#logistic- caret::train
#划分数据集
set.seed(123)
folds <- createFolds(y=data$Groups,k=10)

# 建一个放auc值的空向量
    auc<-as.numeric()
    Errorrate<-as.numeric()
    accuracy<-as.numeric()
    sensitivity<-as.numeric()
    specificity<-as.numeric()
    roc <- vector("list", 10)

#设置交叉验证参数
    set.seed(123)   #使结果具有可重复性
    trainControl<- trainControl(method = "cv", number = 10)

for(i in 1:10){
    test <- data[folds[[i]],]  
    train <- data[-folds[[i]],]    
    logit<- caret::train(Groups ~ ., data = train,
        family = binomial(link = "logit"),
        trainControl= trainControl
        #linout = FALSE,
        #trace = FALSE
        )

    #预测
        pred &l