optim.alpha.stats {BAT} | R Documentation |
Average alpha diversity observed with a given number of samples per method.
optim.alpha.stats(comm, tree, methods, samples, runs = 0)
comm |
A samples x species x sites array, with either abundance or incidence data. |
tree |
A phylo or hclust object (used only for PD or FD) or alternatively a species x traits matrix or data.frame to build a functional tree. |
methods |
A vector specifying the method of each sample (length must be equal to nrow(comm)) |
samples |
A vector defining the number of samples per method to be evaluated (length must be equal to number of methods). |
runs |
Number of random permutations to be made to the sample order. Default is 1000. |
Different combinations of samples per method allow sampling different sub-communities. This function allows knowing the average TD, PD or FD values for a given combination, for one or multiple sites simultaneously. PD and FD are calculated based on a tree (hclust or phylo object, no need to be ultrametric).
A single average alpha diversity value. Rescaled to 0-1 if made for several sites, where 1 is the true diversity of each site.
comm1 <- matrix(c(1,1,0,2,4,0,0,1,2,0,0,3), nrow = 4, ncol = 3, byrow = TRUE) comm2 <- matrix(c(2,2,0,3,1,0,0,0,5,0,0,2), nrow = 4, ncol = 3, byrow = TRUE) comm <- array(c(comm1, comm2), c(4,3,2)) colnames(comm) <- c("Sp1","Sp2","Sp3") methods <- c("Met1","Met2","Met2","Met3") tree <- hclust(dist(c(1:3), method="euclidean"), method="average") optim.alpha.stats(comm,,methods, c(1,1,1)) optim.alpha.stats(comm, tree, methods = methods, samples = c(0,0,1), runs = 100)