optk_gcms: Optimize the number of clusters for a dataset

Description

This function performs clustering analysis on a dataset and determines the optimal number of clusters based on a specified method.

Usage

optk_gcms(
  s,
  var_names = c("bio_1", "bio_12"),
  study_area = NULL,
  cluster = "kmeans",
  method = "wss",
  n = NULL,
  nstart = 10,
  K.max = 10,
  B = 100
)

Value

A ggplot object representing the optimal number of clusters.

Arguments

s: A list of stacks of General Circulation Models.
var_names: Character. A vector with the names of the variables to compare OR 'all'.
study_area: Extent object, or any object from which an Extent object can be extracted. An object that defines the study area for cropping and masking the rasters.
cluster: A character string specifying the method to build the clusters. Options are 'kmeans' (default) or 'hclust'.
method: A character string specifying the method to use for determining the optimal number of clusters. Options are 'wss' for within-cluster sum of squares, 'silhouette' for average silhouette width, and 'gap_stat' for the gap statistic method. Default is 'wss'.
n: An integer specifying the number of randomly selected samples to use in the clustering analysis. If NULL (default), all data is used.
nstart: Numeric. The number of random sets to be chosen. Default is 10. Argument is passed to `stats::kmeans()`.
K.max: Numeric. The maximum number of clusters to consider. Default is 10. Argument is passed to `factoextra::fviz_nbclust()`.
B: Integer. The number of Monte Carlo (“bootstrap”) samples. Default is 100. Argument is passed to `cluster::clusGap()`.

Author

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

Examples

Run this code

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")
optk_gcms(s, var_names, study_area)