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This function calculates a distance between two positions using the Bray-Curtis method. The Bray-Curtis distance is equal to the Manhattan distance divided by the sum of both vectors. To calculate, enter a series of x /y pairs (vectors). The individual numbers are separated by semicolons or spaces.
Bray-Curtis Distance. When the formula for Sorensen dissimilarity is extended from presence/absence data to species abundance data, it results in the Bray-Curtis distance measure:
An included function called dino.dist () will take a matrix of species occurrences versus locality (or any analogous groupings) and return a full pairwise distance matrix as output.
In this chapter we shall consider several non-Euclidean distance measures that are popular in the environmental sciences: the Bray-Curtis dissimilarity, the L1 distance (also called the city-block or Manhattan distance) and the Jaccard index for presence-absence data.
Can compute various sample-sample distances using the microbiota composition of your samples: Bray Curtis ('bray') or any other ecological distance from phyloseq::distance() / vegan::vegdist() UniFrac distances (using the GUniFrac package)
Computes a number of similarity or distance measures between all pairs of rows. The data can be univariate or (more commonly) multivariate, with variables in columns. The results are given as a symmetric similarity/distance matrix.
In ecology and biology, the Bray–Curtis dissimilarity is a statistic used to quantify the dissimilarity in species composition between two different sites, based on counts at each site. It is named after J. Roger Bray and John T. Curtis who first presented it in a paper in 1957.
