# R/E1_Marginals.R In BayesNetBP: Bayesian Network Belief Propagation

#' Obtain marginal distributions
#'
#' Get the marginal distributions of multiple variables
#'
#' @details Get the marginal distributions of multiple variables. The function \code{Marginals}
#' returns a \code{list} of marginal distributions. The marginal distribution of a discrete variable
#' is a named vector of probabilities. Meanwhile, the marginal distributions of
#' continous variables in a CG-BN model are mixtures of Gaussian distributions.
#' To fully represent this information, the marginal of a continuous variable is represented by
#' a \code{data.frame} with three columns to specify
#' parameters for each Gaussian distribution in the mixture, which are
#'
#' \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 mixtures}
#' }
#'
#' @param tree a \code{\linkS4class{ClusterTree}} object
#' @param vars a \code{vector} of variables for query of marginal distributions
#'
#' @return
#'
#' \describe{
#'  \item{\code{marginals}}{a \code{list} of marginal distributions}
#'  \item{\code{types}}{a named \code{vector} indicating the types of the variables whose
#'  marginals are queried: \code{TRUE} for discrete, \code{FALSE} for continuous.}
#' }
#'
#' @author Han Yu
#'
#' @references Cowell, R. G. (2005). Local propagation in conditional Gaussian Bayesian networks.
#' Journal of Machine Learning Research, 6(Sep), 1517-1550.
#'
#' @examples
#'
#' data(liver)
#' tree.init.p <- Initializer(dag=liver$dag, data=liver$data,
#'                            node.class=liver$node.class, #' propagate = TRUE) #' tree.post <- AbsorbEvidence(tree.init.p, c("Nr1i3", "chr1_42.65"), list(1,"1")) #' marg <- Marginals(tree.post, c("HDL", "Ppap2a")) #' marg$marginals$HDL #' head(marg$marginals$Ppap2a) #' #' @seealso \code{\link{PlotMarginals}} for visualization of the marginal distributions, #' \code{\link{SummaryMarginals}} for summarization of the marginal distributions of #' continuous variables. #' #' @export Marginals <- function(tree, vars) { if(!tree@propagated) { stop("The ClusterTree object must be propagated before making queries.") } if(sum(vars %in% tree@absorbed.variables)!=0) { var.in <- vars[vars %in% tree@absorbed.variables] msg1 <- paste0(var.in, collapse=", ") stop(paste0(msg1, " is/are already observed.")) } node.class <- tree@node.class marginal.types <- node.class[vars] margs <- list() for (i in 1:length(vars)) { var <- vars[i] if (node.class[[var]]) { margs[[i]] <- DiscreteMarginal(tree, var) } else { margs[[i]] <- PushMarginal(tree, var) } } names(margs) <- vars output <- list() output$marginals <- margs
output\$types <- marginal.types
class(output) <- "marginals"

return(output)
}


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BayesNetBP documentation built on May 2, 2019, 3:43 p.m.