Skip to contents

Automatically generate a parameter table with initial estimates

Source: R/modelGen.R
auto_param_table.Rd

Constructs a parameter table for nlmixr2 model fitting. It supports:

  • Direct use of a user-provided parameter table.

  • Automatic initialization of parameters from data using getPPKinits().

  • Fallback to a default parameter table created by initialize_param_table().

Usage

auto_param_table(
  dat = NULL,
  param_table = NULL,
  nlmixr2autoinits = TRUE,
  foldername = NULL,
  filename = "test",
  out.inits = TRUE,
  ...
)

Arguments

dat

A data frame containing observed data (required if nlmixr2autoinits = TRUE).

param_table

Optional. A user-provided parameter table (if provided, all other logic is skipped).

nlmixr2autoinits

Logical. Whether to automatically estimate initial values using getPPKinits(). Default is TRUE.

foldername

Character string specifying the folder name for storing nlmixr2autoinits outputs. If NULL (default), tempdir() is used for temporary storage. If specified, a cache directory is created in the current working directory.

filename

Character string specifying the base name for model output files generated during evaluation.

out.inits

Logical flag indicating whether the results returned by the automated initialization procedure should be saved to an RDS file. When TRUE, the output of the initialization step is written to disk for reproducibility or debugging purposes.

...

Additional arguments passed to getPPKinits().

Value

A data.frame representing the parameter table with initial estimates, ready for use in nlmixr2().

Details

When nlmixr2autoinits = TRUE, this function estimates initial values from data, applies a name mapping to internal model parameters, performs log transformations where appropriate, and replaces problematic log values (e.g. log(0) or NA) with log(0.01) for numerical stability.

See also

getPPKinits, initialize_param_table

Author

Zhonghui Huang

Examples

# \donttest{
auto_param_table(dat = pheno_sd)
#> 
#> 
#> Infometrics                               Value          
#> ----------------------------------------  ---------------
#> Dose Route                                bolus          
#> Dose Type                                 combined_doses 
#> Number of Subjects                        59             
#> Number of Observations                    155            
#> Subjects with First-Dose Interval Data    35             
#> Observations in the First-Dose Interval   35             
#> Subjects with Multiple-Dose Data          56             
#> Observations after Multiple Doses         120            
#> ----------------------------------------  ------
#> Estimating half-life....................
#> Half-life estimation complete: Estimated t1/2 = 16.44 h
#> Evaluating the predictive performance of calculated one-compartment model parameters....................
#> Error in loadNamespace(x): there is no package called ‘progress’
# }