我的函数应返回一个data.frame对象，该对象给出以下多项式的K倍误差估计：多项式回归，三次样条和平滑样条，其自由度范围为df.min至df.max 。但是，我的平滑样条曲线不能像其他两个模型那样工作，它显示了以下错误：data $ y [-train]-preds2中的错误：二进制运算符的非数字参数。 I wonder why poly and cubic spline is working but not smoothing spline?有人知道如何解决这个问题吗？

# This part is working fine
smoothCV_1 <- function(x,y,K = 10,df.min = 1,df.max = 10) {
  train=sample (c(TRUE,FALSE),nrow(spline.data),rep=TRUE)
  cv.error2 <- rep(0,10)
  for (i in 1:10) {
    if(i >= 3) {
      spline.fit <- lm(y ~ bs(x,i),data = spline.data,subset = train)
      preds1 <- predict(spline.fit,data)[-train]
      cv.error[i]= mean((data$y[-train] - preds1)^2)

      glm.fit = glm(y ~ poly(x,data=spline.data,subset = train)
      preds2 <- predict(glm.fit,data)[-train]
      cv.error2[i]= mean((data$y[-train] - preds2)^2)
    } else {
      cv.error[i]= NA

      glm.fit = glm(y ~ poly(x,data)[-train]
      cv.error2[i]= mean((data$y[-train] - preds2)^2)
    }
  }
  cv.df <- data.frame(degree = 1:10,cv.error = cv.error,cv.error2 = cv.error2)
  return(cv.df)
}
smoothCV_1(x,10,1,10)

# This part is problematic
smoothCV_3 <- function(x,10)
  for (i in 1:10) {
    if(i >= 1) {
      fit2 <- with(smooth.spline(x,df=i),subset = train)
      preds2 <- predict(fit2,data)[-train]
#This part shows error:
#Error in data$y[-train] - preds2 : non-numeric argument to binary operator
      cv.error2[i]= mean((data$y[-train] - preds2)^2)
      print(cv.error[i])
    } else {
      cv.error2[i]= NA
    }
  }
  cv.df <- data.frame(degree = 1:10,cv.error2 = cv.error2)
  return(cv.df)
}
smoothCV_3(x,10)