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Compute Within-Cluster Sum of Squares (WCSS)

compute_wcss.Rd

Compute the Within-Cluster Sum of Squares (WCSS) for a given variable and a range of cluster values (k) using k-means clustering.

Usage

compute_wcss(data, variable, k_values, seed = 42)

Arguments

data

A data frame containing the data for clustering.

variable

The variable in the data frame to be used for clustering. Use the name of the column in "".

k_values

A vector of integers specifying the number of clusters (k) to be considered.

Value

A numeric vector containing the WCSS values for each value of k.

Details

This function computes the Within-Cluster Sum of Squares (WCSS) for each value of k provided in the k_values parameter using k-means clustering. It returns a numeric vector where each element represents the WCSS value for the corresponding value of k.

See also

For automatically finding optimal values of k, see the choose_optimal_k function.

Examples


# Create a dataframe of maple ash and pine species with range size, average evolutionary distinctiveness, and functional distinctiveness values
species_names <- c("Abies_alba", "Abies_grandis", "Abies_nordmanniana", "Acer_campestre", "Acer_monspessulanum", "Acer_negundo", "Acer_opalus", "Acer_platanoides", "Acer_pseudoplatanus", "Acer_saccharinum", "Fraxinus_angustifolia", "Fraxinus_excelsior", "Fraxinus_ornus", "Fraxinus_pennsylvanica", "Pinus_banksiana", "Pinus_cembra", "Pinus_nigra", "Pinus_pinaster", "Pinus_pinea", "Pinus_ponderosa", "Pinus_strobus", "Pinus_sylvestris", "Pinus_uncinata")

FD_values <- runif(min = -2, max = 2, n=23)

range_values <- runif(min = -2, max = 2, n=23)

mean_evol_dist_values <- runif(min = -2, max = 2, n=23)

forest_data <- data.frame(species_name = species_names, fun_dist = FD_values, range_size = range_values, mean_evol_dist = mean_evol_dist_values)

# Calculate the 8 wcss values for range size 
wcss_range <- compute_wcss(data = forest_data, variable = "range_size", k_values= c(1, 2, 3, 4, 5, 6, 7, 8))

print(wcss_range)
#> [1] 28.3447278  6.8320276  4.1195896  1.2525934  0.8129789  0.6131270  0.5575771
#> [8]  0.4475473

# Calculate the 8 wcss values for evolutionary distinctiveness
wcss_evol_dist <- compute_wcss(data = forest_data, variable = "mean_evol_dist", k_values= c(1, 2, 3, 4, 5, 6, 7, 8))

print(wcss_evol_dist)
#> [1] 30.1670067  8.6043512  2.5742941  1.8581015  0.6582987  0.3645319  0.3576668
#> [8]  0.2289784

# Calculate the 8 wcss values for functional distinctiveness
wcss_fun_dist <- compute_wcss(data = forest_data, variable = "fun_dist", k_values= c(1, 2, 3, 4, 5, 6, 7, 8))

print(wcss_fun_dist)
#> [1] 28.6863690  5.2267493  1.6557100  1.3944138  0.9855532  0.8987467  0.8365107
#> [8]  0.2074878