Silhouette (version 0.9.6)

plotSilhouette: Plot Silhouette Analysis Results

Description

Creates a silhouette plot for visualizing the silhouette widths of clustering results, with bars colored by cluster and an optional summary of cluster statistics in legend.

Usage

plotSilhouette(
  x,
  label = FALSE,
  summary.legend = TRUE,
  grayscale = FALSE,
  linetype = c("dashed", "solid", "dotted", "dotdash", "longdash", "twodash"),
  ...
)

Value

A ggplot2 object representing the Silhouette plot.

Arguments

x

An object of class "Silhouette", typically the output of the Silhouette, softSilhouette, dbSilhouette and cerSilhouette function. Also supports objects classes eclust, hcut, pam, clara, fanny, silhouette, or silhouette from cluster, factoextra, drclust packages. For these classes, explicitly call plotSilhouette() to generate the plot.

label

Logical; if TRUE, the x-axis is labeled with observation row indices from the input data and titled "Row Index". Defaults to FALSE.

summary.legend

Logical; if TRUE, prints a summary of average silhouette widths and sizes for each cluster in legend ("Cluster (Size): Width"). If FALSE, the legend shows only cluster numbers. Defaults to TRUE.

grayscale

Logical; if TRUE, the plot uses a grayscale color palette for clusters. If FALSE, uses the default or specified color palette. Defaults to FALSE.

linetype

Character or numeric value specifying the type of line to be used for the horizontal reference line indicating the average silhouette width. Accepts standard ggplot2 linetype values, such as:

• Character: "solid", "dashed", "dotted", "dotdash", "longdash", "twodash".

• Numeric: integers from 0 to 6 corresponding to ggplot2 line patterns.

Defaults to "dashed".

...

Additional arguments passed to ggpar for customizing the plot (e.g., palette, legend, xlab, ylab, subtitle, title).

Details

The Silhouette plot displays the silhouette width (sil_width) for each observation, grouped by cluster, with bars sorted by cluster and descending silhouette width. The summary.legend option adds cluster sizes and average silhouette widths to the legend.

This function replica of S3 method for objects of class "Silhouette", typically produced by the Silhouette, softSilhouette, , dbSilhouette or , cerSilhouette functions in this package. It also supports objects of the following classes, with silhouette information extracted from their respective component:

• "eclust": Produced by eclust from the factoextra package.

• "hcut": Produced by hcut from the factoextra package.

• "pam": Produced by pam from the cluster package.

• "clara": Produced by clara from the cluster package.

• "fanny": Produced by fanny from the cluster package.

• "silhouette": Produced by silhouette from the cluster package or silhouette from the drclust package.

For these classes ("eclust", "hcut", "pam", "clara", "fanny", "silhouette"), users should explicitly call plotSilhouette() (e.g., plotSilhouette(pam_result)) to ensure the correct method is used, as the generic plot() may not dispatch to this function for these objects.

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. tools:::Rd_expr_doi("10.1016/0377-0427(87)90125-7")

See Also

Silhouette, softSilhouette, dbSilhouette, cerSilhouette, getSilhouette, is.Silhouette

Examples

# \donttest{
data(iris)

# Crisp Silhouette with k-means
out <- kmeans(iris[, -5], 3)
if (requireNamespace("proxy", quietly = TRUE)) {
  library(proxy)
  dist <- dist(iris[, -5], out$centers)
  plot(Silhouette(dist))
}

#' # Fuzzy Silhouette with ppclust::fcm
if (requireNamespace("ppclust", quietly = TRUE)) {
  library(ppclust)
  out_fuzzy <- Silhouette(
    prox_matrix = "d",
    proximity_type = "dissimilarity",
    prob_matrix = "u",
    clust_fun = ppclust::fcm,
    x = iris[, 1:4],
    centers = 3,
    sort = TRUE
  )
  plot(out_fuzzy, summary.legend = FALSE, grayscale = TRUE)
} else {
  message("Install 'ppclust': install.packages('ppclust')")
}

# Silhouette plot for pam clustering
if (requireNamespace("cluster", quietly = TRUE)) {
  library(cluster)
  pam_result <- pam(iris[, 1:4], k = 3)
  plotSilhouette(pam_result)
}

# Silhouette plot for clara clustering
if (requireNamespace("cluster", quietly = TRUE)) {
  clara_result <- clara(iris[, 1:4], k = 3)
  plotSilhouette(clara_result)
}

# Silhouette plot for fanny clustering
if (requireNamespace("cluster", quietly = TRUE)) {
  fanny_result <- fanny(iris[, 1:4], k = 3)
  plotSilhouette(fanny_result)
}

# Example using base silhouette() object
if (requireNamespace("cluster", quietly = TRUE)) {
  sil <- silhouette(pam_result)
  plotSilhouette(sil)
}

# Silhouette plot for eclust clustering
if (requireNamespace("factoextra", quietly = TRUE)) {
  library(factoextra)
  eclust_result <- eclust(iris[, 1:4], "kmeans", k = 3, graph = FALSE)
  plotSilhouette(eclust_result)
}

# Silhouette plot for hcut clustering
if (requireNamespace("factoextra", quietly = TRUE)) {
  hcut_result <- hcut(iris[, 1:4], k = 3)
  plotSilhouette(hcut_result)
}

# Silhouette plot for hcut clustering
if (requireNamespace("drclust", quietly = TRUE)) {
  library(drclust)
  iris_mat <- as.matrix(iris[,-5])
  drclust_out <- dpcakm(iris_mat, 20, 3)
  d <- silhouette(iris_mat, drclust_out)
  plotSilhouette(d$cl.silhouette)
}
# }