View source: R/CLUSTERING-compare-clusterings.R
compare_clusterings_configs | R Documentation |
Create configurations for compare_clusterings()
compare_clusterings_configs(
types = c("p", "h", "f"),
k = 2L,
controls = NULL,
preprocs = pdc_configs("preproc", none = list()),
distances = pdc_configs("distance", dtw_basic = list()),
centroids = pdc_configs("centroid", default = list()),
no.expand = character(0L)
)
types |
Clustering types. It must be any combination of (possibly abbreviated): partitional, hierarchical, fuzzy, tadpole. |
k |
A numeric vector with one or more elements specifying the number of clusters to test. |
controls |
A named list of tsclust-controls. |
preprocs |
Preprocessing configurations. See details. |
distances |
Distance configurations. See details. |
centroids |
Centroid configurations. See details. |
no.expand |
A character vector indicating parameters that should not be expanded between
|
Preprocessing, distance and centroid configurations are specified with the helper function
pdc_configs()
, refer to the examples in compare_clusterings()
to see how this is used.
The controls list may be specified with the usual tsclust-controls functions. The names of the
list must correspond to "partitional", "hierarchical", "fuzzy" or "tadpole" clustering. Again,
please refer to the examples in compare_clusterings()
.
A list for each clustering type, each of which includes a data frame with the computed and merged
configurations. Each data frame has an extra attribute num.configs
specifying the number of
configurations.
# compare this with leaving no.expand empty
compare_clusterings_configs(
distances = pdc_configs("d", dtw_basic = list(window.size = 1L:2L, norm = c("L1", "L2"))),
centroids = pdc_configs("c", dba = list(window.size = 1L:2L, norm = c("L1", "L2"))),
no.expand = c("window.size", "norm")
)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.