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Computes silhouette widths index for several possible numbers of clusters(partitions) k, which determines how well an object falls within their cluster compared to other clusters. The best number of clusters k is the one with the highest silhouette width.

## Usage

get_sil_widths(dist_mat, max.k = 10)

# S3 method for sil_width_df
plot(x, ...)

## Arguments

dist_mat

A Gower distance matrix, the output of a call to get_gower_dist.

max.k

The maximum number of clusters(partitions) to search across.

x

A sil_width_df object; the output of a call to get_sil_widths().

...

Further arguments passed to ggplot2::geom_line to control the appearance of the plot.

## Details

get_sil_widths calls cluster::pam on the supplied Gower distance matrix with each number of clusters (partitions) up to max.k and stores the average silhouette widths across the clustered characters. When plot = TRUE, a plot of the sillhouette widths against the number of clusters is produced, though this can also be produced seperately on the resulting data frame using plot.sil_width_df(). The number of clusters with the greatest silhouette width should be selected for use in the final clustering specification.

## Value

For get_sil_widths(), it produces a data frame, inheriting from class "sil_width_df", with two columns: k is the number of clusters, and sil_width is the silhouette widths for each number of clusters. If plot = TRUE, the output is returned invisibly.

For plot() on a get_sil_widths() object, it produces a ggplot object that can be manipulated using ggplot2 syntax (e.g., to change the theme or labels).

## See also

vignette("char-part") for the use of this function as part of an analysis pipeline.

get_gower_dist, cluster::pam

## Examples

# See vignette("char-part") for how to use this
# function as part of an analysis pipeline

data("characters")

#Reading example file as categorical data
Dmatrix <- get_gower_dist(characters)

#Get silhouette widths for k=7
sw <- get_sil_widths(Dmatrix, max.k = 7)

sw
#>   k sil_width
#> 1 2 0.2340255
#> 2 3 0.2626128
#> 3 4 0.1863738
#> 4 5 0.1243595
#> 5 6 0.1205568
#> 6 7 0.1102649

plot(sw, color = "red", size =2)