# resamples

##### Collation and Visualization of Resampling Results

These functions provide methods for collection, analyzing and visualizing a set of resampling results from a common data set.

- Keywords
- models

##### Usage

`resamples(x, ...)`## S3 method for class 'default':
resamples(x, modelNames = names(x), ...)

## S3 method for class 'resamples':
summary(object, ...)

##### Arguments

##### Details

The ideas and methods here are based on Hothorn et al (2005) and Eugster et al (2008).

The results from `train`

can have more than one performance metric per resample. Each metric in the input object is saved.

`resamples`

checks that the resampling results match; that is, the indices in the object `trainObject$control$index`

are the same. Also, the argument `trainControl`

`returnResamp`

should have a value of `"final"`

for each model.

The summary function computes summary statistics across each model/metric combination.

##### Value

- An object with class
`"resamples"`

with elements call the call values a data frame of results where rows correspond to resampled data sets and columns indicate the model and metric models a character string of model labels metrics a character string of performance metrics methods a character string of the `train`

`method`

argument values for each model

##### References

Hothorn et al. The design and analysis of benchmark experiments. Journal of Computational and Graphical Statistics (2005) vol. 14 (3) pp. 675-699

Eugster et al. Exploratory and inferential analysis of benchmark experiments. Ludwigs-Maximilians-Universitat Munchen, Department of Statistics, Tech. Rep (2008) vol. 30

##### See Also

`train`

, `trainControl`

, `diff.resamples`

, `xyplot.resamples`

, `densityplot.resamples`

, `bwplot.resamples`

, `splom.resamples`

##### Examples

```
data(BloodBrain)
set.seed(1)
tmp <- createDataPartition(logBBB,
p = .8,
times = 100)
rpartFit <- train(bbbDescr, logBBB,
"rpart",
tuneLength = 16,
trControl = trainControl(
method = "LGOCV", index = tmp))
ctreeFit <- train(bbbDescr, logBBB,
"ctree",
trControl = trainControl(
method = "LGOCV", index = tmp))
earthFit <- train(bbbDescr, logBBB,
"earth",
tuneLength = 20,
trControl = trainControl(
method = "LGOCV", index = tmp))
## or load pre-calculated results using:
## load(url("http://caret.r-forge.r-project.org/Classification_and_Regression_Training_files/exampleModels.RData"))
resamps <- resamples(list(CART = rpartFit,
CondInfTree = ctreeFit,
MARS = earthFit))
resamps
summary(resamps)
```

*Documentation reproduced from package caret, version 4.65, License: GPL-2*