# xyplot.resamples

##### Lattice Functions for Visualizing Resampling Results

Lattice functions for visualizing resampling results across models

- Keywords
- hplot

##### Usage

```
## S3 method for class 'resamples':
xyplot(x, data = NULL, what = "scatter", models = NULL,
metric = x$metric[1], units = "min", ...)
```## S3 method for class 'resamples':
dotplot(x, data = NULL, models = x$models,
metric = x$metric, conf.level = 0.95, ...)

## S3 method for class 'resamples':
densityplot(x, data = NULL, models = x$models, metric = x$metric, ...)

## S3 method for class 'resamples':
bwplot(x, data = NULL, models = x$models, metric = x$metric, ...)

## S3 method for class 'resamples':
splom(x, data = NULL, variables = "models",
models = x$models, metric = NULL, panelRange = NULL, ...)

## S3 method for class 'resamples':
parallel(x, data = NULL, models = x$models, metric = x$metric[1], ...)

##### Details

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

`dotplot`

plots the average performance value (with two-sided confidence limits) for each model and metric.

`densityplot`

and `bwplot`

display univariate visualizations of the resampling distributions while `splom`

shows the pair-wise relationships.

##### Value

- a lattice object

##### 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

##### Examples

```
#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))
dotplot(resamps,
scales =list(x = list(relation = "free")),
between = list(x = 2))
bwplot(resamps,
metric = "RMSE")
densityplot(resamps,
auto.key = list(columns = 3),
pch = "|")
xyplot(resamps,
models = c("CART", "MARS"),
metric = "RMSE")
splom(resamps, metric = "RMSE")
splom(resamps, variables = "metrics")
parallel(resamps, metric = "RMSE")
```

*Documentation reproduced from package caret, version 5.07-001, License: GPL-2*