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diff --git a/man/mkinfit.Rd b/man/mkinfit.Rd index db3f5f3e..97dffef7 100644 --- a/man/mkinfit.Rd +++ b/man/mkinfit.Rd @@ -3,10 +3,6 @@ \name{mkinfit} \alias{mkinfit} \title{Fit a kinetic model to data with one or more state variables} -\source{ -Rocke, David M. und Lorenzato, Stefan (1995) A two-component model - for measurement error in analytical chemistry. Technometrics 37(2), 176-184. -} \usage{ mkinfit( mkinmod, @@ -53,16 +49,16 @@ not observed in degradation data, because there is a lower limit of detection.} \item{parms.ini}{A named vector of initial values for the parameters, - including parameters to be optimised and potentially also fixed parameters - as indicated by \code{fixed_parms}. If set to "auto", initial values for - rate constants are set to default values. Using parameter names that are - not in the model gives an error. +including parameters to be optimised and potentially also fixed parameters +as indicated by \code{fixed_parms}. If set to "auto", initial values for +rate constants are set to default values. Using parameter names that are +not in the model gives an error. - It is possible to only specify a subset of the parameters that the model - needs. You can use the parameter lists "bparms.ode" from a previously - fitted model, which contains the differential equation parameters from - this model. This works nicely if the models are nested. An example is - given below.} +It is possible to only specify a subset of the parameters that the model +needs. You can use the parameter lists "bparms.ode" from a previously +fitted model, which contains the differential equation parameters from +this model. This works nicely if the models are nested. An example is +given below.} \item{state.ini}{A named vector of initial values for the state variables of the model. In case the observed variables are represented by more than one @@ -141,48 +137,50 @@ is 1e-8, lower than in \code{\link{lsoda}}.} is 1e-10, much lower than in \code{\link{lsoda}}.} \item{error_model}{If the error model is "const", a constant standard - deviation is assumed. +deviation is assumed. - If the error model is "obs", each observed variable is assumed to have its - own variance. +If the error model is "obs", each observed variable is assumed to have its +own variance. - If the error model is "tc" (two-component error model), a two component - error model similar to the one described by Rocke and Lorenzato (1995) is - used for setting up the likelihood function. Note that this model - deviates from the model by Rocke and Lorenzato, as their model implies - that the errors follow a lognormal distribution for large values, not a - normal distribution as assumed by this method.} +If the error model is "tc" (two-component error model), a two component +error model similar to the one described by Rocke and Lorenzato (1995) is +used for setting up the likelihood function. Note that this model +deviates from the model by Rocke and Lorenzato, as their model implies +that the errors follow a lognormal distribution for large values, not a +normal distribution as assumed by this method.} \item{error_model_algorithm}{If "auto", the selected algorithm depends on - the error model. If the error model is "const", unweighted nonlinear - least squares fitting ("OLS") is selected. If the error model is "obs", or - "tc", the "d_3" algorithm is selected. - - The algorithm "d_3" will directly minimize the negative log-likelihood and - - independently - also use the three step algorithm described below. The - fit with the higher likelihood is returned. +the error model. If the error model is "const", unweighted nonlinear +least squares fitting ("OLS") is selected. If the error model is "obs", or +"tc", the "d_3" algorithm is selected. + +The algorithm "d_3" will directly minimize the negative log-likelihood and +\itemize{ +\item independently - also use the three step algorithm described below. The +fit with the higher likelihood is returned. +} - The algorithm "direct" will directly minimize the negative log-likelihood. +The algorithm "direct" will directly minimize the negative log-likelihood. - The algorithm "twostep" will minimize the negative log-likelihood after an - initial unweighted least squares optimisation step. +The algorithm "twostep" will minimize the negative log-likelihood after an +initial unweighted least squares optimisation step. - The algorithm "threestep" starts with unweighted least squares, then - optimizes only the error model using the degradation model parameters - found, and then minimizes the negative log-likelihood with free - degradation and error model parameters. +The algorithm "threestep" starts with unweighted least squares, then +optimizes only the error model using the degradation model parameters +found, and then minimizes the negative log-likelihood with free +degradation and error model parameters. - The algorithm "fourstep" starts with unweighted least squares, then - optimizes only the error model using the degradation model parameters - found, then optimizes the degradation model again with fixed error model - parameters, and finally minimizes the negative log-likelihood with free - degradation and error model parameters. +The algorithm "fourstep" starts with unweighted least squares, then +optimizes only the error model using the degradation model parameters +found, then optimizes the degradation model again with fixed error model +parameters, and finally minimizes the negative log-likelihood with free +degradation and error model parameters. - The algorithm "IRLS" (Iteratively Reweighted Least Squares) starts with - unweighted least squares, and then iterates optimization of the error - model parameters and subsequent optimization of the degradation model - using those error model parameters, until the error model parameters - converge.} +The algorithm "IRLS" (Iteratively Reweighted Least Squares) starts with +unweighted least squares, and then iterates optimization of the error +model parameters and subsequent optimization of the degradation model +using those error model parameters, until the error model parameters +converge.} \item{reweight.tol}{Tolerance for the convergence criterion calculated from the error model parameters in IRLS fits.} @@ -196,7 +194,7 @@ the error model parameters in IRLS fits.} } \value{ A list with "mkinfit" in the class attribute. A summary can be - obtained by \code{\link{summary.mkinfit}}. +obtained by \code{\link{summary.mkinfit}}. } \description{ This function maximises the likelihood of the observed data using the Port @@ -214,9 +212,9 @@ estimators. } \note{ When using the "IORE" submodel for metabolites, fitting with - "transform_rates = TRUE" (the default) often leads to failures of the - numerical ODE solver. In this situation it may help to switch off the - internal rate transformation. +"transform_rates = TRUE" (the default) often leads to failures of the +numerical ODE solver. In this situation it may help to switch off the +internal rate transformation. } \examples{ @@ -268,7 +266,7 @@ fit.SFORB_SFO <- mkinfit(SFORB_SFO, FOCUS_2006_D, parms.ini = fit.SFORB$bparms.o } \dontrun{ -# Weighted fits, including IRLS +# Weighted fits, including IRLS (error_model = "obs") SFO_SFO.ff <- mkinmod(parent = mkinsub("SFO", "m1"), m1 = mkinsub("SFO"), use_of_ff = "max") f.noweight <- mkinfit(SFO_SFO.ff, FOCUS_2006_D, quiet = TRUE) @@ -281,15 +279,23 @@ summary(f.tc) } +\references{ +Rocke DM and Lorenzato S (1995) A two-component model +for measurement error in analytical chemistry. \emph{Technometrics} 37(2), 176-184. + +Ranke J and Meinecke S (2019) Error Models for the Kinetic Evaluation of Chemical +Degradation Data. \emph{Environments} 6(12) 124 +\href{https://doi.org/10.3390/environments6120124}{doi:10.3390/environments6120124}. +} \seealso{ Plotting methods \code{\link{plot.mkinfit}} and - \code{\link{mkinparplot}}. +\code{\link{mkinparplot}}. - Comparisons of models fitted to the same data can be made using - \code{\link{AIC}} by virtue of the method \code{\link{logLik.mkinfit}}. +Comparisons of models fitted to the same data can be made using +\code{\link{AIC}} by virtue of the method \code{\link{logLik.mkinfit}}. - Fitting of several models to several datasets in a single call to - \code{\link{mmkin}}. +Fitting of several models to several datasets in a single call to +\code{\link{mmkin}}. } \author{ Johannes Ranke |