TargetsSpecifications: Specify Targets

In imabc: Incremental Mixture Approximate Bayesian Computation (IMABC)

Description

Helper functions that can be used to create an imabc targets object used by imabc().

Usage

add_target(
  target,
  starting_range,
  stopping_range,
  target_name = NULL,
  FUN = NULL
)

group_targets(..., group_name = NULL)

define_targets(..., target_df = NULL)

as.targets(df, ...)

Arguments

target	numeric(1). The value a target function is aiming for.
starting_range	numeric(2). The initial range of values imabc will consider as good when testing simulated parameters.
stopping_range	numeric(2). The range of values a target function's simulated value must be within to be considered calibrated.
target_name	Optional character(1). The name of the target.
FUN	Optional function. The function that takes parameters and calculated the target value. See Target Function.
...	In group_targets: The results of add_target calls - one for each target within a grouping of targets. See Target Groups. In define_targets: The results of add_target and/or group_target calls - one for each target or grouping of targets. In as.targets: alternate column names for the target settings can be any one of target_names, targets, current_lower_bounds, current_upper_bounds, stopping_lower_bounds, or stopping_upper_bounds
group_name	Optional character(1). The name for the grouping of targets.
target_df	Optional data.frame. Targets stored as a data.frame or from the results object of a previous run.
df	data.frame. Each row is a target and the columns represent the different pieces of information relevant to the targets.

Value

A targets imabc object.

Target Values

When specifying values the following condition must always hold true:

starting_range[1] <= stopping_range[1] <= target <= stopping_range[2] <= starting_range[2]

As imabc simulates parameters, it will test them using the target function(s) against the starting range. Parameters whose values fall within the starting range will be kept through to the next iteration and will be used to generate new parameters for testing. As the parameters get better at falling withing the initial range, imabc will reduce the valid range of targets to be considered. Once the current valid range matches the stopping range the algorithm will no longer reduce the valid range of target values.

Target Groups

A grouped target refers to a set of scalar targets that were gathered as part of the same study or otherwise refer to a series of outcomes, such as outcomes reported by age, by sex, or over time (a time series). When targets are grouped imabc will constrict the range of valid target values relative to the least improved target within the group of targets. This lets the range of simulated parameters stay wide enough to continue improving all the targets.

Target Names

The user can specify names by either specifying the input target_name in add_target or by setting the result of an add_target call to a object in group_targets or define_targets (e.g. group_targets(t1 = add_target(...))). If the user specifies the target_name input and sets add_target to an object, the target_name value will be used. If no name is specified a unique name will be generated automatically.

These same rules also applies to groups of targets and the group_name input in group_targets. However, group_targets can only be added as an input to define_targets. If a single target is added in define_targets it will not have a group name.

Target Function

There are multiple ways to specify a target function. One way is to attach it to the target object using the FUN input in add_target. The inputs to the target function can either be a single object (e.g. function(x)) or several objects whose name is equal to the parameter they represent (e.g. function(x1, x2)). If a single object is used, the user can assume that a name vector with all parameters specified in the priors object will be passed to the function and the order of the vector will be the same as the order in which they were specified with define_priors. For example, if someone specified three parameters named x1, x3, and x2 respectively then the following specifications would all be equivalent:

function(x1, x3) { x1 + x3 } == function(x) { x["x1"] + x["x3"] } == function(x) { x[1] + x[2] }

Additionally, for more complex situations the user may also reference the targets object and priors object within a target function but they must specify them as inputs (e.g. function(x, targets, priors)) and use the objects as they exist within those objects. See define_target_function for more details and other ways to specify the target function.

Examples

add_target(target = 0.5, starting_range = c(0.2, 0.9), stopping_range = c(0.48, 0.51))
add_target(
  target = 1.5, starting_range = c(1.0, 2.0), stopping_range = c(1.49, 1.51),
  FUN = function(x1, x2) { x1 + x2 + rnorm(1, 0, 0.01) }
)

group_targets(
  targ1 = add_target(target = 0.5, starting_range = c(0.2, 0.9), stopping_range = c(0.48, 0.51)),
  add_target(
    target_name = "targ2",
    target = 1.5, starting_range = c(1.0, 2.0), stopping_range = c(1.49, 1.51),
    FUN = function(x1, x2) { x1 + x2 + rnorm(1, 0, 0.01) }
  )
)
define_targets(
  group1 = group_targets(
    targ1 = add_target(target = 0.5, starting_range = c(0.2, 0.9), stopping_range = c(0.48, 0.51)),
    add_target(
      target_name = "targ2",
      target = 1.5, starting_range = c(1.0, 2.0), stopping_range = c(1.49, 1.51)
    )
  ),
  targ3 = add_target(target = 1, starting_range = c(0.2, 1.9), stopping_range = c(0.9, 1.1))
)

df <- data.frame(
  target_groups = c("G1", "G1", NA),
  target_names = c("T1", "T3", "T2"),
  targets = c(1.5, 0.5, -1.5),
  current_lower_bounds = c(1, 0.2, -2),
  current_upper_bounds = c(2, 0.9, -1),
  stopping_lower_bounds = c(1.49, 0.49, -1.51),
  stopping_upper_bounds = c(1.51, 0.51, -1.49)
)
as.targets(df)

imabc documentation built on April 12, 2021, 9:06 a.m.