xyplot.resamples

From caret v4.39 by Max Kuhn

Lattice Functions for Visualizing Resampling Results

Lattice functions for visualizing resampling results across models

Keywords: hplot

Usage

## S3 method for class 'resamples':
xyplot(x, data = NULL, models = x$models[1:2], metric = x$metric[1], ...)
## 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, 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 exactly 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 and does not condition.
...: further arguments to pass to either histogram, densityplot, xyplot

Details

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.

densityplot and bwplot display univariate visualizations of the resampling distributions while splom shows the pair-wise relationships.

Value

a lattice object

Examples

#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))
bwplot(resamps,
       metric = "RMSE")

densityplot(resamps,
            auto.key = list(columns = 3),
            pch = "|")

xyplot(resamps,
       models = c("CART", "MARS"),
       metric = "RMSE")

splom(resamps, metric = "RMSE")

Documentation reproduced from package caret, version 4.39, License: GPL-2

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