kernel.similarity {BAT} | R Documentation |
Calculate pairwise distance metrics (centroid and minimum distance) and similarity indices (Intersection, Jaccard, Soerensen-Dice) among n-dimensional hypervolumes.
kernel.similarity(comm)
comm |
A 'HypervolumesList' object, preferably built using function kernel.build. |
Computes a pairwise comparison between kernel density hypervolumes of multiple species or communities, based on the distance and similarity metrics implemented in hypervolume R package (Blonder et al., 2014, 2018). See Mammola (2019) for a description of the different indices, and a comparison between their performance. Note that computation time largely depends on the number of 'Hypervolume' objects in the list, and scales almost exponentially with the number of hypervolume axes.
Five pairwise distance matrices, one per each of the distance and similarity indices (in order: distance between centroids, minimum distance, Jaccard overlap, Soerensen-Dice overlap, and Intersection among hypervolumes).
Blonder, B., Lamanna, C., Violle, C. & Enquist, B.J. (2014) The n-dimensional hypervolume. Global Ecology and Biogeography, 23: 595-609.
Blonder, B., Morrow, C.B., Maitner, B., Harris, D.J., Lamanna, C., Violle, C., ... & Kerkhoff, A.J. (2018) New approaches for delineating n-dimensional hypervolumes. Methods in Ecology and Evolution, 9: 305-319.
Mammola, S. (2019) Assessing similarity of n-dimensional hypervolumes: Which metric to use?. Journal of Biogeography, 46: 2012-2023.
## Not run: comm <- rbind(c(1,1,1,1,1), c(1,1,1,1,1), c(0,0,1,1,1),c(0,0,1,1,1)) colnames(comm) = c("SpA","SpB","SpC","SpD", "SpE") rownames(comm) = c("Site 1","Site 2","Site 3","Site 4") trait <- cbind(c(2.2,4.4,6.1,8.3,3),c(0.5,1,0.5,0.4,4),c(0.7,1.2,0.5,0.4,5),c(0.7,2.2,0.5,0.3,6)) colnames(trait) = c("Trait 1","Trait 2","Trait 3","Trait 4") rownames(trait) = colnames(comm) hvlist = kernel.build(comm, trait) kernel.similarity(hvlist) hvlist = kernel.build(comm, trait, axes = 0.9) kernel.similarity(hvlist) ## End(Not run)