# 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, models = x$models[1:2], metric = x$metric[1], ...)
```## 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], ...)

##### Arguments

- x
- an object generated by
`resamples`

- data
- Not used
- models
- a character string for which models to plot. Note:
`xyplot`

requires exactly two models whereas the other methods can plot more than two. - metric
- a character string for which metrics to use as conditioning variables in the plot.
`splom`

requires exactly one metric when`variables = "models"`

and at least two when`variables = "metrics"`

. - variables
- either "models" or "metrics"; which variable should be treated as the scatter plot variables?
- panelRange
- a common range for the panels. If
`NULL`

, the panel ranges are derived from the values across all the models - conf.level
- the confidence level for intervals about the mean (obtained using
`t.test`

) - ...
- further arguments to pass to either
`histogram`

,`densityplot`

,`xyplot`

##### Details

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

`xyplot`

only uses two models in the plot. The plot uses difference of the models on the y-axis and the average of the models on the x-axis.

`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 4.65, License: GPL-2*