R/redmeat.R

In bnRep: A Repository of Bayesian Networks from the Academic Literature

#' redmeat Bayesian Network
#'
#' Framing and tailoring prefactual messages to reduce red meat consumption: Predicting effects through a psychology-based graphical causal model.
#'
#' @usage NULL
#'
#' @format
#' A discrete Bayesian network to predict the potential effects of message delivery from the observation of the psychosocial antecedents. Probabilities were given within the referenced paper. The vertices are:
#' \describe{
#'    \item{Baseline_Intention}{(high, medium, low);}
#'    \item{Desensitization}{(high, medium, low);}
#'    \item{Diffused_Responsibility}{(high, medium, low);}
#'    \item{Food_Involvment}{(high, medium, low);}
#'    \item{Future_Intention}{(high_positive, low_positive, neutral, low_negative, high_negative);}
#'    \item{Message}{(gain, nonloss, nongain, loss);}
#'    \item{Perceived_Control}{(high, medium, low);}
#'    \item{Perceived_Severity}{(high, medium, low);}
#'    \item{Prevention_Focus}{(high, medium, low);}
#'    \item{Promotion_Focus}{(high, medium, low);}
#'    \item{Systematic_Processing}{(high, medium, low);}
#'    }
#'
#' @return An object of class \code{bn.fit}. Refer to the documentation of \code{bnlearn} for details.
#' @keywords NULL
#' @importClassesFrom bnlearn bn.fit
#' @references Catellani, P., Carfora, V., & Piastra, M. (2022). Framing and tailoring prefactual messages to reduce red meat consumption: Predicting effects through a psychology-based graphical causal model. Frontiers in Psychology, 13, 825602.
"redmeat"