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Tool to help decide how many clusters to use for partition around medoids algorithm.

Usage

suggest_k(
  data,
  range = 3:10,
  samples_col = "Sample",
  abundance_col = "Abundance",
  index = "Average Silhouette Score",
  detailed = FALSE,
  ...
)

Arguments

data

a data.frame with, at least, the classification, abundance and sample information for each phylogenetic unit.

range

The range of values of k to test, default is from 3 to 10.

samples_col

String with name of column with sample names.

abundance_col

string with name of column with abundance values. Default is "Abundance".

index

Index used to select best k. Can be one of: "Average Silhouette Score", "Davies-Bouldin" or "Calinsky-Harabasz".

detailed

If False (default) returns an integer with best overall k. If TRUE, returns a list with full details.

...

Extra arguments.

Value

Integer indicating best k from selected index. Optionally, can return a list with details.

Details

The best k is selected for each sample, based on the selected index. If different k's are obtained for different samples (probable) then we calculate the mean value of k and return it as an integer. Alternatively, we can return a more detailed result in the form of a list.

Note: this function is used within define_rb(), with default parameters, for the optional automatic selection of k.

Detailed option

If detailed = TRUE, then the output is a list with information to help decide for k. More specifically, the list will include:

  • A data.frame summarizing what information each index provides and how to interpret the value.

  • A brief summary indicating the number of samples in the dataset and the range of k values used.

  • A data.frame with the best k for each sample, based on each index.

Automatic k selection

If detailed = FALSE, this function will provide a single integer with the best k. The default decision is based on the maximum average Silhouette score obtained for the values of k between 3 and 10. To better understand why the average Silhouette score and this range of k's were selected, we refer to Pascoal et al., 2023 (manuscript in preparation) and to vignette("explore-classifications").

Alternatively, this function can also provide the best k, as an integer, based on another index (Davies-Bouldin and Calinsky-Harabasz) and can compare the entire of possible k's.

Examples


# \donttest{
# Get the best k with default parameters
suggest_k(nice_tidy)
#> [1] 3


# Get detailed results to decide for yourself
suggest_k(nice_tidy, detailed = TRUE, range = 2:7)
#> [[1]]
#> [1] "This list contains several details that might help you decide a k parameter."
#> 
#> [[2]]
#>                      Score                 Criteria                     Details
#> 1     Davies-Bouldin index Minimum value for best k Measures cluster separation
#> 2  Calinsky-Harabasz index Maximum value for best k Measures cluster definition
#> 3 Average Silhouette Score Maximum value for best k    Measures cluster density
#> 
#> $SamplesSummary
#> [1] "You study has 9 samples. For each one we calculated all indices obtained for each k, from 2 to 7"
#> 
#> $DaviesBouldin
#> # A tibble: 9 × 3
#>   Sample         CH     k
#>   <chr>       <dbl> <int>
#> 1 ERR2044662 17589.     7
#> 2 ERR2044663 12741.     7
#> 3 ERR2044664 87610.     7
#> 4 ERR2044665  9486.     6
#> 5 ERR2044666 19652.     7
#> 6 ERR2044667  6504.     7
#> 7 ERR2044669  6616.     7
#> 8 ERR2044668 36144.     7
#> 9 ERR2044670  9480.     7
#> 
#> $CalinskyHarabasz
#> # A tibble: 9 × 3
#>   Sample         CH     k
#>   <chr>       <dbl> <int>
#> 1 ERR2044662 17589.     7
#> 2 ERR2044663 12741.     7
#> 3 ERR2044664 87610.     7
#> 4 ERR2044665  9486.     6
#> 5 ERR2044666 19652.     7
#> 6 ERR2044667  6504.     7
#> 7 ERR2044669  6616.     7
#> 8 ERR2044668 36144.     7
#> 9 ERR2044670  9480.     7
#> 
#> $averageSilhouette
#> # A tibble: 9 × 3
#>   Sample     average_Silhouette     k
#>   <chr>                   <dbl> <int>
#> 1 ERR2044662              0.977     2
#> 2 ERR2044663              0.978     2
#> 3 ERR2044664              0.984     2
#> 4 ERR2044665              0.979     2
#> 5 ERR2044666              0.983     2
#> 6 ERR2044667              0.972     2
#> 7 ERR2044669              0.976     2
#> 8 ERR2044668              0.979     2
#> 9 ERR2044670              0.932     3
#> 

# Get best k, based on Davies-Bouldin index
suggest_k(nice_tidy, detailed = FALSE, index = "Davies-Bouldin")
#> [1] 6
# }