Suggest a number of clusters — k_diagnostic • depictr

Computes a cluster-quality diagnostic across a range of k and draws the diagnostic curve, with the suggested k highlighted. Three criteria are available: the average silhouette width (maximised), the total within-cluster sum of squares "elbow" (the point of maximum curvature), and the gap statistic (the smallest k whose gap is within one standard error of the next, using the Tibshirani et al. heuristic).

Usage

k_diagnostic(
  data,
  k_range = 2:8,
  method = c("silhouette", "wss", "gap"),
  cols = NULL,
  scale = TRUE,
  nstart = 10,
  B = 50,
  title = NULL
)

Arguments

data: A data frame or numeric matrix.
k_range: Integer vector of candidate cluster counts to evaluate.
method: Diagnostic: "silhouette", "wss" (within sum of squares elbow) or "gap".
cols: When data is a data frame, the numeric columns to use.
scale: Whether to scale variables to unit variance first.
nstart: Number of random starts for stats::kmeans().
B: Number of reference bootstrap samples for the gap statistic.
title: Plot title.

Value

A ggplot2::ggplot object showing the diagnostic value against k, with the suggested k marked and named in the subtitle. The underlying computation is attached as attributes: attr(p, "k_table") (a data frame with one row per k), attr(p, "suggested") (the chosen k) and attr(p, "method").

References

Rousseeuw, P. J. (1987). Silhouettes: A graphical aid to the interpretation and validation of cluster analysis. Journal of Computational and Applied Mathematics, 20, 53-65. doi:10.1016/0377-0427(87)90125-7

Tibshirani, R., Walther, G., & Hastie, T. (2001). Estimating the number of clusters in a data set via the gap statistic. Journal of the Royal Statistical Society: Series B, 63(2), 411-423. doi:10.1111/1467-9868.00293

Examples

p <- k_diagnostic(crop_yield, k_range = 2:6,
                  cols = c("rainfall", "fertiliser", "soil_ph", "yield"))
attr(p, "suggested")
#> [1] 2