# xyplot.resamples

From caret v4.45
by Max Kuhn

##### 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':
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, 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 and does not condition. - ...
- further arguments to pass to either
`histogram`

,`densityplot`

,`xyplot`

##### Details

`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.

`densityplot`

and `bwplot`

display univariate visualizations of the resampling distributions while `splom`

shows the pair-wise relationships.

##### Value

- a lattice object

##### 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))
bwplot(resamps,
metric = "RMSE")
densityplot(resamps,
auto.key = list(columns = 3),
pch = "|")
xyplot(resamps,
models = c("CART", "MARS"),
metric = "RMSE")
splom(resamps, metric = "RMSE")
```

*Documentation reproduced from package caret, version 4.45, License: GPL-2*

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