caret (version 6.0-24)

resamples: Collation and Visualization of Resampling Results

Description

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

Usage

resamples(x, ...)
## S3 method for class 'default':
resamples(x, modelNames = names(x), ...)
## S3 method for class 'resamples':
summary(object, ...)
## S3 method for class 'resamples':
sort(x, decreasing = FALSE, metric = x$metric[1], FUN = mean, ...)
modelCor(x, metric = x$metric[1], ...)

Arguments

x

a list of two or more objects of class train, sbf or rfe with a common set of resampling indices in the control

modelNames

an optional set of names to give to the resampling results

object

an object generated by resamples

metric

a character string for the performance measure used to sort or computing the between-model correlations

decreasing

logical. Should the sort be increasing or decreasing?

FUN

a function whose first argument is a vector and returns a scalar, to be applied to each model's performance measure.

...

only used for sort and modelCor and captures arguments to pass to sort or FUN.

Value

• For resamples: an object with class "resamples" with elements
• call
  the call
• values
  a data frame of results where rows correspond to resampled data sets and columns indicate the model and metric
• models
  a character string of model labels
• metrics
  a character string of performance metrics
• methods
  a character string of the train method argument values for each model
• For sort.resamples a character string in the sorted order is generated. modelCor returns a correlation matrix.

Details

The ideas and methods here are based on Hothorn et al (2005) and Eugster et al (2008).

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.

References

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

See Also

train, trainControl, diff.resamples, xyplot.resamples, densityplot.resamples, bwplot.resamples, splom.resamples

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))

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

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

## resamps
## summary(resamps)