plot_silhouette: Plotting Functions

In ilangurudev/approxmapR: ApproxMAP (APPROXimate Multiple Alignment Pattern)

Plotting Functions

Description

A function which allows one to visualize the silhouette values for the individual as well as for the cluster.

Usage

  plot_silhouette <- (ktable,
                      save_graph = TRUE, graph_file_name_individual = NULL, graph_file_name_cluster = NULL,
                      save_table = TRUE, table_file_name = NULL,
                      size_width = 855, size_height = 317,
                      output_directory = "~")

Arguments

ktable	A silhouette_object from the ktable which is produced from find_optimal_k function. This object is found in the silhouette_object column.
save_graph	Default value is TRUE which will save the plot as a PNG file.
graph_file_name_individual	Allows user to specify the file name for the graph that is being saved, if nothing is specified then a default file name is used.
graph_file_name_cluster	Allows user to specify the file name for the graph that is being saved, if nothing is specified then a default file name is used.
save_table	Default value is TRUE which will save the table as a CSV file.
table_file_name	Allows user to specify the file name for the table that is being saved, if nothing is specified then a default file name is used.
size_width	The width of the graph.
size_height	The height of the graph.
output_directory	The path to where the exports should be placed.

Value

Returns two plots, one for the silhouette values at the individual level and another for the silhouette values at the cluster level. Also has the ability to save the images as well as the silhouette table to a file.

Examples

  data("demo1")
  demo1 <- data.frame(do.call("rbind", strsplit(as.character(demo1$id.date.item), ",")))
  names(demo1) <- c("id", "period", "event")


  agg <- demo1 %>% aggregate_sequences(format = "%m/%d/%Y",
                                     unit = "month",
                                     n_units = 1,
                                     include_date = TRUE,
                                     summary_stats = TRUE)

  ktable <- agg %>% find_optimal_k(clustering = "k-medoids", min_k = 2, max_k = 9,
                                   use_cache = TRUE, save_table = TRUE)

  plot_silhouette(ktable$silhouette_object[[8]])

ilangurudev/approxmapR documentation built on March 22, 2022, 1:15 p.m.