xyplot.resamples {caret} | R Documentation

## Lattice Functions for Visualizing Resampling Results

### Description

Lattice 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, ...)

### Arguments

`x` | an object generated by `resamples`
`data` | Not used

`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`)

### Details

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

`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

### Author(s)

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

### See Also

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

### Examples

    ## 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")

    ## End(Not run)

[Package caret version 6.0-76 Index]