View source: R/ec_cooccurrence.R
ec_cooc_count_pair | R Documentation |
Compute the co-occurrence between all pairs of species and descriptive metrics of the co-occurrence network.
ec_cooc_count_pair(x)
ec_cooc_count_triplet(x)
ec_checkerboard(x)
x |
a |
Currently bi
tests the presence of a significant value of occurrence
assuming species occurrence are independent binomial distribution. by
takes the limited number of sites into account by using an hypergeometric
distribution (see Veech 2013). Note that if the number of sites is large and
the occurrence of both species relatively low, then bi
and hy
give very similar results.
ec_cooc_count_pair()
: A matrix with all pairs of species and the corresponding co-occurrence counts.
ec_cooc_count_triplet()
: A matrix with all triplets of species and the corresponding co-occurrence counts.
ec_checkerboard()
: Compute the checkerboard score and return a list of three elements:
units
which incudes checkerboard units and t
c_score
checkerboard scores.
c_score_s2
the S2 statistics in Roberts & Stone (1990).
Veech, J. A. (2013). A probabilistic model for analysing species co-occurrence: Probabilistic model. Global Ecology and Biogeography, 22(2), 252–260.
Arita, H. T. (2016). Species co-occurrence analysis: Pairwise versus matrix-level approaches: Correspondence. Global Ecology and Biogeography, 25(11), 1397–1400.
Stone, L., & Roberts, A. (1990). The checkerboard score and species distributions. Oecologia, 85(1), 74–79. https://doi.org/10.1007/BF00317345
Roberts, A., & Stone, L. (1990). Island-sharing by archipelago species. Oecologia, 83(4), 560–567. https://doi.org/10.1007/BF00317210
mat <- ec_generate_pa(1000, 6, .2)
out <- ec_cooc_count_pair(mat)
#plot(out$zs_bi*sqrt(1/0.2), out$zs_hy)
#abline(0,1)
# Classical example, in Stone & Roberts 1990
mat0 <- matrix(0, 10, 10)
mat1 <- matrix(1, 10, 10)
matU <- rbind(cbind(mat1, mat0), cbind(mat0, mat1))
ec_checkerboard(matU)
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