kernel.dispersion {BAT} | R Documentation |
Average distance to centroid or dissimilarity between random points within the boundaries of the kernel density hypervolume.
kernel.dispersion(comm, func = "dissimilarity", frac = 0.1)
comm |
A 'Hypervolume' or 'HypervolumesList' object, preferably built using function kernel.build. |
func |
Function for calculating dispersion. One of 'divergence', 'dissimilarity' or 'regression'. |
frac |
A value between 0.01 and 1, indicating the fraction of random points to be used. Default is 0.1. |
This function calculates dispersion either: i) as the average distance between stochastic points within the kernel density hypervolume and the centroid of these points (divergence; Laliberte & Legendre, 2010; see also Carmona et al., 2019); ii) as the average distance between all points (dissimilarity, see also function BAT::dispersion); or iii) as the average distance between stochastic points within the kernel density hypervolume and a regression line fitted through the points. The number of stochastic points is controlled by the 'frac' parameter (increase this number for less deviation in the estimation).
A value or vector of dispersion values for each site.
Carmona, C.P., de Bello, F., Mason, N.W.H. & Leps, J. (2019) Trait probability density (TPD): measuring functional diversity across scales based on TPD with R. Ecology, 100: e02876.
Laliberte, E. & Legendre, P. (2010) A distance-based framework for measuring functional diversity from multiple traits. Ecology 91: 299-305.
## Not run: comm = rbind(c(1,3,0,5,3), c(3,2,5,1,0)) colnames(comm) = c("SpA", "SpB", "SpC", "SpD", "SpE") rownames(comm) = c("Site 1", "Site 2") trait = data.frame(body = c(1,2,3,4,4), beak = c(1,5,4,1,2)) rownames(trait) = colnames(comm) hv = kernel.build(comm[1,], trait) kernel.dispersion(hv) hvlist = kernel.build(comm, trait, axes = 2) kernel.dispersion(hvlist) kernel.dispersion(hvlist, func = "divergence") ## End(Not run)