "none", "std", "mean", see details of \code{\link{modCost}}.
}
\item{scaleVar}{
@@ -115,6 +117,23 @@ mkinfit(mkinmod, observed,
the numerical solver if that is used (see \code{solution} argument.
The default value is 100.
}
+ \item{reweight.method}{
+ The method used for iteratively reweighting residuals, also known
+ as iteratively reweighted least squares (IRLS). Default is NULL,
+ the other method implemented is called "obs", meaning that each
+ observed variable is assumed to have its own variance, this is
+ estimated from the fit and used for weighting the residuals
+ in each iteration until convergence of this estimate up to
+ \code{reweight.tol} or up to the maximum number of iterations
+ specified by \code{reweight.maxiter}.
+ }
+ \item{reweight.tol}{
+ Tolerance for convergence criterion for the variance components
+ in IRLS fits.
+ }
+ \item{reweight.max.iter}{
+ Maximum iterations in IRLS fits.
+ }
\item{trace_parms}{
Should a trace of the parameter values be listed?
}
@@ -123,8 +142,14 @@ mkinfit(mkinmod, observed,
}
}
\value{
- A list with "mkinfit" and "modFit" in the class attribute.
- A summary can be obtained by \code{\link{summary.mkinfit}}.
+ A list with "mkinfit" and "modFit" in the class attribute.
+ A summary can be obtained by \code{\link{summary.mkinfit}}.
+}
+\note{
+ The implementation of iteratively reweighted least squares is inspired by the
+ work of the KinGUII team at Bayer Crop Science (Walter Schmitt and Zhenglei
+ Gao). A similar implemention can also be found in CAKE 2.0, which is the
+ other GUI derivative of mkin, sponsored by Syngenta.
}
\author{
Johannes Ranke <jranke@uni-bremen.de>
@@ -136,7 +161,6 @@ SFO_SFO <- mkinmod(
m1 = list(type = "SFO"))
# Fit the model to the FOCUS example dataset D using defaults
fit <- mkinfit(SFO_SFO, FOCUS_2006_D)
-str(fit)
summary(fit)
# Use stepwise fitting, using optimised parameters from parent only fit, FOMC
@@ -147,9 +171,10 @@ FOMC_SFO <- mkinmod(
m1 = list(type = "SFO"))
# Fit the model to the FOCUS example dataset D using defaults
fit.FOMC_SFO <- mkinfit(FOMC_SFO, FOCUS_2006_D)
-# Use starting parameters from parent only FOMC fit (not really needed in this case)
-fit.FOMC = mkinfit(FOMC, FOCUS_2006_D)
-fit.FOMC_SFO <- mkinfit(FOMC_SFO, FOCUS_2006_D, parms.ini = fit.FOMC$bparms.ode, plot=TRUE)
+# Use starting parameters from parent only FOMC fit
+fit.FOMC = mkinfit(FOMC, FOCUS_2006_D, plot=TRUE)
+fit.FOMC_SFO <- mkinfit(FOMC_SFO, FOCUS_2006_D,
+ parms.ini = fit.FOMC$bparms.ode, plot=TRUE)
}
# Use stepwise fitting, using optimised parameters from parent only fit, SFORB
@@ -162,6 +187,29 @@ fit.SFORB_SFO <- mkinfit(SFORB_SFO, FOCUS_2006_D)
# Use starting parameters from parent only SFORB fit (not really needed in this case)
fit.SFORB = mkinfit(SFORB, FOCUS_2006_D)
fit.SFORB_SFO <- mkinfit(SFORB_SFO, FOCUS_2006_D, parms.ini = fit.SFORB$bparms.ode, plot=TRUE)
+
+# Weighted fits, including IRLS
+SFO_SFO.ff <- mkinmod(parent = list(type = "SFO", to = "m1"),
+ m1 = list(type = "SFO"), use_of_ff = "max")
+f.noweight <- mkinfit(SFO_SFO.ff, FOCUS_2006_D)
+summary(f.noweight)
+f.irls <- mkinfit(SFO_SFO.ff, FOCUS_2006_D, reweight.method = "obs")
+summary(f.irls)
+f.w.mean <- mkinfit(SFO_SFO.ff, FOCUS_2006_D, weight = "mean")
+summary(f.w.mean)
+f.w.mean.irls <- mkinfit(SFO_SFO.ff, FOCUS_2006_D, weight = "mean",
+ reweight.method = "obs")
+summary(f.w.mean.irls)
+
+# Manual weighting
+dw <- FOCUS_2006_D
+errors <- c(parent = 2, m1 = 1)
+dw$err.man <- errors[FOCUS_2006_D$name]
+f.w.man <- mkinfit(SFO_SFO.ff, dw, err = "err.man")
+summary(f.w.man)
+f.w.man.irls <- mkinfit(SFO_SFO.ff, dw, err = "err.man",
+ reweight.method = "obs")
+summary(f.w.man.irls)
}
\keyword{ models }
\keyword{ optimize }

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