WARDEN (version 2.0.6)

input_block: Build an input block for automatic parameter selection

Description

Creates an unevaluated {} expression that calls pick_val_v() with the correct arguments for base case, PSA, DSA, and scenario analyses. The expression is meant to be passed directly to run_sim() or run_sim_parallel() as a *_inputs argument.

Usage

input_block(
  .data = NULL,
  base,
  psa,
  sens,
  names_out,
  psa_indicators = NULL,
  sens_indicators = NULL,
  indicator_sens_binary = FALSE,
  dsa_names = NULL,
  distributions = NULL,
  covariances = NULL
)

Value

A {} language object (same type as add_item()) with a warden_block_meta attribute used by the engine to build the iteration schedule.

Arguments

.data

Optional existing {} block to prepend (for pipe chaining).

base

A list of base case values, one entry per parameter.

psa

An unevaluated expression producing PSA draws (e.g. pick_psa(...)), evaluated at runtime per simulation.

sens

The sensitivity data object (e.g. l_inputs). At runtime the engine indexes it as sens[[sens_name_used]] to pick the active column.

names_out

Character vector or list of strings of output parameter names.

psa_indicators

List of 0/1 indicators controlling which parameters draw from PSA. NULL means all parameters draw from PSA.

sens_indicators

List of indicators, one entry per parameter (may be a scalar or a vector for vector-valued parameters). In binary mode: 0 = inactive, 1 = active. In grouped mode: 0 = inactive, same integer = varied together. NULL means all parameters are active. Only in grouped mode can subparameters (e.g., elements of a vector parameter) be varied at different steps of a DSA.

indicator_sens_binary

Logical. TRUE = binary (0/1) mode; each active parameter is varied independently via create_indicators(). FALSE (default) = grouped integer mode when sens_indicators and distributions are supplied; otherwise falls back to binary mode.

dsa_names

Character vector of sensitivity_names values that are DSA directions (e.g. c("DSA_min","DSA_max")). Those names iterate through active parameters/groups one at a time. All other names passed to sensitivty_names in run_sim() are treated as scenarios. NULL (the default) means all sensitivity names are scenarios.

distributions

List of distribution names (e.g. "rnorm", "mvrnorm", "rdirichlet"), required in grouped mode to determine which parameters are vectors and require conditional distributions.

covariances

List of covariance matrices or scalars, required for "mvrnorm" / "rdirichlet" distributions.

Details

Supports two modes:

• Binary (indicator_sens_binary = TRUE): sens_indicators are 0/1 per parameter. Each non-zero parameter is varied independently via create_indicators().

• Grouped (indicator_sens_binary = FALSE, the default when sens_indicators and distributions are supplied): sens_indicators are integer group labels. Parameters sharing the same integer are varied together; 0 means the parameter is never varied.

DSA vs scenario analyses

Supply dsa_names with the subset of sensitivity_names that correspond to DSA directions (e.g. c("DSA_min","DSA_max")). Each DSA name runs one iteration per active parameter/group; other names (scenarios) apply all active parameters simultaneously in a single iteration.

When dsa_names = NULL (the default) every sensitivity name is treated as a scenario -- all active parameters take their scenario value at once, one iteration per sensitivity_names entry. This is the correct default when there is no DSA.

sens_indicators and zeros

Any parameter whose sens_indicators entry is 0 (or all-zero for vector-valued parameters) is never varied: it always uses the base or PSA value regardless of whether we are running a DSA or a scenario. This applies to both modes.

See Also

pick_val_v(), add_item(), run_sim()

Examples

l_inputs <- list(
  parameter_name = list("util.sick", "util.sicker"),
  base_value     = list(0.8, 0.5),
  PSA_dist       = list("rnorm", "rbeta_mse"),
  a              = list(0.8, 0.5),
  b              = list(0.04, 0.025),
  n              = list(1, 1),
  DSA_min        = list(0.6, 0.3),
  DSA_max        = list(0.9, 0.7),
  psa_indicators = list(1, 1)
)

blk <- input_block(
  base                  = l_inputs[["base_value"]],
  psa                   = pick_psa(l_inputs[["PSA_dist"]], l_inputs[["n"]],
                                   l_inputs[["a"]], l_inputs[["b"]]),
  sens                  = l_inputs,
  names_out             = l_inputs[["parameter_name"]],
  psa_indicators        = l_inputs[["psa_indicators"]],
  indicator_sens_binary = TRUE,
  dsa_names             = c("DSA_min", "DSA_max")
)
stopifnot(is.call(blk))
stopifnot(!is.null(attr(blk, "warden_block_meta")))