Perform a hierarchical model fit with multiple starting values

- - -

The purpose of this method is to check if a certain algorithm for fitting -nonlinear hierarchical models (also known as nonlinear mixed-effects models) -will reliably yield results that are sufficiently similar to each other, if -started with a certain range of reasonable starting parameters. It is -inspired by the article on practical identifiabiliy in the frame of nonlinear -mixed-effects models by Duchesne et al (2021).

- -

multistart(
-  object,
-  n = 50,
-  cores = if (Sys.info()["sysname"] == "Windows") 1 else parallel::detectCores(),
-  cluster = NULL,
-  ...
-)
-
-# S3 method for saem.mmkin
-multistart(object, n = 50, cores = 1, cluster = NULL, ...)
-
-# S3 method for multistart
-print(x, ...)
-
-best(object, ...)
-
-# S3 method for default
-best(object, ...)
-
-which.best(object, ...)
-
-# S3 method for default
-which.best(object, ...)

- -

Arguments

object: The fit object to work with
n: How many different combinations of starting parameters should be -used?
cores: How many fits should be run in parallel (only on posix platforms)?
cluster: A cluster as returned by parallel::makeCluster to be used -for parallel execution.
...: Passed to the update function.
x: The multistart object to print

Value

- - -

A list of saem.mmkin objects, with class attributes -'multistart.saem.mmkin' and 'multistart'.

- - -

The object with the highest likelihood

- - -

The index of the object with the highest likelihood

References

Duchesne R, Guillemin A, Gandrillon O, Crauste F. Practical -identifiability in the frame of nonlinear mixed effects models: the example -of the in vitro erythropoiesis. BMC Bioinformatics. 2021 Oct 4;22(1):478. -doi: 10.1186/s12859-021-04373-4.

Examples

# \dontrun{
-library(mkin)
-dmta_ds <- lapply(1:7, function(i) {
-  ds_i <- dimethenamid_2018$ds[[i]]$data
-  ds_i[ds_i$name == "DMTAP", "name"] <-  "DMTA"
-  ds_i$time <- ds_i$time * dimethenamid_2018$f_time_norm[i]
-  ds_i
-})
-names(dmta_ds) <- sapply(dimethenamid_2018$ds, function(ds) ds$title)
-dmta_ds[["Elliot"]] <- rbind(dmta_ds[["Elliot 1"]], dmta_ds[["Elliot 2"]])
-dmta_ds[["Elliot 1"]] <- dmta_ds[["Elliot 2"]] <- NULL
-
-f_mmkin <- mmkin("DFOP", dmta_ds, error_model = "tc", cores = 7, quiet = TRUE)
-f_saem_full <- saem(f_mmkin)
-f_saem_full_multi <- multistart(f_saem_full, n = 16, cores = 16)
-parplot(f_saem_full_multi, lpos = "topleft")
-
-illparms(f_saem_full)
-#> [1] "sd(log_k2)"
-
-f_saem_reduced <- update(f_saem_full, no_random_effect = "log_k2")
-illparms(f_saem_reduced)
-# On Windows, we need to create a PSOCK cluster first and refer to it
-# in the call to multistart()
-library(parallel)
-cl <- makePSOCKcluster(12)
-f_saem_reduced_multi <- multistart(f_saem_reduced, n = 16, cluster = cl)
-parplot(f_saem_reduced_multi, lpos = "topright", ylim = c(0.5, 2))
-
-stopCluster(cl)
-# }
-

- -

Perform a hierarchical model fit with multiple starting values

Arguments

Value

References

See also

Examples