R/F2_SummaryMarginal.R

In BayesNetBP: Bayesian Network Belief Propagation

#' Summary a continuous marginal distribution
#'
#' This function summary the marginal distributions of continuous variables by outputing the
#' mean, standard deviation, and number of subpopulations
#'
#' @param marginals the marginal distributions obtained from \code{\link{Marginals}} function
#'
#' @return a \code{data.frame} object containing information about the marginal distributions for continuous variables.
#' The marginal distributions of continous variables in a CG-BN model are mixtures of Gaussian distributions.
#' Therefore, besides the mean and standard deviation, the object has an additional column to specify the number of Gaussian
#' mixtures.
#'
#' \describe{
#'  \item{\code{mean}}{the mean value of a Gaussian distribution.}
#'  \item{\code{sd}}{the standard deviation of a Gaussian distribution.}
#'  \item{\code{n}}{the number of Gaussian distributions in the mixture.}
#' }
#'
#' @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)
#' marg <- Marginals(tree.init.p, c("HDL", "Ppap2a", "Neu1"))
#' SummaryMarginals(marginals=marg)
#'
#' @seealso \code{\link{Marginals}}
#'
#' @export

SummaryMarginals <- function(marginals) {

  margs <- marginals$marginals
  nms0 <- names(margs)
  types <- marginals$types

  nms <- mu <- sd <- n <-c()

  for (i in 1:length(types)) {
    if(!types[i]) {
      msd <- MeanSD(margs[[i]])
      nms <- c(nms, nms0[i])
      mu <- c(mu, msd[1])
      sd <- c(sd, msd[2])
      n <- c(n, nrow(margs[[i]]))
    }
  }

  df <- data.frame(Mean=mu, SD=sd, n=n)
  rownames(df) <- nms
  return(df)
}