nlmixr2auto (version 1.0.0)

step_rv: Evaluate residual error model structure

Description

Evaluates alternative residual error model structures by modifying the residual variability setting in the model code.

Usage

step_rv(
  dat,
  start.mod = NULL,
  search.space = "ivbase",
  no.cores = NULL,
  param_table = NULL,
  penalty.control = NULL,
  precomputed_results_file = NULL,
  filename = "test",
  foldername = NULL,
  .modEnv = NULL,
  verbose = TRUE,
  ...
)

Value

A list with the following elements:

• results_table: A data frame summarizing the evaluated residual error models and their fit statistics,

• best_code: A named integer vector corresponding to the selected model code,

• best_row: A one-row data frame containing the summary of the selected model.

Arguments

dat

A data frame containing pharmacokinetic data in standard nlmixr2 format, including "ID", "TIME", "EVID", and "DV", and may include additional columns.

start.mod

A named integer vector specifying the starting model code. If NULL, a base model is generated using base_model().

search.space

Character, one of ivbase or oralbase. Default is ivbase.

no.cores

Integer. Number of CPU cores to use. If NULL, uses rxode2::getRxThreads().

param_table

Optional parameter table used during model estimation.

penalty.control

Optional penalty control object used for reporting penalty terms in the results table.

precomputed_results_file

Optional path to a CSV file of previously computed model results used for caching.

filename

Optional character string used as a prefix for output files. Defaults to "test".

foldername

Character string specifying the name of the folder to be created in the current working directory to store intermediate results. If NULL, a name is generated automatically.

.modEnv

Optional environment used to store model indices and cached results across steps.

verbose

Logical. If TRUE, print progress messages.

...

Additional arguments passed to the model estimation function.

Author

Zhonghui Huang

Details

Candidate models are constructed by assigning different residual error types to the model code. Each candidate differs only in the residual variability specification, and all other structural and statistical components are kept unchanged. Model selection is based on comparison of Fitness values obtained during estimation.

See Also

mod.run, base_model, penaltyControl

Examples

# \donttest{
  dat <- pheno_sd
  param_table <- initialize_param_table()
  param_table$init[param_table$Name == "lcl"] <- log(0.008)
  param_table$init[param_table$Name == "lvc"] <- log(0.6)
  penalty.control <- penaltyControl()
  penalty.control$penalty.terms <-
    c("rse","theta", "covariance","shrinkage","omega","correlation","sigma")
  step_rv(
    dat = dat,
    search.space = "ivbase",
    param_table = param_table,
    filename = "step_rv_test",
    penalty.control = penalty.control,
    saem.control = nlmixr2est::saemControl(logLik = TRUE,nBurn=15,nEm=15)
  )
# }