R 中的 chaid 回归树到表的转换

我使用了 CHAID 包这个链接 http://r-forge.r-project.org/R/?group_id=343..它给了我一个可以绘制的chaid对象..我想要一个决策表，其中每个决策规则在一列中而不是决策树中。 .但我不明白如何访问这个 chaid 对象中的节点和路径..请帮助我.. 我按照中给出的程序进行操作这个链接 http://r.ramganalytics.com/r/chaid-based-decision-tree-in-r/#comment-45

我无法在这里发布我的数据，因为它太长了。所以我发布了一个代码，它采用 chaid 提供的示例数据集来执行任务。

复制自chaid的帮助手册：

library("CHAID")

  ### fit tree to subsample
  set.seed(290875)
  USvoteS <- USvote[sample(1:nrow(USvote), 1000),]

  ctrl <- chaid_control(minsplit = 200, minprob = 0.1)
  chaidUS <- chaid(vote3 ~ ., data = USvoteS, control = ctrl)

  print(chaidUS)
  plot(chaidUS)

Output:

Model formula:
vote3 ~ gender + ager + empstat + educr + marstat

Fitted party:
[1] root
|   [2] marstat in married
|   |   [3] educr <HS, HS, >HS: Gore (n = 311, err = 49.5%)
|   |   [4] educr in College, Post Coll: Bush (n = 249, err = 35.3%)
|   [5] marstat in widowed, divorced, never married
|   |   [6] gender in male: Gore (n = 159, err = 47.8%)
|   |   [7] gender in female
|   |   |   [8] ager in 18-24, 25-34, 35-44, 45-54: Gore (n = 127, err = 22.0%)
|   |   |   [9] ager in 55-64, 65+: Gore (n = 115, err = 40.9%)

Number of inner nodes:    4
Number of terminal nodes: 5

所以我的问题是如何在决策表中获取此树数据，其中每个决策规则（分支/路径）在列中。我不明白如何从这个 chaid 对象访问不同的树路径。

CHAID 包用途partykit http://cran.r-project.org/web/packages/partykit/（递归分区）树结构。您可以使用参与方节点遍历树 - 节点可以是终端节点，也可以具有包含有关决策规则（拆分）和拟合数据信息的节点列表。

下面的代码遍历树并创建决策表。它是为了演示目的而编写的，并且仅在一个示例树上进行了测试。

tree2table <- function(party_tree) {

  df_list <- list()
  var_names <-  attr( party_tree$terms, "term.labels")
  var_levels <- lapply( party_tree$data, levels)

  walk_the_tree <- function(node, rule_branch = NULL) {
    # depth-first walk on partynode structure (recursive function)
    # decision rules are extracted for every branch
    if(missing(rule_branch)) {
      rule_branch <- setNames(data.frame(t(replicate(length(var_names), NA))), var_names)
      rule_branch <- cbind(rule_branch, nodeId = NA)
      rule_branch <- cbind(rule_branch, predict = NA)
    }
    if(is.terminal(node)) {
      rule_branch[["nodeId"]] <- node$id
      rule_branch[["predict"]] <- predict_party(party_tree, node$id) 
      df_list[[as.character(node$id)]] <<- rule_branch
    } else {
      for(i in 1:length(node)) {
        rule_branch1 <- rule_branch
        val1 <- decision_rule(node,i)
        rule_branch1[[names(val1)[1]]] <- val1
        walk_the_tree(node[i], rule_branch1)
      }
    }
  }

  decision_rule <- function(node, i) {
    # returns split decision rule in data.frame with variable name an values
    var_name <- var_names[node$split$varid[[1]]]
    values_vec <- var_levels[[var_name]][ node$split$index == i]
    values_txt <- paste(values_vec, collapse = ", ")
    return( setNames(values_txt, var_name))
  }
  # compile data frame list
  walk_the_tree(party_tree$node)
  # merge all dataframes
  res_table <- Reduce(rbind, df_list)
  return(res_table)
}

使用 CHAID 树对象调用函数：

table1 <- tree2table(chaidUS)

结果应该是这样的：

gender   ager                       empstat   educr              marstat                          nodeId   predict  
-------- -------------------------- --------- ------------------ -------------------------------- -------- ---------
NA       NA                         NA        <HS, HS, >HS       married                          3        Gore     
NA       NA                         NA        College, Post Coll married                          4        Bush     
male     NA                         NA        NA                 widowed, divorced, never married 6        Gore     
female   18-24, 25-34, 35-44, 45-54 NA        NA                 widowed, divorced, never married 8        Gore     
female   55-64, 65+                 NA        NA                 widowed, divorced, never married 9        Gore