sim.table: Similarity Summary Table
In vegetarian: Jost Diversity Measures for Community Data

Description Usage Arguments Details Value Author(s) References See Also Examples

Creates a summary table of community overlap for all possible pairwise combinations of samples (e.g. sites, plots) using the similarity function.

1	sim.table(abundances, q = 1, labels = FALSE, half = TRUE, diag = TRUE, boot = FALSE, boot.arg = list(s.sizes = NULL, num.iter = 100))

`abundances`	Community data as a matrix where columns are individual species and rows are sites. Matrix elements are abundance data (e.g. counts, percent cover estimates).
`q`	Order of the diversity measure. Defaults to the Shannon case where q = 1.
`labels`	Logical statement. If labels=TRUE, then site names are given as the first column of the abundance matrix. The default is labels=FALSE where no site names are given in the abundance matrix.
`half`	Logical statement that changes the display of the resulting matrix. The default is half=TRUE, where the similarities are not repeated and are given as NaN values on one side of the diagonal in the resulting matrix. If half=FALSE, then the displayed similarities are repeated on either side of the diagonal of the resulting matrix.
`diag`	Logical statement that changes the display of the resulting matrix. The default is diag=TRUE, where the elements in the diagonal of the resulting similarity matrix are all equal to one because a sample should be completely identical to itself. If diag=FALSE, then then the elements in the diagonal of the resulting similarity matrix are given NaN values.
`boot`	Logical indicating whether to use bootstrapping to estimate uncertainty. If boot=TRUE, only standard errors will be output in table; to get both values and standard error of similarities, must call sim.groups twice, setting boot to both TRUE and FALSE
`boot.arg`	(optional) List of arguments to pass bootstrapping function: list(s.sizes=number you specify, num.iter=number you specify)

This function calculates pairwise similarity for two or more samples, so the abundance data must be a matrix with two or more rows (samples). Depending on the specification of the order (q), other different similarity indices may be calculated (e.g. Sorenson index when q=0, Horn index when q=1, Morisita-Horn index when q=2) (Jost 2007).

A matrix of pairwise similarities showing community overlap calculated using the similarity function.

Noah Charney, Sydne Record

Jost, L. 2007. Partitioning diversity into independent alpha and beta components. Ecology 88(10): 2427-2439.

similarity, sim.groups

data(simesants)
sim.table(simesants[,-1])
sim.table(simesants,labels=TRUE)
sim.table(simesants,labels=TRUE, diag=FALSE)
sim.table(simesants,labels=TRUE, half=FALSE)
sim.table(simesants,labels=TRUE,boot=TRUE)#standard errors only
sim.table(simesants,labels=TRUE,boot=TRUE, boot.arg=list(num.iter=200), q=2)#standard errors only