Compute gap statistic for determining optimal number of clusters
Usage
tidy_gap_stat(data, FUN_cluster = NULL, max_k = 10, B = 50, nstart = 25)
Arguments
- data
A data frame or tibble
- FUN_cluster
Clustering function (default: uses kmeans internally)
- max_k
Maximum number of clusters (default: 10)
- B
Number of bootstrap samples (default: 50)
- nstart
If using kmeans, number of random starts (default: 25)
Value
A list of class "tidy_gap" containing:
gap_data: tibble with gap statistics for each k
k_firstSEmax: optimal k via firstSEmax method (most conservative)
k_globalmax: optimal k via globalmax method
k_firstmax: optimal k via firstmax method
recommended_k: recommended k (uses firstSEmax)
model: the clusGap result
Examples
# \donttest{
gap <- tidy_gap_stat(iris[, 1:4], max_k = 6, B = 10)
gap$recommended_k
#> [1] 6
# }
