R/select.VAR_estimate.R

In BGGM: Bayesian Gaussian Graphical Models

#' Graph Selection for \code{var.estimate} Object
#'
#' @param object An object of class \code{VAR.estimate}.
#'
#' @param cred Numeric. The credible interval width for selecting the graph
#'  (defaults to 0.95; must be between 0 and 1).
#'
#' @param alternative A character string specifying the alternative hypothesis. It
#'                    must be one of "two.sided" (default), "greater"  or "less".
#'                    See note for futher details.
#'
#' @param ... Currently ignored.
#'
#' @return An object of class \code{select.var_estimate}, including
#'
#' \itemize{
#'
#' \item {pcor_adj} Adjacency matrix for the partial correlations.
#'
#' \item {beta_adj} Adjacency matrix for the regression coefficients.
#'
#' \item {pcor_weighted_adj} Weighted adjacency matrix for the partial correlations.
#'
#' \item {beta_weighted_adj} Weighted adjacency matrix for the regression coefficients.
#'
#' \item \code{pcor_mu} Partial correlation matrix (posterior mean).
#'
#' \item \code{beta_mu} A matrix including the regression coefficients (posterior mean).
#'
#' }
#'
#'
#' @examples
#' \donttest{
#' # data
#' Y <- subset(ifit, id == 1)[,-1]
#'
#' # fit model with alias (var_estimate also works)
#' fit <- var_estimate(Y, progress = FALSE)
#'
#' # select graphs
#' select(fit, cred = 0.95)
#'
#' }
#' @export
select.var_estimate <- function(object,
                                cred = 0.95,
                                alternative = "two.sided"
                                ,...){


  pcors <- object$fit$pcors[,,51:(object$iter +50)]

  pcor_mat <- apply(pcors, 1:2, mean)

  beta <- object$fit$beta[,,51:(object$iter +50)]

  beta_mat <- apply(beta, 1:2, mean)


  if(alternative == "two.sided"){

    lb <- (1 - cred) / 2

    ub <- 1 - lb

    pcor_adj <- ifelse(apply(pcors, 1:2, quantile, lb) < 0 &
                       apply(pcors, 1:2, quantile, ub) > 0, 0, 1)


    beta_adj <- ifelse(apply(beta, 1:2, quantile, lb) < 0 &
                       apply(beta, 1:2, quantile, ub) > 0, 0, 1)

  } else if(alternative == "greater") {

    lb <- (1 - cred)

    pcor_adj <- ifelse(apply(pcors, 1:2, quantile, lb) > 0, 1, 0)

    beta_adj <- ifelse(apply(beta, 1:2, quantile, lb) > 0, 1, 0)


  } else {

    ub <- cred

    pcor_adj <- ifelse(apply(pcors, 1:2, quantile, ub) < 0, 1, 0)

    beta_adj <- ifelse(apply(beta, 1:2, quantile, ub) < 0, 1, 0)

  }

  beta_weighted_adj <-  beta_adj * beta_mat
  pcor_weighted_adj <- pcor_adj * pcor_mat


  returned_object <- list(
    pcor_adj = pcor_adj,
    beta_adj = beta_adj,
    beta_weighted_adj =  beta_weighted_adj,
    pcor_weighted_adj =  pcor_weighted_adj,
    beta_mu = beta_mat,
    pcor_mu = pcor_mat,
    alternative = alternative,
    cred = cred,
    object = object
  )

  class(returned_object) <- c("BGGM",
                              "select.var_estimate",
                              "var_estimate",
                              "select")
  return(returned_object)

}

print_select_var_estimate <- function(x, ...){
  object <- x
  p <- ncol(object$pcor_adj)
  cat("BGGM: Bayesian Gaussian Graphical Models \n")
  cat("--- \n")
  cat("Vector Autoregressive Model (VAR) \n")
  cat("--- \n")
  cat("Posterior Samples:", object$object$iter, "\n")
  cat("Credible Interval:",
      gsub("*0.","", formatC( round(object$cred, 4), format='f', digits=2)),
      "% \n")
  cat("--- \n")
  cat("Call: \n")
  print(object$object$call)
  cat("--- \n")
  cat("Partial Correlations: \n\n")
  colnames(object$pcor_weighted_adj) <- colnames(object$object$Y)
  row.names(object$pcor_weighted_adj) <- colnames(object$object$Y)
  print(round(object$pcor_weighted_adj, 3))
  cat("--- \n")
  cat("Coefficients: \n\n")
  colnames(object$beta_weighted_adj) <- colnames(object$object$Y)
  row.names(object$beta_weighted_adj) <- colnames(object$object$X)
  print(round(object$beta_weighted_adj, 3))
  cat("--- \n")
}