R/cra_linear.R

In hBayesDM: Hierarchical Bayesian Modeling of Decision-Making Tasks

#' @templateVar MODEL_FUNCTION cra_linear
#' @templateVar CONTRIBUTOR \href{https://ccs-lab.github.io/team/jaeyeong-yang/}{Jaeyeong Yang} <\email{jaeyeong.yang1125@@gmail.com}>
#' @templateVar TASK_NAME Choice Under Risk and Ambiguity Task
#' @templateVar TASK_CODE cra
#' @templateVar TASK_CITE 
#' @templateVar MODEL_NAME Linear Subjective Value Model
#' @templateVar MODEL_CODE linear
#' @templateVar MODEL_CITE (Levy et al., 2010)
#' @templateVar MODEL_TYPE Hierarchical
#' @templateVar DATA_COLUMNS "subjID", "prob", "ambig", "reward_var", "reward_fix", "choice"
#' @templateVar PARAMETERS \code{alpha} (risk attitude), \code{beta} (ambiguity attitude), \code{gamma} (inverse temperature)
#' @templateVar REGRESSORS "sv", "sv_fix", "sv_var", "p_var"
#' @templateVar POSTPREDS "y_pred"
#' @templateVar LENGTH_DATA_COLUMNS 6
#' @templateVar DETAILS_DATA_1 \item{subjID}{A unique identifier for each subject in the data-set.}
#' @templateVar DETAILS_DATA_2 \item{prob}{Objective probability of the variable lottery.}
#' @templateVar DETAILS_DATA_3 \item{ambig}{Ambiguity level of the variable lottery (0 for risky lottery; greater than 0 for ambiguous lottery).}
#' @templateVar DETAILS_DATA_4 \item{reward_var}{Amount of reward in variable lottery. Assumed to be greater than zero.}
#' @templateVar DETAILS_DATA_5 \item{reward_fix}{Amount of reward in fixed lottery. Assumed to be greater than zero.}
#' @templateVar DETAILS_DATA_6 \item{choice}{If the variable lottery was selected, choice == 1; otherwise choice == 0.}
#' @templateVar LENGTH_ADDITIONAL_ARGS 0
#' 
#' @template model-documentation
#'
#' @export
#' @include hBayesDM_model.R
#' @include preprocess_funcs.R

#' @references
#' Levy, I., Snell, J., Nelson, A. J., Rustichini, A., & Glimcher, P. W. (2010). Neural representation of subjective value under risk and ambiguity. Journal of Neurophysiology, 103(2), 1036-1047.
#'


cra_linear <- hBayesDM_model(
  task_name       = "cra",
  model_name      = "linear",
  model_type      = "",
  data_columns    = c("subjID", "prob", "ambig", "reward_var", "reward_fix", "choice"),
  parameters      = list(
    "alpha" = c(0, 1, 2),
    "beta" = c(-Inf, 0, Inf),
    "gamma" = c(0, 1, Inf)
  ),
  regressors      = list(
    "sv" = 2,
    "sv_fix" = 2,
    "sv_var" = 2,
    "p_var" = 2
  ),
  postpreds       = c("y_pred"),
  preprocess_func = cra_preprocess_func)