![]() # build a matrix of fuzzy similarity among these fuzzy Rotifers.invd <- distPres(rotif.env, sp.cols = 18:47,Ĭls = c("Longitude", "Latitude"), id.col = 1, suffix = ".d", # based on inverse distance to presences: # calculate a fuzzy version of the presence-absence data # build a matrix of similarity among these binary dataīin.sim.mat <- simMat(rotif.env, method = "Jaccard") # load and look at the rotif.env presence-absence data: Methods in Ecology and Evolution, 6: 853-858. (2015) fuzzySim: applying fuzzy logic to binary similarity indices in ecology. Similarity is calculated with the fuzzy version of the index specified in method, which yields traditional binary similarity if the data are binary (0 or 1), or fuzzy similarity if the data are fuzzy (between 0 and 1) (Barbosa, 2015).īarbosa A.M. This function returns a square matrix of pair-wise similarities among the species distributions (columns) in data. The fuzzy versions of species occurrence data and of binary similarity indices introduce tolerance for small spatial differences in species' occurrence localities, allow for uncertainty about species occurrence, and may compensate for under-sampling and geo-referencing errors (Barbosa, 2015). Integer value indicating the amount of messages to display currently meaningful values are 0, 1, and 2 (the default). ![]() Logical value indicating whether the upper triangle of the matrix (symmetric to the lower triangle) should be filled. Logical value indicating whether the diagonal of the matrix should be filled (with ones). The similarity index whose fuzzy version to use. These data can also be transposed for comparing regional species compositions. Fuzzy presence-absence can be obtained, for example, with multGLM, distPres or multTSA. one column per species), with 1 meaning presence, 0 meaning absence, and values in between for fuzzy presence (or the degree to which each locality belongs to the set of species presences see Zadeh, 1965). Usage simMat(data, method, diag = TRUE, upper = TRUE, verbosity = 2)Ī matrix or data frame containing (optionally fuzzy) species presence-absence data (in wide format, i.e. SimMat takes a matrix or data frame containing species occurrence data or regional species composition, either categorical (0 or 1) or fuzzy (between 0 and 1), and uses the fuzSim function to calculate a square matrix of pair-wise similarities between them, using a fuzzy logic version (Barbosa, 2015) of the specified similarity index.
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