resamples

From caret v4.39 by Max Kuhn

Collation and Visualization of Resampling Results

These functions provide methods for collection, analyzing and visualizing a set of resampling results from a common data set.

Keywords: models

Usage

resamples(x, ...)
## S3 method for class 'default':
resamples(x, modelNames = names(x), ...)
## S3 method for class 'resamples':
summary(object, ...)

Arguments

x: a list of two or more train objects with a common set of resampling indices in the control object.
modelNames: an optional set of names to give to the resampling results
object: an object generated by resamples
...: not currently used

Details

The results from train can have more than one performance metric per resample. Each metric in the input object is saved.

resamples checks that the resampling results match; that is, the indices in the object trainObject$control$index are the same. Also, the argument trainControl returnResamp should have a value of "final" for each model.

The summary function computes summary statistics across each model/metric combination.

Value

An object with class "resamples" with elements
callthe call
valuesa data frame of results where rows correspond to resampled data sets and columns indicate the model and metric
modelsa character string of model labels
metricsa character string of performance metrics
methodsa character string of the train method argument values for each model

Examples

data(BloodBrain)
set.seed(1)
tmp <- createDataPartition(logBBB,
                           p = .8,
                           times = 100)

rpartFit <- train(bbbDescr, logBBB,
                  "rpart", 
                  tuneLength = 16,
                  trControl = trainControl(
                    method = "LGOCV", index = tmp))


ctreeFit <- train(bbbDescr, logBBB,
                  "ctree", 
                  trControl = trainControl(
                    method = "LGOCV", index = tmp))


earthFit <- train(bbbDescr, logBBB,
                  "earth",
                  tuneLength = 20,
                  trControl = trainControl(
                    method = "LGOCV", index = tmp))

resamps <- resamples(list(CART = rpartFit,
                          CondInfTree = ctreeFit,
                          MARS = earthFit))

## or load pre-calculated results using:
## load(url("http://caret.r-forge.r-project.org/Classification_and_Regression_Training_files/exampleModels.RData"))

resamps
summary(resamps)

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

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