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Hierarchical Clustering of GCMs

Source: R/hclust_gcms.R
hclust_gcms.Rd

This function performs hierarchical clustering on a random subset of raster values and produces a dendrogram visualization of the clusters.

Usage

hclust_gcms(
  s,
  var_names = c("bio_1", "bio_12"),
  study_area = NULL,
  scale = TRUE,
  k = 3,
  n = NULL
)

Arguments

s

A list of stacks of General Circulation Models (GCMs).

var_names

Character. A vector of names of the variables to include, or 'all' to include all variables.

study_area

An Extent object, or any object from which an Extent object can be extracted. Defines the study area for cropping and masking the rasters.

scale

Logical. Should the data be centered and scaled? Default is TRUE.

k

Integer. The number of clusters to identify.

n

Integer. The number of values to use in the clustering. If NULL (default), all data is used.

Value

A dendrogram visualizing the clusters and the suggested GCMs.

See also

transform_gcms flatten_gcms

Author

Luíz Fernando Esser (luizesser@gmail.com) https://luizfesser.wordpress.com

Examples

var_names <- c("bio_1", "bio_12")
s <- import_gcms(system.file("extdata", package = "chooseGCM"), var_names = var_names)[1:5]
study_area <- terra::ext(c(-80, -70, -50, -40)) |>
  terra::vect(crs="+proj=longlat +datum=WGS84 +no_defs")
hclust_gcms(s, var_names, study_area, k = 4, n = 500)
#> CRS from s and study_area are not identical. Reprojecting study area.
#> $suggested_gcms
#> [1] "ae" "cc" "cr" "ev"
#> 
#> $dend_plot

#>