BAT (version 2.1.1)

optim.alpha.stats: Efficiency statistics for alpha-sampling.

Description

Average alpha diversity observed with a given number of samples per method.

Usage

optim.alpha.stats(comm, tree, methods, samples, runs = 0)

Arguments

comm

A samples x species x sites array, with either abundance or incidence data.

tree

An hclust or phylo object (used only to optimize PD or FD sampling).

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.

Value

A single average alpha diversity value. Rescaled to 0-1 if made for several sites, where 1 is the true diversity of each site.

Details

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).

Examples

# NOT RUN {
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)
# }