custom header test
sim10(speciesData, rowWeights = runif(dim(speciesData)[1]), colWeights = runif(dim(speciesData)[2]))
Randomizes a binary matrix speciesData by reshuffling all elements. Rows and column probabilities are proportional to user-supplied row and column weights, which define relative suitability probabilities for species and sites. Makes a call to the vector_sample function.
This function incorporates vectors of weights for species and/or sites to condition the simulation. These two vectors are used as outer products to set cell probabilities for the entire matrix. Thus:
p(cell_ij)=p(row_i)p(col_j)Weights must be positive real numbers. The algorithm will scale them so they sum to 1.0, so they can be used in their natural units (e.g. island area, species abudance), and will be scaled properly. If all species (or sites) are assumed to be equally likely, the weight vector should be set to the same constant for all elements.
sim10 allows users to incorporate independent data on species occurrence probabilities and site suitabilities. This represents an important conceptual advance over standard co-occurrence analyses, which must infer these probabilities from the matrix itself. sim10 may generate empty rows or columns, especially if weights are very small for some species or sites. Also, the results may be sensitive to algebraic transformations of the weights (x, x^2, log(x), etc.), and these transformations may be hard to justify biologically. Nevertheless, sim10 is worth exploring for rich data sets with site and species attributes.
Jenkins, D.G. 2006. In search of quorum effects in metacommunity structure: species co-occurrence analyses. Ecology 87:1523-1531
Gotelli, N.J., G.R. Graves, and C. Rahbek. 2010. Macroecological signals of species interactions in the Danish avifauna. Proceedings of the National Academy of Sciences, U.S.A. 107: 530-535.
randomMatrix <- sim10(speciesData=matrix(rbinom(40,1,0.5),nrow=8))
vector_sample for weighted vector sampling.