在函数中传递参数时未找到R eval(predvars,data,env)对象

kiz8lqtg  于 2022-12-20  发布在  其他
关注(0)|答案(1)|浏览(633)

我的可重复的例子如下;
请不要打扰在所有的潜在意义的计算(没有,实际上),因为它只是一个摘录我的真实的数据集;

train <- structure(list(no2 = c(25.5, 31.2, 33.4, 29.9, 31.8),
                        vv_scal = c(1.3, 1.3, 0.8, 1.1, 0.9), 
                        temp = c(-0.7, -2, 1.5, 0.4, 1.1), 
                        prec = c(0, 11, 9, 3, 0), 
                        co = c(1.6, 2.9, 3.2, 2.6, 3)), 
                        row.names = c(NA, -5L), 
                        class = c("tbl_df", "tbl", "data.frame"))

test <- structure(list(no2 = c(41.6, 41.4, 46.6, 44.7, 43.2), 
                       vv_scal = c(1.2, 1.2, 1.2, 1, 1), 
                       temp = c(0.9, 1, 0.1, 1.6, 3.8), 
                       prec = c(0, 0, 0, 0, 0), 
                       co = c(4.3, 4.3, 4.9, 4.7, 4.5)), 
                       row.names = c(NA, -5L), 
                       class = c("tbl_df", "tbl", "data.frame"))
                       
                       

forest_ci <- function(B, train_df, test_df, var_rf){
  
  # Initialize a matrix to store the predicted values
  predictions <- matrix(nrow = B, ncol = nrow(test_df))
  
  # bootstrapping predictions
  for (b in 1:B) {
    
    # Fit a random forest model
    model <- randomForest::randomForest(var_rf~., data = train_df) # not working
    #model <- randomForest::randomForest(no2~., data = train_df)   # working
    
    # Store the predicted values from the resampled model
    predictions[b, ] <- predict(model, newdata = test_df)
    
  }
  
  predictions
  
}

predictions <- forest_ci(B=2, train_df=train, test_df=test, var_rf = no2)

我收到以下错误消息:

Error in eval(predvars, data, env) : object 'no2' not found

我认为理解这个错误与“非标准求值”和“捕获表达式”的概念有关
http://adv-r.had.co.nz/Computing-on-the-language.html
根据一些线索的建议,以下是其中一些线索:
how do I pass a variable name to an argument in a function
Passing a variable name to a function in R
我一直在尝试使用不同的功能组合:substitute()、eval()、quote(),但没有太多成功;
我知道这个问题已经在这里讨论过了,但我到目前为止还找不到一个合适的解决方案;
我的目标是在函数参数中传递变量名,以便在随机森林模型提供的回归(和预测)中进行计算
谢啦,谢啦

3pmvbmvn

3pmvbmvn1#

尝试使用rlang中的ensym()inject()

forest_ci <- function(B, train_df, test_df, var_rf){
  
  y = rlang::ensym(var_rf)
  
  # Initialize a matrix to store the predicted values
  predictions <- matrix(nrow = B, ncol = nrow(test_df))
  
  # bootstrapping predictions
  for (b in 1:B) {
    
    # Fit a random forest model
    model <- rlang::inject(randomForest::randomForest(!!y~., data = train_df)) # not working
    #model <- randomForest::randomForest(no2~., data = train_df)   # working
    
    # Store the predicted values from the resampled model
    predictions[b, ] <- predict(model, newdata = test_df)
    
  }
  
  predictions
  
}

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