R/NodeTreePlot.R

In moreparty: A Toolbox for Conditional Inference Trees and Random Forests

#' @importFrom rlang .data
#' @importFrom stats weighted.mean

#' @export

NodeTreePlot <- function(ct) {
  
  response <- ct$fitted[,"(response)"]
  
  if(is.numeric(response)) {
    
      rule <- list.rules.party(ct)
      rule <- as.character(rule)
      rule <- gsub("%in%", "in", rule, fixed = TRUE)
      rule <- gsub("c(", "(", rule, fixed = TRUE)
      rule <- gsub('\"', "", rule, fixed = TRUE)
      rule <- gsub("NA, ", "", rule)
      rule <- gsub(", NA", "", rule)
      rule <- sapply(rule, simplify_rule)
      rule <- factor(rule)
      
      levels(rule) <- gsub(" & ", "\n", levels(rule))
      
      df <- ct$fitted
      names(df) <- c("node", "weights", "response")
      df$node <- factor(df$node)
      
      freq <- table(df$node)
      freq <- data.frame(node = names(freq), freq = as.numeric(freq))
      
      rule <- data.frame(node = factor(levels(df$node)), rule)
      rule <- merge(rule, freq, sort = FALSE)
      rule$rule <- paste0(rule$rule, "\n(n=", rule$freq, ")")
      df <- merge(df, rule, by = "node")
      
      moy <- sapply(split(df, df$rule), function(x) stats::weighted.mean(x$response, x$weights, na.rm = TRUE))
      sorted_levs <- names(sort(moy))
      df$rule <- factor(df$rule, levels = sorted_levs)
      
      moy <- data.frame(rule = factor(names(moy)), moy)
      df <- merge(df, moy, by = "rule")
      
      colors <- c("#009392FF","#39B185FF","#9CCB86FF","#E9E29CFF","#EEB479FF","#E88471FF","#CF597EFF")
      
      p <- ggplot2::ggplot(df, ggplot2::aes(x = .data$rule, y = .data$response, fill = .data$moy)) +
            ggplot2::geom_boxplot() +
            ggplot2::coord_flip() +
            ggplot2::ylab(names(ct$data[1])) +
            ggplot2::scale_fill_gradientn(colours = colors) +
            ggplot2:: theme_minimal() +
            ggplot2::theme(panel.grid.minor.x = ggplot2::element_blank(),
                           panel.grid.minor.y = ggplot2::element_blank())
      
  } else if(is.factor(response)) {
    if(nlevels(response) == 2) {
      
      rule <- list.rules.party(ct)
      rule <- as.character(rule)
      rule <- gsub("%in%", "in", rule, fixed = TRUE)
      rule <- gsub("c(", "(", rule, fixed = TRUE)
      rule <- gsub('\"', "", rule, fixed = TRUE)
      rule <- gsub("NA, ", "", rule)
      rule <- gsub(", NA", "", rule)
      rule <- sapply(rule, simplify_rule)
    
      # rule <- sapply(strwrap(rule, width=40, simplify=FALSE), paste, collapse="\n")
      rule <- gsub(" & ", "\n", rule)
      
      df <- ct$fitted
      names(df) <- c("node", "weights", "response")
      df$node <- factor(df$node)
      df$response_num <- as.numeric(df$response==levels(df$response)[2])
      
      rule <- data.frame(node = factor(levels(df$node)), rule)
      df <- merge(df, rule, by = "node", sort = FALSE)
      
      moy <- sapply(split(df, df$rule), function(x) stats::weighted.mean(x$response_num, x$weights, na.rm = TRUE))
      moy <- data.frame(rule = names(moy), moy)
      moy$freq <- as.numeric(table(df$rule))
      moy <- moy[order(moy$moy), ]
      moy$rule <- paste0(moy$rule, "\n(n=", moy$freq, ")")
      moy$rule <- factor(moy$rule, levels = moy$rule)
      
      colors <- c("#009392FF","#39B185FF","#9CCB86FF","#E9E29CFF","#EEB479FF","#E88471FF","#CF597EFF")
      
      p <- ggplot2::ggplot(moy, ggplot2::aes(x = .data$rule, y = .data$moy, color = .data$moy)) +
            ggplot2::geom_segment(ggplot2::aes(x = .data$rule, xend = .data$rule, y = 0, yend = .data$moy)) +
            ggplot2::geom_point(size = 4) +
            ggplot2::coord_flip() +
            ggplot2::ylab(names(ct$data[1])) +
            ggplot2::scale_color_gradientn(colours = colors) +
            ggplot2::theme_minimal() +
            ggplot2::theme(panel.grid.minor.x = ggplot2::element_blank(),
                           panel.grid.major.y = ggplot2::element_blank(),
                           panel.grid.minor.y = ggplot2::element_blank())
    }
  }
  return(p)
}