```
## 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], ...)

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 modelsconf.level

the confidence level for intervals about the mean (obtained using

`t.test`

)...

further arguments to pass to either

`histogram`

, `densityplot`

, `xyplot`

- a lattice object

`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.

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

`resamples`

, `dotplot`

, `bwplot`

, `densityplot`

, `xyplot`

, `splom`

#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")