# xyplot.resamples: Lattice Functions for Visualizing Resampling Results In caret: Classification and Regression Training

 xyplot.resamples R Documentation

## Lattice Functions for Visualizing Resampling Results

### Description

Lattice and ggplot functions for visualizing resampling results across models

### 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'
parallelplot(x, data = NULL, models = x\$models, metric = x\$metric[1], ...)

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

## 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'
dotplot(
x,
data = NULL,
models = x\$models,
metric = x\$metric,
conf.level = 0.95,
...
)

## S3 method for class '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.

a lattice object

Max Kuhn

### 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

`resamples`, `dotplot`, `bwplot`, `densityplot`, `xyplot`, `splom`

### Examples

``````
## Not run:

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")

## End(Not run)

``````

caret documentation built on March 31, 2023, 9:49 p.m.