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-rw-r--r--man/mkinfit.Rd28
-rw-r--r--man/sigma_rl.Rd25
2 files changed, 47 insertions, 6 deletions
diff --git a/man/mkinfit.Rd b/man/mkinfit.Rd
index 8e2fbeb1..32edb28b 100644
--- a/man/mkinfit.Rd
+++ b/man/mkinfit.Rd
@@ -30,7 +30,9 @@ mkinfit(mkinmod, observed,
control.modFit = list(),
transform_rates = TRUE,
transform_fractions = TRUE,
- plot = FALSE, quiet = FALSE, err = NULL, weight = "none",
+ plot = FALSE, quiet = FALSE, err = NULL,
+ weight = c("none", "std", "mean", "tc"),
+ tc = c(sigma_low = 0.5, rsd_high = 0.07),
scaleVar = FALSE,
atol = 1e-8, rtol = 1e-10, n.outtimes = 100,
reweight.method = NULL,
@@ -176,8 +178,11 @@ mkinfit(mkinmod, observed,
}
\item{weight}{
only if \code{err}=\code{NULL}: how to weight the residuals, one of "none",
- "std", "mean", see details of \code{\link{modCost}}.
+ "std", "mean", see details of \code{\link{modCost}}, or "tc" for the
+ two component error model of Rocke and Lorenzato.
}
+ \item{tc}{The two components of the Rocke and Lorenzato error model as used
+ for (initial) weighting}.
\item{scaleVar}{
Will be passed to \code{\link{modCost}}. Default is not to scale Variables
according to the number of observations.
@@ -199,12 +204,19 @@ mkinfit(mkinmod, observed,
\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
+ i.e. no iterative weighting.
+ The first reweighting method is called "obs", meaning that each
+ observed variable is assumed to have its own variance. This variance
+ is estimated from the fit (mean squared residuals) 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.max.iter}.
+ The second reweighting method is called "tc" (two-component error model).
+ When using this method, the two components of the error model according
+ to Rocke and Lorenzato (1995) are estimated from the fit and the resulting
+ variances are used for weighting the residuals in each iteration until
+ convergence of these components or up to the maximum number of iterations
+ specified.
}
\item{reweight.tol}{
Tolerance for convergence criterion for the variance components
@@ -243,6 +255,10 @@ mkinfit(mkinmod, observed,
numerical ODE solver. In this situation it may help to switch off the
internal rate transformation.
}
+\source{
+ Rocke, David M. und Lorenzato, Stefan (1995) A two-component model for
+ measurement error in analytical chemistry. Technometrics 37(2), 176-184.
+}
\author{
Johannes Ranke
}
diff --git a/man/sigma_rl.Rd b/man/sigma_rl.Rd
new file mode 100644
index 00000000..d1c22a77
--- /dev/null
+++ b/man/sigma_rl.Rd
@@ -0,0 +1,25 @@
+\name{sigma_rl}
+\alias{sigma_rl}
+\title{ Two component error model of Rocke and Lorenzato}
+\description{
+ Function describing the standard deviation of the measurement error
+ in dependence of the measured value:
+
+ \eqn{sigma = sqrt(sigma_low^2 + y^2 * rsd_high^2)}
+}
+\usage{
+sigma_rl(y, sigma_low, rsd_high)
+}
+\arguments{
+ \item{y}{ The magnitude of the observed value }
+ \item{sigma_low}{ The asymptotic minimum of the standard deviation for low observed values }
+ \item{rsd_high}{ The coefficient describing the increase of the standard deviation with
+ the magnitude of the observed value }
+}
+\value{
+ The standard deviation of the response variable.
+}
+\references{
+ Rocke, David M. und Lorenzato, Stefan (1995) A two-component model for
+ measurement error in analytical chemistry. Technometrics 37(2), 176-184.
+}

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