xyplot.resamples  R Documentation 
Lattice and ggplot functions for visualizing resampling results across models
## 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, ... )
x 
an object generated by 
data 
Only used for the 
what 
for 
models 
a character string for which models to plot. Note:

metric 
a character string for which metrics to use as conditioning
variables in the plot. 
units 
either "sec", "min" or "hour"; which 
... 
further arguments to pass to either

variables 
either "models" or "metrics"; which variable should be treated as the scatter plot variables? 
panelRange 
a common range for the panels. If 
conf.level 
the confidence level for intervals about the mean
(obtained using 
mapping, environment 
Not used. 
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 twosided
confidence limits) for each model and metric.
densityplot
and bwplot
display univariate visualizations of
the resampling distributions while splom
shows the pairwise
relationships.
a lattice object
Max Kuhn
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
resamples
, dotplot
,
bwplot
,
densityplot
,
xyplot
, splom
## 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)
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