pharmr (version 2.2.0)

run_iivsearch: run_iivsearch

Description

Run IIVsearch tool. For more details, see :ref:iivsearch.

Usage

run_iivsearch(
  model,
  results,
  algorithm = "top_down_exhaustive",
  search_space = "IIV?(@IIV,EXP);COVARIANCE?(IIV,@IIV)",
  as_fullblock = FALSE,
  rank_type = "bic",
  linearize = FALSE,
  cutoff = NULL,
  strictness = "minimization_successful or (rounding_errors and sigdigs>=0.1)",
  correlation_algorithm = NULL,
  E_p = NULL,
  E_q = NULL,
  parameter_uncertainty_method = NULL,
  ...
)

Value

(IIVSearchResults) IIVsearch tool result object

Arguments

model

(Model) Pharmpy model

results

(ModelfitResults) Results for model

algorithm

(str) Which algorithm to run when determining number of IIVs.

search_space

(str) Search space to explore

as_fullblock

(logical) Whether to add IIVs as a fullblock

rank_type

(str) Which ranking type should be used. Default is BIC.

linearize

(logical) Wheter or not use linearization when running the tool.

cutoff

(numeric (optional)) Cutoff for which value of the ranking function that is considered significant. Default is NULL (all models will be ranked)

strictness

(str) Strictness criteria

correlation_algorithm

(str (optional)) Which algorithm to run for the determining block structure of added IIVs. If NULL, the algorithm is determined based on the 'algorithm' argument

E_p

(numeric or str (optional)) Expected number of predictors for diagonal elements (used for mBIC). Must be set when using mBIC and when the argument 'algorithm' is not 'skip'

E_q

(numeric or str (optional)) Expected number of predictors for off-diagonal elements (used for mBIC). Must be set when using mBIC and when the argument correlation_algorithm is not skip or NULL

parameter_uncertainty_method

(str (optional)) Parameter uncertainty method. Will be used in ranking models if strictness includes parameter uncertaint

...

Arguments to pass to tool

Examples

if (FALSE) {
model <- load_example_model("pheno")
results <- load_example_modelfit_results("pheno")
run_iivsearch(model=model, results=results, algorithm='top_down_exhaustive')
}