caret (version 5.05.004)

xyplot.resamples: 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$metric[1], units = "min", ...)
## S3 method for class 'resamples':
dotplot(x, data = NULL, models = x$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], ...)

Value

• a lattice object

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.

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

#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")

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