# 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 default
resamples(x, modelNames = names(x), ...)

# S3 method for resamples
sort(x, decreasing = FALSE, metric = x$metric[1], FUN = mean, ...)

# S3 method for resamples
summary(object, metric = object$metrics, ...)

# S3 method for resamples
as.matrix(x, metric = x$metric[1], ...)

# S3 method for resamples
as.data.frame(x, row.names = NULL, optional = FALSE, metric = x$metric[1], ...)

modelCor(x, metric = x$metric[1], ...)

# S3 method for resamples
print(x, ...)

##### Arguments

- x
a list of two or more objects of class

`train`

,`sbf`

or`rfe`

with a common set of resampling indices in the`control`

object. For`sort.resamples`

, it is an object generated by`resamples`

.- …
only used for

`sort`

and`modelCor`

and captures arguments to pass to`sort`

or`FUN`

.- modelNames
an optional set of names to give to the resampling results

- decreasing
logical. Should the sort be increasing or decreasing?

- metric
a character string for the performance measure used to sort or computing the between-model correlations

- FUN
a function whose first argument is a vector and returns a scalar, to be applied to each model's performance measure.

- object
an object generated by

`resamples`

- row.names, optional
not currently used but included for consistency with

`as.data.frame`

##### 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

For `resamples`

: an object with class `"resamples"`

with
elements

the call

a data frame of results where rows correspond to resampled data sets and columns indicate the model and metric

a character string of model labels

a character string of performance metrics

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

```
# NOT RUN {
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/exampleModels.RData"))
## resamps <- resamples(list(CART = rpartFit,
## CondInfTree = ctreeFit,
## MARS = earthFit))
## resamps
## summary(resamps)
# }
```

*Documentation reproduced from package caret, version 6.0-85, License: GPL (>= 2)*