大家好:
小弟想將每個自變量逐步丟入隨機森林建模，如第一個模型結果會是投入1個自變量，第二個模型是投入2個，以此類推，以圖中iris資料為例，會共做4次模型，產出4個結果，但是我的迴圈只跑了頭跟尾，也就是1、4個自變量，漏了2、3個自變量的模型結果，想問問要怎麼改

data(iris)

library(dplyr)
library(MLmetrics)
library(randomForest)
#iris <-  subset(iris,Species != "setosa")
out_c = data.frame()
out_p = data.frame()
out_r = data.frame()
out_f = data.frame()

n <- nrow(iris)
random <- sample(seq_len(n), size = round(0.7 * n))
train <- iris[random,]
test <- iris[-random,]

for(i in range(1,4)){
  traindata <- train[,c(i,5)]
  testdata <- test[,c(i,5)]
  features <- setdiff(x = names(traindata), y = "Species")
  set.seed(123)
  c <- as.data.frame(tuneRF(x = traindata[features], y = traindata$Species,ntreeTry = 500)) #因建模預設500
  out_c = rbind(out_c, c$mtry[which.min(c$OOBError)])
  rf_model <- randomForest(Species~., data = traindata,
                           ntree = 500, mtry = c$mtry[which.min(c$OOBError)],
                           do.trace = 100,na.action = na.roughfix)
  rf_future <- predict(rf_model,testdata)
  rf_future <- as.data.frame(rf_future)
  rf_final <- cbind(rf_future,testdata)
  p <- Precision(rf_final$Species, rf_final$rf_future, positive = 'versicolor')
  out_p = rbind(out_p, p) 
  r <- Recall(rf_final$Species, rf_final$rf_future, positive = 'versicolor')
  out_r = rbind(out_r, r) 
  f <- F1_Score(rf_final$Species, rf_final$rf_future, positive = 'versicolor')
  out_f = rbind(out_f, f) 
  print(i)
}