From e5d1df9a9b1f0951d7dfbaf24eee4294470b73e2 Mon Sep 17 00:00:00 2001 From: Johannes Ranke Date: Thu, 17 Nov 2022 14:54:20 +0100 Subject: Complete update of online docs for v1.2.0 --- docs/reference/multistart.html | 243 +++++++++++++++++++++++++++++++++++++++++ 1 file changed, 243 insertions(+) create mode 100644 docs/reference/multistart.html (limited to 'docs/reference/multistart.html') diff --git a/docs/reference/multistart.html b/docs/reference/multistart.html new file mode 100644 index 00000000..8bdce122 --- /dev/null +++ b/docs/reference/multistart.html @@ -0,0 +1,243 @@ + +Perform a hierarchical model fit with multiple starting values — multistart • mkin + + +
+
+ + + +
+
+
+

Perform a hierarchical model fit with multiple starting values

+ Source: R/multistart.R + +
+ +
+

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.

+
+
+

See also

+

parplot, llhist

+
+ +
+

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 cluster first. When working with
+# such a cluster, we need to export the mmkin object to the cluster
+# nodes, as it is referred to when updating the saem object on the nodes.
+library(parallel)
+cl <- makePSOCKcluster(12)
+f_saem_reduced_multi <- multistart(f_saem_reduced, n = 16, cluster = cl)
+parplot(f_saem_reduced_multi, lpos = "topright")
+
+stopCluster(cl)
+# }
+
+
+
+ +
+ + +
+ + + + + + + + -- cgit v1.2.1