define_target_function: Define Target Function(s)

Description Usage Arguments Details Value Examples

View source: R/define_target_function.R


Helps the user build a target function that applies parameters to a function or set of functions. The results of this function are then compared to the target goals to determine the goodness of fit of the parameters.


define_target_function(targets, priors, FUN = NULL, use_seed = FALSE)



targets object. Created using the define_targets function. Available to use within the target function(s) See Details.


priors object. Created using the define_priors function. Available to use within the target function(s) See Details.


Optional function. If the user does not define target functions using define_targets, they can specify a single function here. See Details.


logical. Should the algorithm set a seed before each set of parameters is sent to the target function(s). The seed is set once for each set of parameters.


FUN: While the user can define a function for each target they create using add_target, there may be times when the user wants to have more control over how the functions are evaluated. For example, one target may be a function of another target and a parameter. If the target functions are created using define_targets, the first function would have to be evaluated twice. However, by using FUN, the user can create a function where 'T1 = f(x1)' and 'T2 = g(T1, x2)'. This is especially helpful if the target functions take a long time to run.

Specifying Parameters as Target Function Inputs: Whether specifying target functions individually or through the FUN input, the inputs must follow a certain set of rules in order to be applied correctly. It is important to remember that the input(s) are ultimately based on the values specified in the priors object. Thus the target function(s) inputs will have to reference those parameters. This can be done either as a single vector of values (e.g. 'function(x) ... '), or individual inputs for each parameter (e.g. 'function(x1, x2) ... '). If a single vector is used, all parameters will be passed to the target function as a named vector and the user can reference that vector by either using the parameter names (e.g. 'x["x1"]') or by using the order a parameter was added in define_priors as the index number (e.g. 'x[1]'). If individual inputs are given for each parameter, then only the ones specified as inputs will be sent to the given target function. If this route is taken the inputs into the target function must match the name(s) of the parameters being used. The single vector method will be most useful when creating a single target function with FUN, while the individual inputs method is nice for simple target functions added via defined_targets.

Special Target Function Inputs: Beyond specifying the parameters, the user may optionally choose to include the targets and priors objects as inputs into the target function(s). These inputs must be specified as targets and priors respectively (e.g. 'function(x, targets, priors)'). They give you access to all the values defined and updated over the course of a calibration. When using FUN, this can be especially useful if one target calculation is extremely fast while another one is extremely slow; the user can simulate the first, check it against the target bounds, and choose to sidestep the slower target function in order to get a speed boost for the entire calibration. To see what values are available for each object use the names function on a recently created object for each class.

Target Function Outputs: While define_targets will handle the outputs appropriately for imabc, the user must be careful to do the same when specifying a target function through FUN. It is required that the results of FUN is a vector whose length is equal to the number of targets defined. If the vector is named, imabc will ensure that the order of the vector is correct before performing any calculations with the results. The names must match the names given to targets in define_targets; if you didn't provide names, you can find the generated names using 'attr(targets, "target_names")'. If the vector is not named, the order of the results in the vector must match the order the targets were added in define_targets.


An imabc ready function.


priors <- define_priors(
  x1 = add_prior(dist_base_name = "unif"),
  x2 = add_prior(density_fn = "dnorm", mean = 0.5, sd = 0.25)
targets <- define_targets(
  T1 = add_target(target = 0.5, starting_range = c(0.2, 0.9), stopping_range = c(0.48, 0.51)),
    target_name = "T2",
    target = 1.5, starting_range = c(1.0, 2.0), stopping_range = c(1.49, 1.51)
fn1 <- function(x1, x2) { x1 + x2 + sample(c(-1, 1), 1)*rnorm(1, 0, 0.1) }
fn2 <- function(x1, x2) { x1 * x2 + sample(c(-1, 1), 1)*rnorm(1, 0, 0.1) }
fn <- function(x1, x2) {
  res <- c()
  res["T2"] <- fn2(x1, x2)
  res["T1"] <- fn1(x1, x2)
target_fun <- define_target_function(targets, priors, FUN = fn, use_seed = FALSE)

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