kmeans_gcms: Perform K-Means Clustering on GCMs

In chooseGCM: Selecting General Circulation Models for Species Distribution Modeling

Perform K-Means Clustering on GCMs

Description

This function performs k-means clustering on a distance matrix and produces a scatter plot of the resulting clusters.

Usage

kmeans_gcms(
  s,
  var_names = c("bio_1", "bio_12"),
  study_area = NULL,
  scale = TRUE,
  k = 3,
  method = 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 create.
method	Character. The method for distance matrix computation. Default is "euclidean." Possible values are: "euclidean," "maximum," "manhattan," "canberra," "binary," or "minkowski." If NULL, clustering will be performed on the raw variable data.

Value

A scatter plot showing the resulting 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")
kmeans_gcms(s, var_names, study_area, k = 3)

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