prcomp.resamples: Principal Components Analysis of Resampling Results

In caret: Classification and Regression Training

Principal Components Analysis of Resampling Results

Description

Performs a principal components analysis on an object of class resamples and returns the results as an object with classes prcomp.resamples and prcomp.

Usage

## S3 method for class 'resamples'
prcomp(x, metric = x$metrics[1], ...)

## S3 method for class 'prcomp.resamples'
plot(x, what = "scree", dims = max(2, ncol(x$rotation)), ...)

Arguments

x	For prcomp, an object of class resamples and for plot.prcomp.resamples, an object of class plot.prcomp.resamples
metric	a performance metric that was estimated for every resample
...	For prcomp.resamples, options to pass to prcomp, for plot.prcomp.resamples, options to pass to Lattice objects (see Details below) and, for cluster.resamples, options to pass to hclust.
what	the type of plot: "scree" produces a bar chart of standard deviations, "cumulative" produces a bar chart of the cumulative percent of variance, "loadings" produces a scatterplot matrix of the loading values and "components" produces a scatterplot matrix of the PCA components
dims	The number of dimensions to plot when what = "loadings" or what = "components"

Details

The principal components analysis treats the models as variables and the resamples are realizations of the variables. In this way, we can use PCA to "cluster" the assays and look for similarities. Most of the methods for prcomp can be used, although custom print and plot methods are used.

The plot method uses lattice graphics. When what = "scree" or what = "cumulative", barchart is used. When what = "loadings" or what = "components", either xyplot or splom are used (the latter when dims > 2). Options can be passed to these methods using ....

When what = "loadings" or what = "components", the plots are put on a common scale so that later components are less likely to be over-interpreted. See Geladi et al. (2003) for examples of why this can be important.

For clustering, hclust is used to determine clusters of models based on the resampled performance values.

Value

For prcomp.resamples, an object with classes prcomp.resamples and prcomp. This object is the same as the object produced by prcomp, but with additional elements:

metric	the value for the metric argument
call	the call

For plot.prcomp.resamples, a Lattice object (see Details above)

Author(s)

Max Kuhn

References

Geladi, P.; Manley, M.; and Lestander, T. (2003), "Scatter plotting in multivariate data analysis," J. Chemometrics, 17: 503-511

See Also

resamples, barchart, xyplot, splom, hclust

Examples

## Not run: 
#load(url("http://topepo.github.io/caret/exampleModels.RData"))

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

resampPCA

plot(resampPCA, what = "scree")

plot(resampPCA, what = "components")

plot(resampPCA, what = "components", dims = 2, auto.key = list(columns = 3))

clustered <- cluster(resamps)
plot(clustered)


## End(Not run)

caret documentation built on March 31, 2023, 9:49 p.m.