Question

我正在运行r脚本来交叉验证ROC曲线的AUC。我有70个人的数据集； 24有病，46没有病。我列出了在Logistic回归模型中用于开发预测模型的八个变量。

问题是，当我使用cvAUC包中的iid_example时，它返回错误：“ Y0 [[v]]中的错误：下标超出范围”。

我寻求帮助堆栈溢出，并了解这可能是由于行数不正确而导致的，但我找不到。有人可以帮我吗！。

R代码如下所示。 “黄金”是结果，其余变量是协变量。没有具体的暴露变量。

# Create CV folds (stratify by outcome)
.cvFolds <- function(Y, V){
  Y0 <- split(sample(which(Y == 0)),
              rep(1:V, length = length(which(Y == 0))))
  Y1 <- split(sample(which(Y == 1)),
              rep(1:V, length = length(which(Y == 1))))
  folds <- vector("list", length = V)
  for (v in seq(V)) {folds[[v]] <- c(Y0[[v]], Y1[[v]])}
  return(folds)
}

# Train/test glm for each fold
.doFit <- function(v, folds, data){
  fit <- glm(Y ~ ., data = data[-folds[[v]],], family = "binomial")
  pred <- predict(fit, newdata = data[folds[[v]],], type = "response")
  return(pred)
}

iid_example <- function(data, y = "V1", V = 10){
  # Create folds
  folds <- .cvFolds(Y = data$Y, V = V)
  # CV train/predict
  predictions <- unlist(sapply(seq(V), .doFit,
                               folds = folds, data = data))
  # Re-order pred values
  predictions[unlist(folds)] <- predictions
  # Get CV AUC and confidence interval
  out <- ci.cvAUC(predictions = predictions, labels = data$Y,
                  folds = folds, confidence = 0.95)
  return(out)
}


# Load a training set with a binary outcome
train <- tb[ tb$zn == "Negative", c("inf", "tnf", "il2", "il6", "il10", "cd4_200", "cd8_1000","gold")]



library(cvAUC)

# Get cross-validated performance
set.seed(1)
out <- iid_example( data = train, y = "gold", V = 10 )

使用逻辑回归对AOC进行ROC曲线的交叉验证

0 个答案: