Calculates similarity-sensitive normalised metacommunity beta diversity
(the effective number of distinct subcommunities. This measure may be
calculated for a series of orders, represented as a vector of qs.
norm_meta_beta(meta, qs)object of class metacommunity
vector of mode numeric containing q values
norm_meta_beta returns a standard output of class rdiv
R. Reeve, T. Leinster, C. Cobbold, J. Thompson, N. Brummitt, S. Mitchell, and L. Matthews. 2016. How to partition diversity. arXiv 1404.6520v3:1–9.
pop <- data.frame(a = c(1,3), b = c(1,1))
row.names(pop) <- paste0("sp", 1:2)
pop <- pop/sum(pop)
meta <- metacommunity(pop)
# Calculate normalised metacommunity beta diversity
norm_meta_beta(meta, 0:2)
#> measure q type_level type_name partition_level partition_name
#> 1 normalised beta 0 types metacommunity
#> 2 normalised beta 1 types metacommunity
#> 3 normalised beta 2 types metacommunity
#> diversity dat_id transformation normalised k max_d
#> 1 1.000000 naive NA NA NA NA
#> 2 1.031047 naive NA NA NA NA
#> 3 1.060714 naive NA NA NA NA