diff options
Diffstat (limited to 'man')
| -rw-r--r-- | man/mkinfit.Rd | 28 | ||||
| -rw-r--r-- | man/sigma_rl.Rd | 25 |
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. +} |