# prcomp.resamples

##### Principal Components Analysis of Resampling Results

Performs a principal components analysis on an object of class
`resamples`

and returns the results as an object with classes
`prcomp.resamples`

and `prcomp`

.

- Keywords
- hplot

##### Usage

```
# S3 method for resamples
prcomp(x, metric = x$metrics[1], ...)
```# S3 method for prcomp.resamples
plot(x, what = "scree", dims = max(2, ncol(x$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:

the value for the `metric`

argument

the call

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

##### References

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

##### See Also

##### Examples

```
# NOT RUN {
# }
# 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)
# }
```

*Documentation reproduced from package caret, version 6.0-86, License: GPL (>= 2)*