multistart function

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 class 'saem.mmkin'
multistart(object, n = 50, cores = 1, cluster = NULL, ...)

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

best(object, ...)

## Default S3 method:
best(object, ...)

which.best(object, ...)

## Default S3 method:
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

Returns

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

Examples

## Not run:

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", las = 2)
illparms(f_saem_full)

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), las = 2)
stopCluster(cl)
## End(Not run)

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.

See Also

parplot , llhist