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 "Calinski-Harabasz".
- detailed
If False (default) returns an integer with best overall k. If TRUE, returns a list with full details.
- ...
Extra arguments.
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., 2024 (in peer-review) and to
vignette("explore-classifications").
Alternatively, this function can also provide the best k, as an integer, based on another index (Davies-Bouldin and Calinski-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 Calinski-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
#>
#> $CalinskiHarabasz
#> # 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
# }