Adds clusters' ids to a lsp object. The output can be of stars, sf, or terra class. See examples.

lsp_add_clusters(x, clust, output = "sf", window = NULL)

Arguments

x

Object of class lsp - usually the output of the lsp_signature() function

clust

Vector containing an id value for each row in x

output

The class of the output. Either stars, sf, or terra

window

Specifies areas for analysis. It can be either: NULL or an sf object. If window=NULL calculations are performed based on the metadata from x. If an sf object is provided, each feature (row) defines the extent of a local pattern. The sf object should have one attribute (otherwise, the first attribute is used as an id).

Value

Object of class stars, sf, or terra (depending on the output argument) with an additional column "clust" representing clusters' id values.

Examples


library(stars)
#> Loading required package: abind
#> Loading required package: sf
#> Linking to GEOS 3.10.2, GDAL 3.4.2, PROJ 7.2.0; sf_use_s2() is TRUE
library(sf)
landform = read_stars(system.file("raster/landforms.tif", package = "motif"))
landform_cove = lsp_signature(landform,
                               type = "cove",
                               window = 200,
                               normalization = "pdf")

landform_dist = lsp_to_dist(landform_cove,
                            dist_fun = "jensen-shannon")
#> Metric: 'jensen-shannon' using unit: 'log2'; comparing: 15 vectors.

landform_hclust = hclust(landform_dist, method = "ward.D2")
plot(landform_hclust)


clusters = cutree(landform_hclust, k = 4)

landform_grid_sf = lsp_add_clusters(landform_cove, clusters)
plot(landform_grid_sf["clust"])


landform_grid_sfq = lsp_add_quality(landform_grid_sf,
                                       landform_dist)
plot(landform_grid_sfq["quality"])


# \donttest{
# larger data example
library(stars)
library(sf)
landform = read_stars(system.file("raster/landform.tif", package = "motif"))
landform_cove = lsp_signature(landform,
                               type = "cove",
                               window = 200,
                               normalization = "pdf")

landform_dist = lsp_to_dist(landform_cove,
                            dist_fun = "jensen-shannon")
#> Metric: 'jensen-shannon' using unit: 'log2'; comparing: 288 vectors.

landform_hclust = hclust(landform_dist, method = "ward.D2")
plot(landform_hclust)


clusters = cutree(landform_hclust, k = 6)

landform_grid_sf = lsp_add_clusters(landform_cove, clusters)
plot(landform_grid_sf["clust"])


landform_grid_sfq = lsp_add_quality(landform_grid_sf,
                                       landform_dist)
plot(landform_grid_sfq["quality"])

# }