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 mmkin
-mhmkin(objects, ...)
-
-# S3 method for list
-mhmkin(
-  objects,
-  backend = "saemix",
-  algorithm = "saem",
-  no_random_effect = NULL,
-  ...,
-  cores = if (Sys.info()["sysname"] == "Windows") 1 else parallel::detectCores(),
-  cluster = NULL
-)
-
-# S3 method for mhmkin
-[(x, i, j, ..., drop = FALSE)
-
-# S3 method for mhmkin
-print(x, ...)

- -

Arguments

objects: 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.
backend: The backend to be used for fitting. Currently, only saemix is -supported
algorithm: The algorithm to be used for fitting (currently not used)
no_random_effect: Default is NULL and will be passed to saem. If a -character vector is supplied, it will be passed to all calls to saem, -which will exclude random effects for all matching parameters. Alternatively, -a list of character vectors or an object of class 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).
cores: 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.
cluster: A cluster as returned by makeCluster to be used for -parallel execution.
x: An mhmkin object.
i: Row index selecting the fits for specific models
j: Column index selecting the fits to specific datasets
drop: If FALSE, the method always returns an mhmkin object, otherwise -either a list of fit objects or a single fit object.

Value

- - -

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 inheriting from mhmkin.

Author

Johannes Ranke

- -

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)
-#>            error
-#> degradation const tc
-#>        SFO  OK    OK
-#>        FOMC OK    OK
-#> 
-#> OK: Fit terminated successfully
-# 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)
-#>            error
-#> degradation const        tc                        
-#>        SFO  sd(parent_0) sd(parent_0)              
-#>        FOMC sd(log_beta) sd(parent_0), sd(log_beta)
-# 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)
-#>            error
-#> degradation const tc
-#>        SFO  OK    OK
-#>        FOMC OK    OK
-#> 
-#> OK: Fit terminated successfully
-illparms(f_saem_2)
-#>            error
-#> degradation const tc
-#>        SFO          
-#>        FOMC         
-# Model comparisons show that FOMC with two-component error is preferable,
-# and confirms our reduction of the default parameter model
-anova(f_saem_1)
-#> Data: 95 observations of 1 variable(s) grouped in 6 datasets
-#> 
-#>            npar    AIC    BIC     Lik
-#> SFO const     5 574.40 573.35 -282.20
-#> SFO tc        6 543.72 542.47 -265.86
-#> FOMC const    7 489.67 488.22 -237.84
-#> FOMC tc       8 406.11 404.44 -195.05
-anova(f_saem_2)
-#> Data: 95 observations of 1 variable(s) grouped in 6 datasets
-#> 
-#>            npar    AIC    BIC     Lik
-#> SFO const     4 572.22 571.39 -282.11
-#> SFO tc        5 541.63 540.59 -265.81
-#> FOMC const    6 487.38 486.13 -237.69
-#> FOMC tc       6 402.12 400.88 -195.06
-# 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)
-#> Data: 95 observations of 1 variable(s) grouped in 6 datasets
-#> 
-#>            npar    AIC    BIC     Lik
-#> SFO const     4 572.22 571.39 -282.11
-#> SFO tc        5 541.63 540.59 -265.81
-#> FOMC const    6 487.38 486.13 -237.69
-#> FOMC tc       6 402.12 400.88 -195.06
-# }
-

- -

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

Arguments

Value

See also

Author

Examples