hclust_gcms: Hierarchical Clustering of GCMs

In chooseGCM: Selecting General Circulation Models for Species Distribution Modeling

Hierarchical Clustering of GCMs

Description

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.

Author(s)

Luíz Fernando Esser ([e-mail]) https://luizfesser.wordpress.com

See Also

transform_gcms flatten_gcms

Examples

var_names <- c("bio_1", "bio_12")
s <- import_gcms(system.file("extdata", package = "chooseGCM"), var_names = var_names)
study_area <- terra::ext(c(-80, -30, -50, 10)) |> terra::vect(crs="epsg:4326")
hclust_gcms(s, var_names, study_area, k = 4, n = 500)

chooseGCM documentation built on April 3, 2025, 5:27 p.m.