R 语言：将循环结果存储到 Table

Question

我正在使用 R 编程语言。我正在学习如何循环一个过程并将结果存储到 table 中。对于这个例子，我首先生成了一些数据：

#load libraries    
library(caret)
library(rpart)
        
#generate data
            
a = rnorm(1000, 10, 10)

b = rnorm(1000, 10, 5)

c = rnorm(1000, 5, 10)

group <- sample( LETTERS[1:2], 1000, replace=TRUE, prob=c(0.5,0.5) )
group_1 <- 1:1000

#put data into a frame
d = data.frame(a,b,c, group, group_1)

d$group = as.factor(d$group)

然后，我创建了最终的 table，我希望在其中存储循环结果：

#create the final results table in which the results of the loop will be stored
final_table = matrix(1, nrow = 6, ncol=2)

这是我要循环的程序。基本上，我想在此数据上拟合决策树模型。我想拟合 6 个不同的决策树：如果“group_1 > i”，变量“group_1”（响应变量）变为因子变量（“1”或“0”）。 “i”变量有 6 个值 (400,401,402,403,404,405)。因此，决策树拟合了 6 次。我想将这些决策树中的每一个的准确性存储到 "final_table":

for (i in 400:405) 
{
  d$group_1 = ifelse(d$group_1 > i, "1","0")
  d$group_1 = as.factor(d$group_1)
  
  
  trainIndex <- createDataPartition(d$group_1, p = .8,
                                    list = FALSE,
                                    times = 1)
  training = d[ trainIndex,]
  test  <- d[-trainIndex,]
  
  
  fitControl <- trainControl(## 10-fold CV
    method = "repeatedcv",
    number = 10,
    ## repeated ten times
    repeats = 10)
  
  TreeFit <- train(group_1 ~ ., data = training,
                   method = "rpart2",
                   trControl = fitControl)
  
  pred = predict(TreeFit, test, type = "prob")
  labels = as.factor(ifelse(pred[,2]>0.5, "1", "0"))
  con = confusionMatrix(labels, test$group_1)
  
  #update results into table
  row = i - 399
  final_table[row,1] = con$overall[1]
  final_table[row,2] = i
}

但是，这给了我以下错误：

Error in na.fail.default(list(group = c(2L, 1L, 2L, 2L, 2L, 2L, 1L, 1L,  : 
      missing values in object
    In addition: Warning message:
    In Ops.factor(d$group_1, i) : ‘>’ not meaningful for factors

有人可以告诉我我做错了什么吗？

谢谢

Answer 1

您可以在任何其他变量中创建原始数据帧的副本，该变量可用于在每次迭代中覆盖已更改的数据帧。

library(caret)
library(rpart)

e <- d

for (i in 400:405) {
  d <- e
  d$group_1 = as.integer(d$group_1 > i)
  d$group_1 = as.factor(d$group_1)
  
  trainIndex <- createDataPartition(d$group_1, p = .8,list = FALSE,times = 1)
  training = d[ trainIndex,]
  test  <- d[-trainIndex,]
  
  
  fitControl <- trainControl(## 10-fold CV
    method = "repeatedcv",
    number = 10,
    ## repeated ten times
    repeats = 10)
  
  TreeFit <- train(group_1 ~ ., data = training,
                   method = "rpart2",
                   trControl = fitControl)
  
  pred = predict(TreeFit, test, type = "prob")
  labels = as.factor(ifelse(pred[,2]>0.5, "1", "0"))
  con = confusionMatrix(labels, test$group_1)
  
  #update results into table
  row = i - 399
  final_table[row,1] = con$overall[1]
  final_table[row,2] = i
  
}

final_table
#      [,1] [,2]
#[1,] 0.585  400
#[2,] 0.618  401
#[3,] 0.598  402
#[4,] 0.608  403
#[5,] 0.533  404
#[6,] 0.570  405

R 语言：将循环结果存储到 Table

R Language: Storing Results of a Loop into a Table

loops

r

data-manipulation

matrix

decision-tree