Lattice Functions for Visualizing Resampling Results
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 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
Max Kuhn
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
## 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)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.