R/C2_Initializer.R

In BayesNetBP: Bayesian Network Belief Propagation

#' Initialize a ClusterTree object
#'
#' Initialize a ClusterTree object
#'
#' @details A wrapper function to initialize a \code{\linkS4class{ClusterTree}} object. It combines
#' the functions of \code{\link{ClusterTreeCompile}}, \code{\link{LocalModelCompile}},
#' \code{\link{ElimTreeInitialize}} and \code{\link{Propagate}}, thus initialize the \code{\linkS4class{ClusterTree}}
#' object in a single step.
#'
#' @param dag a \code{graphNEL} object of the Bayesian network
#' @param data a \code{data.frame} object
#' @param node.class a named \code{vector} of \code{logical} values, \code{TRUE} if node
#' is discrete, \code{FASLE} if otherwise
#' @param propagate \code{logical} \code{TRUE} if the discrete part of the \code{\linkS4class{ClusterTree}}
#' to be propagated
#'
#' @return \code{\linkS4class{ClusterTree}} object
#'
#' @author Han Yu
#'
#' @references Cowell, R. G. (2005). Local propagation in conditional Gaussian Bayesian networks.
#' Journal of Machine Learning Research, 6(Sep), 1517-1550. \cr
#' \cr
#' Yu H, Moharil J, Blair RH (2020). BayesNetBP: An R Package for Probabilistic Reasoning in Bayesian
#' Networks. Journal of Statistical Software, 94(3), 1-31. <doi:10.18637/jss.v094.i03>.
#'
#' @examples
#' data(liver)
#' tree.init.p <- Initializer(dag=liver$dag, data=liver$data,
#'                            node.class=liver$node.class,
#'                            propagate = TRUE)
#' @seealso \code{\link{ClusterTreeCompile}}, \code{\link{LocalModelCompile}}, \code{\link{ElimTreeInitialize}},
#' \code{\link{Propagate}}
#'
#' @export

Initializer <- function(dag, data, node.class, propagate = TRUE){
  cst <- ClusterTreeCompile(dag=dag, node.class=node.class)
  models <- LocalModelCompile(data=data, dag=dag, node.class=node.class)
  tree.init <- ElimTreeInitialize(tree=cst$tree.graph,
                                  dag=cst$dag,
                                  model=models,
                                  node.sets=cst$cluster.sets,
                                  node.class=cst$node.class)
  if(propagate) {
    tree.init <- Propagate(tree.init)
  }
  return(tree.init)
}