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Temporal discounting binary choice nonlinear model

Source: R/td_bcnm.R
td_bcnm.Rd

Compute a binary choice model for a single subject

Usage

td_bcnm(
  data,
  discount_function = "all",
  choice_rule = c("logistic", "probit", "power"),
  fixed_ends = F,
  fit_err_rate = F,
  gamma_par_starts = c(0.01, 1, 100),
  eps_par_starts = c(0.01, 0.25),
  silent = T,
  optim_args = list(),
  ...
)

Arguments

data

A data frame with columns val_imm and val_del for the values of the immediate and delayed rewards, del for the delay, and imm_chosen (Boolean) for whether the immediate reward was chosen. Other columns can also be present but will be ignored.

discount_function

A string 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.

choice_rule

A string specifying whether the 'logistic' (default), 'probit', or 'power' choice rule should be used.

fixed_ends

A Boolean (false by default) specifying whether the model should satisfy the desiderata that subjects should always prefer something over nothing (i.e., nonzero delayed reward over nothing) and the same reward sooner rather than later.

fit_err_rate

A Boolean (false by default) specifying whether the model should include an error rate (parameterized by "eps"). See Eq. 5 here: https://doi.org/10.3758/s13428-015-0672-2.

gamma_par_starts

A vector of starting values to try for the "gamma" parameter (which controls the steepness of the choice rule) during optimization.

eps_par_starts

A vector of starting values to try for the "eps" parameter (which controls the error rate) during optimization. Ignored if `fit_err_rate = FALSE`.

silent

Boolean (true by default). The call to optim() occurs within a try() wrapper. The value of silent is passed along to try().

optim_args

Additional arguments to pass to optim(). Default is list(silent = T).

...

Additional arguments to provide finer-grained control over the model configuration.

Value

An object of class td_bcnm 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()).

See also

Other nonlinear binary choice model functions: coef.td_bcnm(), deviance.td_bcnm(), fitted.td_bcnm(), logLik.td_bcnm(), predict.td_bcnm(), residuals.td_bcnm()

Examples

if (FALSE) { # \dontrun{
data("td_bc_single_ptpt")
mod <- td_bcnm(td_bc_single_ptpt, discount_function = "hyperbolic", fixed_ends = T)
# 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(...) 'non-analytic'
)
mod <- td_bcnm(td_bc_single_ptpt, discount_function = custom_discount_function, fit_err_rate = T)
} # }