Skip to contents

K-means clustering for morphometric data

Source: R/stat_kmeans.R
stat_kmeans.Rd

Perform k-means clustering on coefficient data.

Usage

stat_kmeans(data, formula = NULL, k = 3, nstart = 25, ...)

Arguments

data

A tibble with coefficient columns

formula

A formula specifying predictors. Can be:

  • Missing: auto-detects single coe column

  • Bare column name: coe

  • Formula: ~ coe, ~ coe + size, ~ coe1 + coe2

  • Use coe in formula to auto-detect coefficient columns

k

Integer. Number of clusters. Default is 3.

nstart

Integer. Number of random starts for k-means. Default is 25.

...

Additional arguments passed to stats::kmeans()

Value

An object of class c("stat_kmeans", "momstats") containing:

  • data: Original tibble (unchanged)

  • model: The stats::kmeans() object

  • method: "kmeans"

  • call: The function call

  • formula: Formula used (if any)

  • predictor_cols: All predictor column names

  • k: Number of clusters

  • centers: Cluster centers in predictor space (matrix)

  • cluster_sizes: Size of each cluster

  • withinss: Within-cluster sum of squares

  • tot_withinss: Total within-cluster sum of squares

  • betweenss: Between-cluster sum of squares

  • variance_explained: Proportion of variance explained by clustering

Details

stat_kmeans() provides a unified interface for k-means clustering on morphometric coefficient data.

Formula syntax

The formula specifies which predictors to use:

  • ~ coe: Use auto-detected coefficient column(s)

  • ~ coe1 + coe2: Use specific coefficient columns

  • ~ coe + size: Coefficient column plus a covariate

  • Bare name or missing: auto-detect single coe column

Getting results

Use collect() to add cluster assignments to your data:

km <- boteft %>% stat_kmeans(k = 3)
boteft_clustered <- collect(km)  # Adds 'cluster' column

Use transduce() to get shapes at cluster centers:

center_shapes <- transduce(km, tibble(cluster = 1:3))

See also

stats::kmeans(), collect.stat_kmeans(), plot.stat_kmeans(), transduce.stat_kmeans()

Examples

if (FALSE) { # \dontrun{
# Basic k-means with 3 clusters
km1 <- boteft %>% stat_kmeans(k = 3)

# More clusters
km2 <- boteft %>% stat_kmeans(k = 5)

# With covariate
km3 <- boteft %>% stat_kmeans(~ coe + length, k = 4)

# Add cluster assignments
boteft_clustered <- collect(km1)

# Get cluster center shapes
centers <- transduce(km1, tibble(cluster = 1:3))

# Plot results
plot(km1)  # Cluster visualization
plot(km1, color = type)  # Color by original grouping
} # }