Evaluate sample k

This functions calculates three indices (Davies-Bouldin, Calinsky-Harabasz and average Silhouette score) for each k. Calculations are made for a single sample and for a default range of k that goes from 3 to 10.

Usage

evaluate_sample_k(
  data,
  sample_id,
  samples_col = "Sample",
  abundance_col = "Abundance",
  range = 3:10,
  with_plot = FALSE,
  ...
)

Arguments

data

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

sample_id

String with name of the sample to apply this function.

samples_col

String with name of column with sample names.

abundance_col

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

range

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

with_plot

If FALSE (default) returns a vector, but if TRUE will return a plot with the scores.

...

Extra arguments.

Value

A data.frame (or plot) with several indices for each number of clusters.

Details

Note: To get the indices for all samples, use evaluate_k() instead.

Data input

This function takes a data.frame with a column for samples and a column for abundance (minimum), but can take any number of other columns. It will then filter the specific sample that you want to analyze. You can also pre-filter for your specific sample, but you still need to provide the sample ID (sample_id) and the table always needs a column for Sample and another for Abundance (indicate how you name them with the arguments samples_col and abundance_col).

Output options

The default option returns a data.frame with Davies-Bouldin, Calinsky-Harabasz and average Silhouette scores for each k. This is a simple output that can then be used for other analysis. However, we also provide the option to show a plot (set with_plot = TRUE).

Three indices are calculated by this function:

• Davies-Bouldin with check_DB();

• Calinsky-Harabasz with check_DB();

• average Silhouette score check_avgSil().

See also

check_CH(), check_DB(), check_avgSil(), suggest_k(), evaluate_k()

Examples

library(dplyr)
#
evaluate_sample_k(nice_tidy, sample_id = "ERR2044662")
#>          DB        CH average_Silhouette  k
#> 1 0.3721866  1821.426          0.9521452  3
#> 2 0.5271704  2054.887          0.8820316  4
#> 3 0.4131651  4933.956          0.8561774  5
#> 4 0.4292696  5465.134          0.8398216  6
#> 5 0.3350836 17589.032          0.8479872  7
#> 6 0.3892966 17179.809          0.7843358  8
#> 7 0.3948026 18083.313          0.7740169  9
#> 8 0.3294451 30332.345          0.7701163 10

# To change range
evaluate_sample_k(nice_tidy, sample_id = "ERR2044662", range = 4:11)
#>          DB        CH average_Silhouette  k
#> 1 0.5271704  2054.887          0.8820316  4
#> 2 0.4131651  4933.956          0.8561774  5
#> 3 0.4292696  5465.134          0.8398216  6
#> 4 0.3350836 17589.032          0.8479872  7
#> 5 0.3892966 17179.809          0.7843358  8
#> 6 0.3948026 18083.313          0.7740169  9
#> 7 0.3294451 30332.345          0.7701163 10
#> 8 0.2782100 55354.240          0.7629755 11

# To make simple plot
evaluate_sample_k(nice_tidy, sample_id = "ERR2044662", range = 4:11, with_plot =TRUE)