Temporal discounting indifference point model

Compute a model of a single subject's indifference points.

Usage

td_ipm(
  data,
  discount_function = c("all", "hyperbolic", "exponential", "inverse-q-exponential",
    "nonlinear-time-hyperbolic", "scaled-exponential", "dual-systems-exponential",
    "nonlinear-time-exponential", "model-free", "constant"),
  optim_args = list(),
  silent = T
)

Arguments

data: A data frame with columns indiff for the pre-computed indifference points and del for the delay.
discount_function: A vector of strings specifying the name of the discount functions to use, or an object of class td_fn (used for creating custom discount functions), or a list of objects of class td_fn.
optim_args: A list of additional args to pass to optim.
silent: A Boolean specifying whether the call to optim (which occurs in a try block) should be silent on error.

Value

An object of class td_ipm with components data (containing the data used for fitting), config (containing the internal configuration of the model, including the discount_function), and optim (the output of optim()).

Examples

if (FALSE) { # \dontrun{
# Basic usage
data("td_ip_simulated_ptpt")
mod <- td_ipm(td_ip_simulated_ptpt, discount_function = "hyperbolic")
# Custom discount function
custom_discount_function <- td_fn(
  name = 'custom',
  fn = function(data, p) (1 - p['b'])*exp(-p['k']*data$del) + p['b'],
  par_starts = list(k = c(0.001, 0.1), b = c(0.001, 0.1)),
  par_lims = list(k = c(0, Inf), b = c(0, 1)),
  ED50 = function(p) 'non-analytic'
)
mod <- td_ipm(td_ip_simulated_ptpt, discount_function = custom_discount_function)
} # }