Generate a set of targets for nlmixr estimation — tar

The targets generated will include the name as the final estimation step, paste(name, "object_simple", sep = "_tar_") (e.g. "pheno_tar_object_simple") as the simplified model object, and paste(name, "data_simple", sep = "_tar_") (e.g. "pheno_tar_data_simple") as the simplified data object.

Usage

tar_nlmixr(
  name,
  object,
  data,
  est = NULL,
  control = list(),
  table = nlmixr2est::tableControl(),
  env = parent.frame()
)

tar_nlmixr_raw(
  name,
  object,
  data,
  est,
  control,
  table,
  object_simple_name,
  data_simple_name,
  fit_simple_name,
  env
)

Arguments

name: Symbol, name of the target. A target name must be a valid name for a symbol in R, and it must not start with a dot. Subsequent targets can refer to this name symbolically to induce a dependency relationship: e.g. tar_target(downstream_target, f(upstream_target)) is a target named downstream_target which depends on a target upstream_target and a function f(). In addition, a target's name determines its random number generator seed. In this way, each target runs with a reproducible seed so someone else running the same pipeline should get the same results, and no two targets in the same pipeline share the same seed. (Even dynamic branches have different names and thus different seeds.) You can recover the seed of a completed target with tar_meta(your_target, seed) and run tar_seed_set() on the result to locally recreate the target's initial RNG state.
object: Fitted object or function specifying the model.
data: nlmixr data
est: estimation method (all methods are shown by `nlmixr2AllEst()`). Methods can be added for other tools
control: The estimation control object. These are expected to be different for each type of estimation method
table: The output table control object (like `tableControl()`)
env: The environment where the model is setup (not needed for typical use)
object_simple_name, data_simple_name, fit_simple_name: target names to use for the simplified object, simplified data, fit of the simplified object with the simplified data, and fit with the original data re-inserted.

Value

A list of targets for the model simplification, data simplification, and model estimation.

Details

For the way that the objects are simplified, see nlmixr_object_simplify() and nlmixr_data_simplify(). To see how to write initial conditions to work with targets, see nlmixr_object_simplify().

Functions

tar_nlmixr_raw(): An internal function to generate the targets

Examples

if (FALSE) {
library(targets)
targets::tar_script({
pheno <- function() {
  ini({
    lcl <- log(0.008); label("Typical value of clearance")
    lvc <-  log(0.6); label("Typical value of volume of distribution")
    etalcl + etalvc ~ c(1,
                        0.01, 1)
    cpaddSd <- 0.1; label("residual variability")
  })
  model({
    cl <- exp(lcl + etalcl)
    vc <- exp(lvc + etalvc)
    kel <- cl/vc
    d/dt(central) <- -kel*central
    cp <- central/vc
    cp ~ add(cpaddSd)
  })
}
list(
  tar_nlmixr(
    name = pheno_model,
    object = pheno,
    data = nlmixr2data::pheno_sd,
    est = "saem"
  )
)
})
targets::tar_make()
}