run_covsearch {pharmr}

run_covsearch

Description

Run COVsearch tool. For more details, see :ref:covsearch.

Usage

run_covsearch(
  search_space,
  p_forward = 0.01,
  p_backward = 0.001,
  max_steps = -1,
  algorithm = "scm-forward-then-backward",
  results = NULL,
  model = NULL,
  max_eval = FALSE,
  adaptive_scope_reduction = FALSE,
  strictness = "minimization_successful or (rounding_errors and sigdigs>=0.1)",
  naming_index_offset = 0,
  ...
)

Arguments

search_space

(str or ModelFeatures) MFL of covariate effects to try

p_forward

(numeric) The p-value to use in the likelihood ratio test for forward steps

p_backward

(numeric) The p-value to use in the likelihood ratio test for backward steps

max_steps

(numeric) The maximum number of search steps to make

algorithm

(str) The search algorithm to use. Currently, 'scm-forward' and 'scm-forward-then-backward' are supported.

results

(ModelfitResults (optional)) Results of model

model

(Model (optional)) Pharmpy model

max_eval

(logical) Limit the number of function evaluations to 3.1 times that of the base model. Default is FALSE.

adaptive_scope_reduction

(logical) Stash all non-significant parameter-covariate effects to be tested after all significant effects have been tested. Once all these have been tested, try adding the stashed effects once more with a regular forward approach. Default is FALSE

strictness

(str (optional)) Strictness criteria

naming_index_offset

(numeric (optional)) index offset for naming of runs. Default is 0

...

Arguments to pass to tool

Value

(COVSearchResults) COVsearch tool result object

Examples

## Not run: 
model <- load_example_model("pheno")
results <- load_example_modelfit_results("pheno")
search_space <- 'COVARIATE(c(CL, V), c(AGE, WT), EXP)'
res <- run_covsearch(search_space, model=model, results=results)

## End(Not run)