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 classresamples
and forplot.prcomp.resamples
, an object of classplot.prcomp.resamples
- metric
a performance metric that was estimated for every resample
- …
For
prcomp.resamples
, options to pass toprcomp
, forplot.prcomp.resamples
, options to pass to Lattice objects (see Details below) and, forcluster.resamples
, options to pass tohclust
.- 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"
orwhat = "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)
# }