Evaluate k from all samples in a dataset

This function extends evaluate_sample_k() for any number of samples in a dataset.

Usage

evaluate_k(
  data,
  range = 2:10,
  samples_col = "Sample",
  abundance_col = "Abundance",
  with_plot = 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 2 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".

with_plot

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

...

Extra arguments.

Value

A nested data.frame (or a plot) with three indices for each k and for each sample.

Details

The plot option (with_plot = TRUE) provides centrality metrics for all samples used.

For more details on indices calculation, please see the documentation for evaluate_sample_k(), check_DB(), check_CH() and check_avgSil().

See also

evaluate_sample_k(), check_DB(), check_CH(), check_avgSil(), suggest_k()

Examples

# \donttest{
library(dplyr)

#' evaluate_k(nice_tidy)


# To make simple plot
evaluate_k(nice_tidy, range = 4:11, with_plot =TRUE)
#> No summary function supplied, defaulting to `mean_se()`
#> No summary function supplied, defaulting to `mean_se()`
#> No summary function supplied, defaulting to `mean_se()`

# }