Package 'nlmixr2targets'

Title: Targets for 'nlmixr2' Pipelines
Description: 'nlmixr2' often has long runtimes. A pipeline toolkit tailored to 'nlmixr2' workflows leverages 'targets' and 'nlmixr2' to ease reproducible workflows. 'nlmixr2targets' ensures minimal rework in model development with 'nlmixr2' and 'targets' by simplifying and standardizing models and datasets.
Authors: Bill Denney [aut, cre]
Maintainer: Bill Denney <[email protected]>
License: GPL (>= 2)
Version: 0.0.0.9000
Built: 2024-11-15 05:23:26 UTC
Source: https://github.com/nlmixr2/nlmixr2targets

Help Index

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_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.

Since setting initial conditions with cmt(0) does not work with targets, the function definition of the object must set it with cmt(initial). cmt(initial) will be converted to cmt(0) before passing to nlmixr2.

Value

object converted to a nlmixrui object. The model name is always "object".

See Also

Other Simplifiers: nlmixr_data_simplify()

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. 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.

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. 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.
...

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. 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.
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. 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.
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. 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.
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)