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, backend = "saemix", algorithm = "saem", ...)

# S3 method for list
  backend = "saemix",
  cores = if (["sysname"] == "Windows") 1 else parallel::detectCores(),
  cluster = NULL

# S3 method for mhmkin
[(x, i, j, ..., drop = FALSE)

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



A list of mmkin objects containing fits of the same degradation models to the same data, but using different error models.


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


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


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


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.

See also

[.mhmkin for subsetting mhmkin objects


Johannes Ranke