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+#' plot the marginal probability for top predictors
+#'
+#' @param result the result from IBGS
+#' @param n.vars the number of top predictors
+
+#' @export
+
+plots.Gibbs <- function(result, n.vars = 20){
+  colors  <- rep(0,n.vars)
+  v.order <- order(result$v.prob, decreasing = TRUE)
+  v.freq  <- result$v.prob[v.order[1:n.vars]]
+
+  colors[v.freq >  result$tau]    <- 2
+  colors[v.freq <= result$tau ]   <- 1
+  plot(1:n.vars, v.freq, xlab = "", ylab = "Marginal Probability",
+       xaxt = "n", main = "", type = "h", col = colors, ylim = c(0,1))
+  mtext(result$x.predictors[v.order[1:n.vars]], side = 1, line = 0.25,
+        at = 1:n.vars, las = 2, cex = 1)
+}

.Rproj.user/3992D3D4/sources/s-54CE4874/24ECBBFE-contents

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+#' a summary of partial results
+#'
+#' @param m.matrix  a matrix of generated samples
+#' @param y         the response variable
+#' @param x         the predictors
+#' @param n.models  the number of top selected models
+#' @param info      the selected model selection criterion from AIC, AICc, BIC and exBIC
+#' @param family    the type of model from linear, logistic, poisson
+#' @param k         the tuning parameter
+#' @param gamma     the tuning parameter to control the penalty
+#' @param p0        the number of total predictors
+#'
+#' @return a list containing partial results
+#' @export
+
+result.GibbsSampler <- function(m.matrix, y, x, k, gamma, p0, n.models, info, family){
+  z <- as.data.frame(cbind(y,x))
+  colnames(z)[1] <- "y"
+
+  n <- dim(m.matrix)[1]
+  m.sic <- vector()
+  for(i in 1:n){
+    m.sic <- c(m.sic, v.sic(m.matrix[i,], y, x, gamma, p0, info, family))
+  }
+
+  m.sic.df <- as.data.frame(table(m.sic))
+  m.sics <- as.numeric(levels(m.sic.df[,1]))
+
+  if(length(m.sics) < n.models)
+    n.models <- length(m.sics)
+
+  m.order <- order(m.sic, decreasing = FALSE)
+  m.index <- cumsum(m.sic.df[,2])
+  m.models <- list()
+  for (i in 1:n.models) {
+    model <- glm(y~., z[,m.matrix[m.order[m.index[i]],]==1], family = family)
+    m.models[[i]] <- model
+  }
+  m.sicc <- m.sics[1:n.models]
+  m.weights <- weight(m.sicc, k)
+
+  return(list(n.models = n.models,
+              m.sics   = m.sics,
+              m.sic    = m.sic,
+              m.seq    = m.matrix[,-1],
+              c.models = list(models  = m.models,
+                              weights = m.weights),
+              k        = k,
+              info     = info,
+              family   = family
+  ))
+}

.Rproj.user/3992D3D4/sources/s-54CE4874/2856D250-contents

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+#' The second step in the block Gibbs sampler search algorithm in the iterations
+#'
+#' @param y        the response variable
+#' @param x1       the non-important predictors
+#' @param x2       the important predictors
+#' @param H        the number of predictors in small groups
+#' @param kapp     the number of selected predictors in first step
+#' @param tau      the threshold to select the important predictors in second step
+#' @param len      the half number of generated samples
+#' @param k        the tuning parameter
+#' @param gamma    the parameter for extended BIC
+#' @param p0       the number of all predictors
+#' @param info     the selected model selection criterion
+#' @param family   the type of model
+
+#' @return  the number of selected columns
+#' @export
+
+BlockGibbsSampler.step2 <- function(y, x1, x2, H, kapp, tau, len,
+                                    k, gamma, p0, info, family){
+  x  <- cbind(x1,x2)
+  z  <- as.data.frame(cbind(y,x1,x2))
+  colnames(z)[1] <- "y"
+
+  n  <- dim(x1)[1]
+  p1 <- dim(x1)[2]
+  if(is.null(dim(x2))){
+    p2 <- 0
+  }else
+    p2 <- dim(x2)[2]
+
+  h  <- ceiling(dim(x1)[2]/(min(H, n-p2)))
+  v.freq   <- BlockGibbsSampler.step1(y, x1, x2, h, len, k, gamma, p0, info, family)
+
+  x.s      <- cbind(x1[,order(v.freq, decreasing = TRUE)[1:kapp]], x2)
+  p.s      <- dim(x.s)[2]
+
+  s.index   <- rep(1, p.s)
+  m.matrix  <- GibbsSamplerStep(y, x.s, x2 = vector(), s.index, len,
+                            k, gamma, p0, info, family)
+  v.freq2    <- colSums(m.matrix)/len
+
+  if(sum(v.freq2[-1] > tau) > 1){
+    v.select   <- as.numeric(colnames(x.s)[v.freq2[-1] > tau])
+  }
+  else{
+    v.select   <- as.numeric(colnames(x.s))
+  }
+  return(sort(v.select))
+}

.Rproj.user/3992D3D4/sources/s-54CE4874/3A7AE6B8-contents

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+# Generated by roxygen2: do not edit by hand
+
+export(AICc)
+export(BlockGibbsSampler)
+export(BlockGibbsSampler.step1)
+export(BlockGibbsSampler.step2)
+export(BlockGibbsSampler.step3)
+export(GibbsSamplerStep)
+export(ac.ratio)
+export(burn.seq)
+export(exBIC)
+export(plots.Gibbs)
+export(predicts.Gibbs)
+export(r.index)
+export(result.GibbsSampler)
+export(sigmoid)
+export(v.sic)
+export(weight)

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