prcomp.resamples | R Documentation |

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

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

and `prcomp`

.

## 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)), ...)

`x` |
For |

`metric` |
a performance metric that was estimated for every resample |

`...` |
For |

`what` |
the type of plot: |

`dims` |
The number of dimensions to plot when |

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.

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 |

`call ` |
the call |

For `plot.prcomp.resamples`

, a Lattice object (see Details above)

Max Kuhn

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

`resamples`

, `barchart`

,
`xyplot`

, `splom`

,
`hclust`

## 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)

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