xyplot.resamples

0th

Percentile

Lattice Functions for Visualizing Resampling Results

Lattice functions for visualizing resampling results across models

Keywords
hplot
Usage
"xyplot"(x, data = NULL, what = "scatter", models = NULL,  metric = x$metric[1], units = "min", ...) "dotplot"(x, data = NULL, models = x$models,  metric = x$metric, conf.level = 0.95, ...) "densityplot"(x, data = NULL, models = x$models, metric = x$metric, ...) "bwplot"(x, data = NULL, models = x$models, metric = x$metric, ...) "splom"(x, data = NULL, variables = "models", models = x$models, metric = NULL, panelRange = NULL, ...)
"parallelplot"(x, data = NULL, models = x$models, metric = x$metric[1], ...)
Arguments
x
an object generated by resamples
data
Not used
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".
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
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)
units
either "sec", "min" or "hour"; which what is either "tTime", "mTime" or "pTime", how should the timings be scaled?
conf.level
the confidence level for intervals about the mean (obtained using t.test)
...
further arguments to pass to either histogram, densityplot, xyplot, dotplot or splom
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.

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

resamples, dotplot, bwplot, densityplot, xyplot, splom

Aliases
• xyplot.resamples
• densityplot.resamples
• bwplot.resamples
• splom.resamples
• parallelplot.resamples
• dotplot.resamples
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)

Documentation reproduced from package caret, version 6.0-70, License: GPL (>= 2)

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