nlmixr2auto (version 1.0.0)

step_correlation: Evaluate inclusion of ETA correlation structure

Description

Evaluates whether correlation between inter-individual random effects (ETA correlation) should be included in the model.

Usage

step_correlation(
  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 models,

• 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 data frame of initial parameter estimates. If NULL, the table is generated by auto_param_table().

penalty.control

A list of penalty control parameters defined by penaltyControl(), specifying penalty values used for model diagnostics during fitness evaluation.

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

Two candidate models are constructed by toggling the correlation setting of inter-individual random effects in the model code. Model selection is based on comparison of Fitness values returned 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")
  start.mod <- base_model("ivbase")
  start.mod["eta.vc"] <- 1L
  step_correlation(
    dat = dat,
    start.mod = start.mod,
    search.space = "ivbase",
    param_table = param_table,
    filename = "step_mcorr_test",
    penalty.control = penalty.control,
    saem.control = nlmixr2est::saemControl(logLik = TRUE,nBurn=15,nEm=15)
  )
# }