## S3 method for class 'resamples':
xyplot(x, data = NULL, models = x$models[1:2], metric = x$metric[1], ...)## 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], ...)
resamples
xyplot
requires exactly two models whereas the other methods can plot more than two.splom
requires exactly one metric when variables = "models"
and at least two when variables = "metrics"
.NULL
, the panel ranges are derived from the values across all the modelst.test
)histogram
, densityplot
, xyplot
xyplot
only uses two models in the plot. The plot uses difference of the models on the y-axis and the average of the models on the x-axis.
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.
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
#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")