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 default
resamples(x, modelNames = names(x), ...)
# S3 method for resamples
sort(x, decreasing = FALSE, metric = x$metric[1],
  FUN = mean, ...)
# S3 method for resamples
summary(object, metric = object$metrics, ...)
# S3 method for resamples
as.matrix(x, metric = x$metric[1], ...)
# S3 method for resamples
as.data.frame(x, row.names = NULL,
  optional = FALSE, metric = x$metric[1], ...)
modelCor(x, metric = x$metric[1], ...)
# S3 method for resamples
print(x, ...)

Arguments

a list of two or more objects of class train, sbf or rfe with a common set of resampling indices in the control object. For sort.resamples, it is an object generated by resamples.

…

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

modelNames

an optional set of names to give to the resampling results

decreasing

logical. Should the sort be increasing or decreasing?

metric

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

FUN

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

object

an object generated by resamples

row.names, optional

not currently used but included for consistency with as.data.frame

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

Examples

Run this code

# NOT RUN {

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)

# }

Run the code above in your browser using DataLab

Last chance! 50% off unlimited learning