compare_caps | R Documentation |
This function searches for clusters in the input data set using different
strategies and generates an object of class mcaps
which stores multiple
objects of class caps
. This is a helper function to facilitate comparison
of clustering methods and choice of an optimal one.
compare_caps(
x,
y,
n_clusters = 1:5,
metric = c("l2", "pearson"),
clustering_method = c("kmeans", "hclust-complete", "hclust-average", "hclust-single",
"dbscan"),
warping_class = c("affine", "dilation", "none", "shift", "srsf"),
centroid_type = c("mean", "medoid", "median", "lowess", "poly"),
cluster_on_phase = FALSE
)
x |
A numeric vector of length |
y |
Either a numeric matrix of shape |
n_clusters |
An integer vector specifying a set of clustering partitions
to create. Defaults to |
metric |
A string specifying the metric used to compare curves. Choices
are |
clustering_method |
A character vector specifying one or more clustering
methods to be fit. Choices are |
warping_class |
A character vector specifying one or more classes of
warping functions to use for curve alignment. Choices are |
centroid_type |
A character vector specifying one or more ways to
compute centroids. Choices are |
cluster_on_phase |
A boolean specifying whether clustering should be
based on phase variation or amplitude variation. Defaults to |
An object of class mcaps
which is a tibble::tibble
storing the
objects of class caps
in correspondence of each combination of possible
choices from the input arguments.
#----------------------------------
# Compare k-means results with k = 1, 2, 3, 4, 5 using mean centroid and
# various warping classes.
## Not run:
sim30_mcaps <- compare_caps(
x = simulated30_sub$x,
y = simulated30_sub$y,
warping_class = c("none", "shift", "dilation", "affine"),
clustering_method = "kmeans",
centroid_type = "mean"
)
## End(Not run)
#----------------------------------
# Then visualize the results
# Either with ggplot2 via ggplot2::autoplot(sim30_mcaps)
# or using graphics::plot()
# You can visualize the WSS values:
plot(sim30_mcaps, validation_criterion = "wss", what = "mean")
plot(sim30_mcaps, validation_criterion = "wss", what = "distribution")
# Or the average silhouette values:
plot(sim30_mcaps, validation_criterion = "silhouette", what = "mean")
plot(sim30_mcaps, validation_criterion = "silhouette", what = "distribution")
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