Package 'nlmixr2targets'

Replace the fit data with the original data, then return the modified fit

Description

This function is intended for use within nlmixr2targets target creation, and it's not typically invoked by users.

Usage

assign_origData(fit, data)

Arguments

fit	an estimated nlmixr2 object
data	the data from the original fit

Value

The fit with the data added back in as fit$env$origData

---

Standardize and simplify data for nlmixr2 estimation

Description

This function is typically not needed by end users.

Usage

nlmixr_data_simplify(data, object, table = list())

Arguments

data	nlmixr data
object	an nlmixr_ui object (e.g. the output of running nlmixr(object = model)
table	The output table control object (like 'tableControl()')

Details

The standardization keeps columns that rxode2 and nlmixr2 use along with the covariates. Column order is standardized (rxode2 then nlmixr2 then alphabetically sorted covariates), and rxode2 and nlmixr2 column names are converted to lower case.

Value

The data with the nlmixr2 column lower case and on the left and the covariate columns on the right and alphabetically sorted.

See Also

Other Simplifiers: nlmixr_object_complicate(), nlmixr_object_simplify()

---

Re-attach labels, metadata, and original data to a simplified fit

Description

The inverse of nlmixr_object_simplify(). Given a fit produced from the simplified, label-and-meta-stripped model, plus the original model function and the original data, this function:

Usage

nlmixr_object_complicate(fit, object, data)

Arguments

fit	An estimated nlmixr2 fit produced from the simplified model.
object	The original model function (or object) the fit was derived from. Its labels and metadata are read back onto fit.
data	The original data the fit corresponds to (before nlmixr_data_simplify() reduced its column set). Must have the same number of rows as the data currently stored on the fit, mirroring assign_origData().

Details

• re-derives parameter labels and the metadata environment from object and writes them back onto fit$ui$iniDf$label and fit$ui$meta, and

• replaces fit$env$origData with the original data.

This is what makes label/meta edits in the source model cheap under targets: the cache hash for the simplified model object is independent of labels and metadata, so tar_make() only re-runs this re-attachment step (and the cheap ⁠_object_simple⁠ step) when only labels or metadata change.

This function is typically not invoked directly by end users; it is the command for the final target produced by tar_nlmixr().

Value

The modified fit.

See Also

nlmixr_object_simplify(), assign_origData().

Other Simplifiers: nlmixr_data_simplify(), nlmixr_object_simplify()

---

Simplify an nlmixr object

Description

This function is typically not needed by end users.

Usage

nlmixr_object_simplify(object)

Arguments

object

Fitted object or function specifying the model.

Details

The object simplification removes comments (so please use label() instead of comments to label parameters) and then converts the object to a "nlmixrui" object.

Object metadata (ui$meta) and parameter labels (ui$iniDf$label) are also stripped from the simplified object before it is written to the indirect cache. They do not affect estimation, and stripping them keeps the cache hash stable across edits to either, so editing only labels or metadata will not invalidate the cached fit. The stripped values are restored on the final fit by nlmixr_object_complicate(), which reads them straight back off the original model.

The natural nlmixr2 DSL form for compartment initial conditions (cmt(0) <- value inside a model({...}) block) trips targets' static analysis because codetools::findGlobals() interprets it as a replacement-function assignment with a non-symbol target. tar_nlmixr() auto-rewrites cmt(0) <- value to cmt(initial) <- value inside model({...}) blocks at construction time (mutating the user's model function in env), and converts back to cmt(0) <- value before nlmixr2 sees the model. The user can therefore write cmt(0) <- value directly. Manual cmt(initial) <- value is also still accepted. Note: because the rewrite mutates the function in env, calling the model function directly (outside tar_make()) after tar_nlmixr() will see cmt(initial) in its body.

The simplified model's model.name is always set to "object". This keeps the simplified output stable so that the MD5 hash used by the targets indirect cache is independent of the symbol the caller bound the model function to.

Value

The MD5 hash used to load the simplified nlmixrui object back from the nlmixr2targets indirect cache.

See Also

nlmixr_object_complicate() for the inverse operation that re-attaches labels, metadata, and the original data on the final fit.

Other Simplifiers: nlmixr_data_simplify(), nlmixr_object_complicate()

---

Runtime helper: undo the cmt(0) -> cmt(initial) rewrite before evaluating the captured object expression.

Description

Wrapped around the captured expression at construction time when tar_nlmixr_protect_zero_initial() performed any rewrites. Needed for pipe forms like pheno |> model({...}) and pheno |> ini(...), because nlmixr2's pipe handlers parse pheno's body before the existing nlmixr_object_simplify_zero_initial_helper() could intervene. Harmless for symbol-only forms (the existing helper reaches them at simplify time anyway).

Usage

nlmixr_object_zero_initial_eval(expr, envir = parent.frame())

Arguments

expr	A quoted (unevaluated) language object.
envir	The parent environment for evaluation. Defaults to the caller's frame (the targets execution env).

Details

Mechanism:

• Walk the quoted expression, rewriting name(initial) <- val back to name(0) <- val via the existing inverse helper.

• Walk the (rewritten) expression for symbols. For each that resolves to a function whose body still contains name(initial) <- val inside model({...}), build a corrected closure with name(0) <- val and bind it in an override frame.

• Evaluate the rewritten expression in the override frame (whose parent is envir), so any lookup of a rewritten symbol hits the corrected closure first.

The corrected copies retain the original closure environment of the user's function; only the body is swapped.

Value

The result of evaluating the corrected expression.

---

Estimate an nlmixr2 model loading the model from a targets indirect hash storage

Description

This is not intended for direct use by users

Usage

nlmixr2_indirect(object, data, est, control)

Arguments

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

---

Generate a set of targets for nlmixr estimation

Description

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. In tar_target(), name is an unevaluated symbol, e.g. tar_target(name = data). In tar_target_raw(), name is a character string, e.g. tar_target_raw(name = "data"). 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 most cases, The target name is the name of its local data file in storage. Some file systems are not case sensitive, which means converting a name to a different case may overwrite a different target. Please ensure all target names have unique names when converted to lower case. 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.

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().

Value

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

Functions

• tar_nlmixr_raw(): An internal function to generate the targets

Examples

## Not run: 
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()

## End(Not run)

---

Generate a list of models based on a single dataset and estimation method

Description

Generate a list of models based on a single dataset and estimation method

Usage

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

Arguments

name	Symbol, name of the target. In tar_target(), name is an unevaluated symbol, e.g. tar_target(name = data). In tar_target_raw(), name is a character string, e.g. tar_target_raw(name = "data"). 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 most cases, The target name is the name of its local data file in storage. Some file systems are not case sensitive, which means converting a name to a different case may overwrite a different target. Please ensure all target names have unique names when converted to lower case. 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.
...	Named arguments with the format "Model description" = modelFunction
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)

Value

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

---

Does the model list refer to another model in the model list?

Description

Does the model list refer to another model in the model list?

Usage

tar_nlmixr_multimodel_has_self_reference(model_list, name)

tar_nlmixr_multimodel_has_self_reference_single(model, name)

Arguments

model_list	A named list of calls for model targets to be created
name	Symbol, name of the target. In tar_target(), name is an unevaluated symbol, e.g. tar_target(name = data). In tar_target_raw(), name is a character string, e.g. tar_target_raw(name = "data"). 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 most cases, The target name is the name of its local data file in storage. Some file systems are not case sensitive, which means converting a name to a different case may overwrite a different target. Please ensure all target names have unique names when converted to lower case. 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.
model	A single model call for the model target to be created

Value

A logical vector the same length as model_list indicating if the model is self-referential to another model in the list

Functions

• tar_nlmixr_multimodel_has_self_reference_single(): A helper function to look at each call for each model separately

---

Generate nlmixr multimodel target set for all models in one call to tar_nlmixr_multimodel()

Description

Generate nlmixr multimodel target set for all models in one call to tar_nlmixr_multimodel()

Usage

tar_nlmixr_multimodel_parse(name, data, est, control, table, model_list, env)

Arguments

name	Symbol, name of the target. In tar_target(), name is an unevaluated symbol, e.g. tar_target(name = data). In tar_target_raw(), name is a character string, e.g. tar_target_raw(name = "data"). 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 most cases, The target name is the name of its local data file in storage. Some file systems are not case sensitive, which means converting a name to a different case may overwrite a different target. Please ensure all target names have unique names when converted to lower case. 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.
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()')
model_list	A named list of calls for model targets to be created
env	The environment where the model is setup (not needed for typical use)

---

Generate a single nlmixr multimodel target set for one model

Description

Generate a single nlmixr multimodel target set for one model

Usage

tar_nlmixr_multimodel_single(object, name, data, est, control, table, env)

Arguments

object	Fitted object or function specifying the model.
name	Symbol, name of the target. In tar_target(), name is an unevaluated symbol, e.g. tar_target(name = data). In tar_target_raw(), name is a character string, e.g. tar_target_raw(name = "data"). 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 most cases, The target name is the name of its local data file in storage. Some file systems are not case sensitive, which means converting a name to a different case may overwrite a different target. Please ensure all target names have unique names when converted to lower case. 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.
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)

---