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Observed Features

Source: R/alpha_div.r
observed.Rd

The count of unique features (richness) in a sample.

Usage

observed(counts, margin = 1L, cpus = n_cpus())

Arguments

counts

A numeric matrix of count data (samples \(\times\) features). Typically contains absolute abundances (integer counts), though proportions are also accepted.

margin

The margin containing samples. 1 if samples are rows, 2 if samples are columns. Ignored when counts is a special object class (e.g. phyloseq). Default: 1

cpus

How many parallel processing threads should be used. The default, n_cpus(), will use all logical CPU cores.

Details

Observed features is defined simply as the number of features with non-zero abundance: $$n$$

Base R Equivalent:

x <- ex_counts[1,]
sum(x > 0)

Input Types

The counts parameter is designed to accept a simple numeric matrix, but seamlessly supports objects from the following biological data packages:

  • phyloseq

  • rbiom

  • SummarizedExperiment

  • TreeSummarizedExperiment

For large datasets, standard matrix operations may be slow. See vignette('performance') for details on using optimized formats (e.g. sparse matrices) and parallel processing.

See also

alpha_div(), vignette('adiv')

Other Richness metrics: ace(), chao1(), margalef(), menhinick(), squares()

Examples

    observed(ex_counts)
#> Saliva   Gums   Nose  Stool 
#>      4      5      6      5