screen_simulation_inputs {predictNMB}

Screen many simulation inputs: a parent function to do_nmb_sim()

Description

Runs do_nmb_sim() with a range of inputs.

Usage

screen_simulation_inputs(
  sample_size,
  n_sims,
  n_valid,
  sim_auc,
  event_rate,
  cutpoint_methods = get_inbuilt_cutpoint_methods(),
  fx_nmb_training,
  fx_nmb_evaluation,
  pair_nmb_train_and_evaluation_functions = FALSE,
  meet_min_events = TRUE,
  min_events = NA,
  show_progress = FALSE,
  cl = NULL
)

Arguments

sample_size

A value (or vector of values): Sample size of training set. If missing, a sample size calculation will be performed and the calculated size will be used.

n_sims

A value (or vector of values): Number of simulations to run.

n_valid

A value (or vector of values): Sample size for evaluation set.

sim_auc

A value (or vector of values): Simulated model discrimination (AUC).

event_rate

A value (or vector of values): simulated event rate of the binary outcome being predicted.

cutpoint_methods

cutpoint methods to include. Defaults to use the inbuilt methods. This doesn't change across calls to do_nmb_sim().

fx_nmb_training

A function or NMBsampler (or list of) that returns named vector of NMB assigned to classifications use for obtaining cutpoint on training set.

fx_nmb_evaluation

A function or NMBsampler (or list of) that returns named vector of NMB assigned to classifications use for obtaining cutpoint on evaluation set.

pair_nmb_train_and_evaluation_functions

logical. Whether or not to pair the lists of functions passed for fx_nmb_training and fx_nmb_evaluation. If two treatment strategies are being used, it may make more sense to pair these because selecting a value-optimising or cost-minimising threshold using one strategy but evaluating another is likely unwanted.

meet_min_events

Whether or not to incrementally add samples until the expected number of events (sample_size * event_rate) is met. (Applies to sampling of training data only.)

min_events

A value: the minimum number of events to include in the training sample. If less than this number are included in sample of size sample_size, additional samples are added until the min_events is met. The default (NA) will use the expected value given the event_rate and the sample_size.

show_progress

Logical. Whether to display a progress bar.

cl

A cluster made using parallel::makeCluster(). If a cluster is provided, the simulation will be done in parallel.

Value

Returns a predictNMBscreen object.

Examples

# Screen for optimal cutpoints given increasing values of
# model discrimination (sim_auc)

get_nmb <- function() c("TP" = -3, "TN" = 0, "FP" = -1, "FN" = -4)
sim_screen_obj <- screen_simulation_inputs(
  n_sims = 50, n_valid = 10000, sim_auc = seq(0.7, 0.9, 0.1),
  event_rate = 0.1, fx_nmb_training = get_nmb, fx_nmb_evaluation = get_nmb
)