mhmkin: Fit nonlinear mixed-effects models built from one or more...

View source: R/mhmkin.R

mhmkinR Documentation

Fit nonlinear mixed-effects models built from one or more kinetic degradation models and one or more error models


The name of the methods expresses that (multiple) hierarchichal (also known as multilevel) multicompartment kinetic models are fitted. Our kinetic models are nonlinear, so we can use various nonlinear mixed-effects model fitting functions.


mhmkin(objects, ...)

## S3 method for class 'mmkin'
mhmkin(objects, ...)

## S3 method for class 'list'
  backend = "saemix",
  algorithm = "saem",
  no_random_effect = NULL,
  auto_ranef_threshold = 3,
  cores = if (["sysname"] == "Windows") 1 else parallel::detectCores(),
  cluster = NULL

## S3 method for class 'mhmkin'
x[i, j, ..., drop = FALSE]

## S3 method for class 'mhmkin'
print(x, ...)



A list of mmkin objects containing fits of the same degradation models to the same data, but using different error models. Alternatively, a single mmkin object containing fits of several degradation models to the same data


Further arguments that will be passed to the nonlinear mixed-effects model fitting function.


The backend to be used for fitting. Currently, only saemix is supported


The algorithm to be used for fitting (currently not used)


Default is NULL and will be passed to saem. If you specify "auto", random effects are only included if the number of datasets in which the parameter passed the t-test is at least 'auto_ranef_threshold'. Beware that while this may make for convenient model reduction or even numerical stability of the algorithm, it will likely lead to underparameterised models.


See 'no_random_effect.


The number of cores to be used for multicore processing. This is only used when the cluster argument is NULL. On Windows machines, cores > 1 is not supported, you need to use the cluster argument to use multiple logical processors. Per default, all cores detected by parallel::detectCores() are used, except on Windows where the default is 1.


A cluster as returned by makeCluster to be used for parallel execution.


An mhmkin object.


Row index selecting the fits for specific models


Column index selecting the fits to specific datasets


If FALSE, the method always returns an mhmkin object, otherwise either a list of fit objects or a single fit object.


A two-dimensional array of fit objects and/or try-errors that can 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 mhmkin.


Johannes Ranke

See Also

[.mhmkin for subsetting mhmkin objects

mkin documentation built on Nov. 20, 2022, 1:06 a.m.