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Diffstat (limited to 'man/mhmkin.Rd')
| -rw-r--r-- | man/mhmkin.Rd | 50 |
1 files changed, 44 insertions, 6 deletions
diff --git a/man/mhmkin.Rd b/man/mhmkin.Rd index 4230e44f..c77f4289 100644 --- a/man/mhmkin.Rd +++ b/man/mhmkin.Rd @@ -43,11 +43,12 @@ supported} \item{no_random_effect}{Default is NULL and will be passed to \link{saem}. If a character vector is supplied, it will be passed to all calls to \link{saem}, -regardless if the corresponding parameter is in the model. Alternatively, -an object of class \link{illparms.mhmkin} can be specified. This has to have -the same dimensions as the return object of the current call. In this way, -ill-defined parameters found in corresponding simpler versions of the -degradation model can be specified.} +which will exclude random effects for all matching parameters. Alternatively, +a list of character vectors or an object of class \link{illparms.mhmkin} can be +specified. They have to have the same dimensions that the return object of +the current call will have, i.e. the number of rows must match the number +of degradation models in the mmkin object(s), and the number of columns must +match the number of error models used in the mmkin object(s).} \item{cores}{The number of cores to be used for multicore processing. This is only used when the \code{cluster} argument is \code{NULL}. On Windows @@ -74,7 +75,7 @@ be indexed using the degradation model names for the first index (row index) and the error model names for the second index (column index), with class attribute 'mhmkin'. -An object of class \code{\link{mhmkin}}. +An object inheriting from \code{\link{mhmkin}}. } \description{ The name of the methods expresses that (\strong{m}ultiple) \strong{h}ierarchichal @@ -82,6 +83,43 @@ The name of the methods expresses that (\strong{m}ultiple) \strong{h}ierarchicha fitted. Our kinetic models are nonlinear, so we can use various nonlinear mixed-effects model fitting functions. } +\examples{ +\dontrun{ +# We start with separate evaluations of all the first six datasets with two +# degradation models and two error models +f_sep_const <- mmkin(c("SFO", "FOMC"), ds_fomc[1:6], cores = 2, quiet = TRUE) +f_sep_tc <- update(f_sep_const, error_model = "tc") +# The mhmkin function sets up hierarchical degradation models aka +# nonlinear mixed-effects models for all four combinations, specifying +# uncorrelated random effects for all degradation parameters +f_saem_1 <- mhmkin(list(f_sep_const, f_sep_tc), cores = 2) +status(f_saem_1) +# The 'illparms' function shows that in all hierarchical fits, at least +# one random effect is ill-defined (the confidence interval for the +# random effect expressed as standard deviation includes zero) +illparms(f_saem_1) +# Therefore we repeat the fits, excluding the ill-defined random effects +f_saem_2 <- update(f_saem_1, no_random_effect = illparms(f_saem_1)) +status(f_saem_2) +illparms(f_saem_2) +# Model comparisons show that FOMC with two-component error is preferable, +# and confirms our reduction of the default parameter model +anova(f_saem_1) +anova(f_saem_2) +# The convergence plot for the selected model looks fine +saemix::plot(f_saem_2[["FOMC", "tc"]]$so, plot.type = "convergence") +# The plot of predictions versus data shows that we have a pretty data-rich +# situation with homogeneous distribution of residuals, because we used the +# same degradation model, error model and parameter distribution model that +# was used in the data generation. +plot(f_saem_2[["FOMC", "tc"]]) +# We can specify the same parameter model reductions manually +no_ranef <- list("parent_0", "log_beta", "parent_0", c("parent_0", "log_beta")) +dim(no_ranef) <- c(2, 2) +f_saem_2m <- update(f_saem_1, no_random_effect = no_ranef) +anova(f_saem_2m) +} +} \seealso{ \code{\link{[.mhmkin}} for subsetting \link{mhmkin} objects } |