> require(party)
> require(mlbench)
> data(BreastCancer)
> BreastCancer$Id <- NULL
> grid <- expand.grid(.maxdepth=c(2:6), .mincriterion=c(0.95, 0.97, 0.99))
> print(grid)
   .maxdepth .mincriterion
1          2          0.95
2          3          0.95
3          4          0.95
4          5          0.95
5          6          0.95
6          2          0.97
7          3          0.97
8          4          0.97
9          5          0.97
10         6          0.97
11         2          0.99
12         3          0.99
13         4          0.99
14         5          0.99
15         6          0.99
> ct.best <- train(Class ~ . , data=BreastCancer, method="ctree", tuneGrid=grid)
Loading required package: class

Attaching package: 'class'

The following object(s) are masked from 'package:reshape':

    condense


Attaching package: 'e1071'

The following object(s) are masked from 'package:gtools':

    permutations

Fitting: maxdepth=2, mincriterion=0.95 
Fitting: maxdepth=3, mincriterion=0.95 
Fitting: maxdepth=4, mincriterion=0.95 
Fitting: maxdepth=5, mincriterion=0.95 
Fitting: maxdepth=6, mincriterion=0.95 


> grid <- expand.grid(.maxdepth=c(2, 4), .mincriterion=c(0.95, 0.99))
> print(grid)
  .maxdepth .mincriterion
1         2          0.95
2         4          0.95
3         2          0.99
4         4          0.99
> ct.best <- train(Class ~ . , data=BreastCancer, method="ctree", tuneGrid=grid)
Fitting: maxdepth=2, mincriterion=0.95 
Fitting: maxdepth=4, mincriterion=0.95 
Fitting: maxdepth=2, mincriterion=0.99 
Fitting: maxdepth=4, mincriterion=0.99 
Aggregating results
Selecting tuning parameters
Fitting model on full training set
> print(ct.best)
683 samples
 80 predictors

Pre-processing: None 
Resampling: Bootstrap (25 reps) 

Summary of sample sizes: 683, 683, 683, 683, 683, 683, ... 

Resampling results across tuning parameters:

  mincriterion  Accuracy  Kappa  maxdepth  Accuracy SD  Kappa SD  maxdepth SD
  0.95          0.939     0.867  3         0.0156       0.0337    1.01       
  0.99          0.94      0.868  3         0.0157       0.0337    1.01       

Accuracy was used to select the optimal model using  the largest value.
The final value used for the model was mincriterion = 0.99. 
> 
> 
> 
> 
> grid <- expand.grid(.maxdepth=c(2, 4), .mincriterion=c(0.95, 0.99))
> print(grid)
  .maxdepth .mincriterion
1         2          0.95
2         4          0.95
3         2          0.99
4         4          0.99
> ct.best <- train(Class ~ . , data=BreastCancer, method="ctree2", tuneGrid=grid)
Fitting: maxdepth=2, mincriterion=0.95 
Fitting: maxdepth=4, mincriterion=0.95 
Fitting: maxdepth=2, mincriterion=0.99 
Fitting: maxdepth=4, mincriterion=0.99 
Aggregating results
Selecting tuning parameters
Fitting model on full training set
> print(ct.best)
683 samples
 80 predictors

Pre-processing: None 
Resampling: Bootstrap (25 reps) 

Summary of sample sizes: 683, 683, 683, 683, 683, 683, ... 

Resampling results across tuning parameters:

  maxdepth  Accuracy  Kappa  mincriterion  Accuracy SD  Kappa SD  mincriterion SD
  2         0.935     0.858  0.97          0.0163       0.0343    0.0202         
  4         0.935     0.857  0.97          0.0142       0.0322    0.0202         

Accuracy was used to select the optimal model using  the largest value.
The final value used for the model was maxdepth = 4.