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Diffstat (limited to 'man/mkinfit.Rd')
| -rw-r--r-- | man/mkinfit.Rd | 46 |
1 files changed, 17 insertions, 29 deletions
diff --git a/man/mkinfit.Rd b/man/mkinfit.Rd index 228bab24..59bb5e5f 100644 --- a/man/mkinfit.Rd +++ b/man/mkinfit.Rd @@ -35,8 +35,7 @@ mkinfit(mkinmod, observed, tc = c(sigma_low = 0.5, rsd_high = 0.07), scaleVar = FALSE, atol = 1e-8, rtol = 1e-10, n.outtimes = 100, - reweight.method = NULL, - reweight.tol = 1e-8, reweight.max.iter = 10, + error_model = c("auto", "obs", "tc", "const"), trace_parms = FALSE, ...) } \arguments{ @@ -202,33 +201,22 @@ mkinfit(mkinmod, observed, the numerical solver if that is used (see \code{solution_type} 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, - 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 an error model similar to - the one described by - 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. Note that this method 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{reweight.tol}{ - Tolerance for convergence criterion for the variance components - in IRLS fits. - } - \item{reweight.max.iter}{ - Maximum iterations in IRLS fits. + \item{error_model}{ + If the error model is "auto", the generalised error model described by Ranke + et al. (2019) is used for specifying the likelihood function. Simplications + of this error model are tested as well and the model yielding the lowest + AIC is returned. + + 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). + When using this method, a two component error model similar to the + one described by Rocke and Lorenzato (1995) is used for setting up + the likelihood function, as described in the abovementioned paper. + 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{trace_parms}{ Should a trace of the parameter values be listed? |