R插入符：调整GLM提升修剪参数

我正在尝试调整GLM增强模型的参数 . 根据有关此模型的Caret package documentation，有2个参数可以调整，mstop和prune .

library(caret)
    library(mlbench)

    data(Sonar)

    set.seed(25)
    trainIndex = createDataPartition(Sonar$Class, p = 0.9, list = FALSE)
    training = Sonar[ trainIndex,]
    testing  = Sonar[-trainIndex,]

    ### set training parameters
    fitControl = trainControl(method = "repeatedcv",
                              number = 10,
                              repeats = 10,
                              ## Estimate class probabilities
                              classProbs = TRUE,
                              ## Evaluate a two-class performances  
                              ## (ROC, sensitivity, specificity) using the following function 
                              summaryFunction = twoClassSummary)

    ### train the models
    set.seed(69)
    # Use the expand.grid to specify the search space   
    glmBoostGrid = expand.grid(mstop = c(50, 100, 150, 200, 250, 300),
                               prune = c('yes', 'no'))

    glmBoostFit = train(Class ~ ., 
                        data = training,
                        method = "glmboost",
                        trControl = fitControl,
                        tuneGrid = glmBoostGrid,
                        metric = 'ROC')
glmBoostFit

输出如下：

Boosted Generalized Linear Model 

188 samples
 60 predictors
  2 classes: 'M', 'R' 

No pre-processing
Resampling: Cross-Validated (10 fold, repeated 10 times) 
Summary of sample sizes: 169, 169, 169, 169, 170, 169, ... 
Resampling results across tuning parameters:

  mstop  ROC        Sens   Spec       ROC SD      Sens SD    Spec SD  
   50    0.8261806  0.764  0.7598611  0.10208114  0.1311104  0.1539477
  100    0.8265972  0.729  0.7625000  0.09459835  0.1391250  0.1385465
  150    0.8282083  0.717  0.7726389  0.09570417  0.1418152  0.1382405
  200    0.8307917  0.714  0.7769444  0.09484042  0.1439011  0.1452857
  250    0.8306667  0.719  0.7756944  0.09452604  0.1436740  0.1535578
  300    0.8278403  0.728  0.7722222  0.09794868  0.1425398  0.1576030

Tuning parameter 'prune' was held constant at a value of yes
ROC was used to select the optimal model using  the largest value.
The final values used for the model were mstop = 200 and prune = yes.

prune参数保持不变（ Tuning parameter 'prune' was held constant at a value of yes ），尽管 glmBoostGrid 也包含 prune == no . 我在 boost_control 方法中查看了 mboost 包文档，只能访问 mstop 参数，那么如何使用 train 方法的 tuneGrid 参数调整 prune 参数？