R/B2_LocalModelCompile.R

In BayesNetBP: Bayesian Network Belief Propagation

#' Model compilation
#'
#' Compile the local models
#'
#' @details This function compiles the local models, including the conditional
#' probability tables for discrete variables, and linear predictor potentials
#' for continuous variables. The qtlnet and qtl package need to be installed if data is
#' a \code{qtlnet} object.
#'
#' @param data a \code{data.frame} object or a \code{qtlnet} object
#' @param dag \code{NULL} if data is \code{qtlnet} object, or a \code{graphNEL} object of conditional
#' Gaussian Bayesian network if data is \code{data.frame}.
#' @param node.class \code{NULL} if data is \code{qtlnet} object, or a \code{vector} of logical values
#' named by node names, \code{TRUE} for discrete, \code{FALSE} for continuous variables if data
#' is \code{data.frame}.
#'
#' @return
#' \describe{
#' \item{\code{pots}}{a \code{list} of discrete potentials (conditional probability tables)
#' for each discrete variable. }
#' \item{\code{bags}}{a \code{list} of sets of continuous potentials (lppotentials), each set for a
#' continuous variables.}
#' }
#'
#' @import doBy
#' @importFrom graph nodes
#' @importFrom igraph igraph.options
#' @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)
#' models <- LocalModelCompile(data=liver$data, dag=liver$dag, node.class=liver$node.class)
#'
#' @seealso \code{\link{ElimTreeInitialize}}
#'
#' @export

LocalModelCompile <- function(data, dag=NULL, node.class=NULL) {

  if("qtlnet" %in% class(data)){
    extr <- extractQTL(data)
    data <- extr$data
    dag <- extr$dag
    node.class <- extr$node.class
  }

  models <- ModelCompileData(data, dag, node.class)
  return(models)
}

# @importFrom qtl find.marker
# @importFrom qtl scanone