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Perform K-Means Clustering on GCMs

Source: R/kmeans_gcms.R
kmeans_gcms.Rd

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.

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)
study_area <- terra::ext(c(-80, -30, -50, 10)) |> terra::vect(crs="epsg:4326")
kmeans_gcms(s, var_names, study_area, k = 3)
#> $suggested_gcms
#> [1] "ev" "ch" "in"
#> 
#> $kmeans_plot

#>