如何对 R 中的预测数据应用 wilcox 检验？

Question

当我们有一个数据文件时，我们使用以下代码进行 k 折交叉验证训练数据，

set.seed(308)

rand_search <- train(
    Effort ~ ., data = d,
    method = "svmRadial",
    ##Create 20 random parameter values
    tuneLength = 20,
    metric = "RMSE",
    preProc = c("center", "scale"),
    trControl = rand_ctrl
) 
  model1 <- predict(rand_search, newdata = test1)

And another search algorithm like grid
grid_search <- train(
    Effort ~ ., data = d,
    method = "svmRadial",
    ##Create 20 random parameter values
    tuneLength = 20,
    metric = "RMSE",
    preProc = c("center", "scale"),
    trControl = rand_ctrl
) 
model2 <- predict(grid_search, newdata = test1)

我的问题是如果我们必须找到显着性检验（wilcox检验），我们如何应用它？我们是否需要像下面那样将模式 1 和模式 2 传递给 wilcox 测试？

wilcox.test（模型 1，模型 2）

Answer 1

在trainControl中不需要指定数据。在 train 函数中，您必须提及

之类的数据

#Model training
set.seed(308) 
rand_search <- train(Effort ~ ., data = train1 ,
                                method = "svmRadial",
                                ## Create 20 random parameter values
                                tuneLength = 20,
                                metric = "RMSE",
                                preProc = c("center", "scale"),
                                trControl = rand_ctrl)

并且 test1 应该用于预测目的，例如

#For calibration
models_cal <- predict(rand_search, newdata = train1)
#For independent validation
models_val <- predict(rand_search, newdata = test1)

如何对 R 中的预测数据应用 wilcox 检验？

How to apply wilcox test on the predicted data in R?

r

predict

statistical-test