R/dbdm_prob_weight.R

In hBayesDM: Hierarchical Bayesian Modeling of Decision-Making Tasks

#' @templateVar MODEL_FUNCTION dbdm_prob_weight
#' @templateVar CONTRIBUTOR \href{https://ccs-lab.github.io/team/yoonseo-zoh/}{Yoonseo Zoh} <\email{zohyos7@@gmail.com}>
#' @templateVar TASK_NAME Description Based Decison Making Task
#' @templateVar TASK_CODE dbdm
#' @templateVar TASK_CITE 
#' @templateVar MODEL_NAME Probability Weight Function
#' @templateVar MODEL_CODE prob_weight
#' @templateVar MODEL_CITE (Erev et al., 2010; Hertwig et al., 2004; Jessup et al., 2008)
#' @templateVar MODEL_TYPE Hierarchical
#' @templateVar DATA_COLUMNS "subjID", "opt1hprob", "opt2hprob", "opt1hval", "opt1lval", "opt2hval", "opt2lval", "choice"
#' @templateVar PARAMETERS \code{tau} (probability weight function), \code{rho} (subject utility function), \code{lambda} (loss aversion parameter), \code{beta} (inverse softmax temperature)
#' @templateVar REGRESSORS 
#' @templateVar POSTPREDS "y_pred"
#' @templateVar LENGTH_DATA_COLUMNS 8
#' @templateVar DETAILS_DATA_1 \item{subjID}{A unique identifier for each subject in the data-set.}
#' @templateVar DETAILS_DATA_2 \item{opt1hprob}{Possiblity of getting higher value of outcome(opt1hval) when choosing option 1.}
#' @templateVar DETAILS_DATA_3 \item{opt2hprob}{Possiblity of getting higher value of outcome(opt2hval) when choosing option 2.}
#' @templateVar DETAILS_DATA_4 \item{opt1hval}{Possible (with opt1hprob probability) outcome of option 1.}
#' @templateVar DETAILS_DATA_5 \item{opt1lval}{Possible (with (1 - opt1hprob) probability) outcome of option 1.}
#' @templateVar DETAILS_DATA_6 \item{opt2hval}{Possible (with opt2hprob probability) outcome of option 2.}
#' @templateVar DETAILS_DATA_7 \item{opt2lval}{Possible (with (1 - opt2hprob) probability) outcome of option 2.}
#' @templateVar DETAILS_DATA_8 \item{choice}{If option 1 was selected, choice == 1; else if option 2 was selected, choice == 2.}
#' @templateVar LENGTH_ADDITIONAL_ARGS 0
#' 
#' @template model-documentation
#'
#' @export
#' @include hBayesDM_model.R
#' @include preprocess_funcs.R

#' @references
#' Erev, I., Ert, E., Roth, A. E., Haruvy, E., Herzog, S. M., Hau, R., ... & Lebiere, C. (2010). A choice prediction competition: Choices from experience and from description. Journal of Behavioral Decision Making, 23(1), 15-47.
#'
#' Hertwig, R., Barron, G., Weber, E. U., & Erev, I. (2004). Decisions from experience and the effect of rare events in risky choice. Psychological science, 15(8), 534-539.
#'
#' Jessup, R. K., Bishara, A. J., & Busemeyer, J. R. (2008). Feedback produces divergence from prospect theory in descriptive choice. Psychological Science, 19(10), 1015-1022.
#'


dbdm_prob_weight <- hBayesDM_model(
  task_name       = "dbdm",
  model_name      = "prob_weight",
  model_type      = "",
  data_columns    = c("subjID", "opt1hprob", "opt2hprob", "opt1hval", "opt1lval", "opt2hval", "opt2lval", "choice"),
  parameters      = list(
    "tau" = c(0, 0.8, 1),
    "rho" = c(0, 0.7, 2),
    "lambda" = c(0, 2.5, 5),
    "beta" = c(0, 0.2, 1)
  ),
  regressors      = NULL,
  postpreds       = c("y_pred"),
  preprocess_func = dbdm_preprocess_func)