# xyplot.resamples

##### Lattice Functions for Visualizing Resampling Results

Lattice and ggplot functions for visualizing resampling results across models

- Keywords
- hplot

##### Usage

```
# S3 method for resamples
xyplot(x, data = NULL, what = "scatter",
models = NULL, metric = x$metric[1], units = "min", ...)
```# S3 method for resamples
parallelplot(x, data = NULL, models = x$models,
metric = x$metric[1], ...)

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

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

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

# S3 method for resamples
dotplot(x, data = NULL, models = x$models,
metric = x$metric, conf.level = 0.95, ...)

# S3 method for resamples
ggplot(data = NULL, mapping = NULL,
environment = NULL, models = data$models, metric = data$metric[1],
conf.level = 0.95, ...)

##### Arguments

- x
an object generated by

`resamples`

- data
Only used for the

`ggplot`

method; an object generated by`resamples`

- what
for

`xyplot`

, the type of plot. Valid options are: "scatter" (for a plot of the resampled results between two models), "BlandAltman" (a Bland-Altman, aka MA plot between two models), "tTime" (for the total time to run`train`

versus the metric), "mTime" (for the time to build the final model) or "pTime" (the time to predict samples - see the`timingSamps`

options in`trainControl`

,`rfeControl`

, or`sbfControl`

)- models
a character string for which models to plot. Note:

`xyplot`

requires one or 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"`

.- units
either "sec", "min" or "hour"; which

`what`

is either "tTime", "mTime" or "pTime", how should the timings be scaled?- …
further arguments to pass to either

`histogram`

,`densityplot`

,`xyplot`

,`dotplot`

or`splom`

- 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`

)- mapping, environment
Not used.

##### Details

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

`dotplot`

and `ggplot`

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

```
# NOT RUN {
# }
# NOT RUN {
#load(url("http://topepo.github.io/caret/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")
parallelplot(resamps, metric = "RMSE")
# }
# NOT RUN {
# }
```

*Documentation reproduced from package caret, version 6.0-80, License: GPL (>= 2)*