Description Usage Arguments Details Value Author(s) References See Also Examples
Performs a principal components analysis on an object of class resamples
and returns the results as an object with classes prcomp.resamples
and prcomp
.
1 2 3 4 5 6 7 8 9 |
x |
For |
metric |
a performance metric that was estimated for every resample |
what |
the type of plot: |
dims |
The number of dimensions to plot when |
... |
For |
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
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 | ## Not run:
#load(url("http://caret.r-forge.r-project.org/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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