xyplot.resamples
From caret v6.070
by Max Kuhn
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 whenvariables = "models"
and at least two whenvariables = "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 BlandAltman, aka MA plot between two models), "tTime" (for the total time to runtrain
versus the metric), "mTime" (for the time to build the final model) or "pTime" (the time to predict samples  see thetimingSamps
options intrainControl
,rfeControl
, orsbfControl
)  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
orsplom
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 twosided confidence limits) for each model and metric.
densityplot
and bwplot
display univariate visualizations of the resampling distributions while splom
shows the pairwise 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. 675699
Eugster et al. Exploratory and inferential analysis of benchmark experiments. LudwigsMaximiliansUniversitat Munchen, Department of Statistics, Tech. Rep (2008) vol. 30
See Also
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)
Community examples
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