manhattan: Manhattan

In ecodive: Parallel and Memory-Efficient Ecological Diversity Metrics

Manhattan

Description

Manhattan beta diversity metric.

Usage

manhattan(counts, weighted = TRUE, pairs = NULL, cpus = n_cpus())

Arguments

counts	An OTU abundance matrix where each column is a sample, and each row is an OTU. Any object coercible with as.matrix() can be given here, as well as phyloseq, rbiom, SummarizedExperiment, and TreeSummarizedExperiment objects.
weighted	If TRUE, the algorithm takes relative abundances into account. If FALSE, only presence/absence is considered.
pairs	Which combinations of samples should distances be calculated for? The default value (NULL) calculates all-vs-all. Provide a numeric or logical vector specifying positions in the distance matrix to calculate. See examples.
cpus	How many parallel processing threads should be used. The default, n_cpus(), will use all logical CPU cores.

Value

A dist object.

Calculation

In the formulas below, x and y are two columns (samples) from counts. n is the number of rows (OTUs) in counts.

D = \displaystyle \sum_{i = 1}^{n} |x_i - y_i|

  x <- c(4, 0, 3, 2, 6)  
  y <- c(0, 8, 0, 0, 5)  
  sum(abs(x-y))
  #>  18

References

Paul EB 2006. Manhattan distance. Dictionary of Algorithms and Data Structures. https://xlinux.nist.gov/dads/HTML/manhattanDistance.html

See Also

Other beta_diversity: bray_curtis(), canberra(), euclidean(), generalized_unifrac(), gower(), jaccard(), kulczynski(), unweighted_unifrac(), variance_adjusted_unifrac(), weighted_normalized_unifrac(), weighted_unifrac()

Examples

    # Example counts matrix
    ex_counts
    
    # Manhattan weighted distance matrix
    manhattan(ex_counts)
    
    # Manhattan unweighted distance matrix
    manhattan(ex_counts, weighted = FALSE)
    
    # Only calculate distances for A vs all.
    manhattan(ex_counts, pairs = 1:3)
    

ecodive documentation built on Aug. 23, 2025, 1:13 a.m.