Run IOVsearch tool. For more details, see :ref:iovsearch.
run_iovsearch( column ="OCC", list_of_parameters =NULL, rank_type ="bic", cutoff =NULL, distribution ="same-as-iiv", results =NULL, model =NULL, strictness ="minimization_successful or (rounding_errors and sigdigs>=0.1)", E =NULL,...)
Arguments
column: (str) Name of column in dataset to use as occasion column (default is 'OCC')
list_of_parameters: (array(str or array(str)) (optional)) List of parameters to test IOV on, if none all parameters with IIV will be tested (default)
rank_type: (str) Which ranking type should be used. Default is BIC.
cutoff: (numeric (optional)) Cutoff for which value of the ranking type that is considered significant. Default is NULL (all models will be ranked)
distribution: (str) Which distribution added IOVs should have (default is same-as-iiv)
results: (ModelfitResults (optional)) Results for model
model: (Model (optional)) Pharmpy model
strictness: (str (optional)) Strictness criteria
E: (numeric or str (optional)) Expected number of predictors (used for mBIC). Must be set when using mBI