Like R, the code provided here is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to modify and redistribute it under the conditions of the GNU General Public License, Version 3. In R, you can get details on the license by typing RShowDoc("GPL-3").
This code has been developed for published or ongoing projects. If the code has been used in a publication, I would appreciate a citation; the documentation includes a "how to cite" section in cases where this is possible. For the code to run correctly, you will need to download and then load into R the code for the main function and for any additional supporting functions.
If you have questions, run into problems or find bugs, send me a message at sebastian.tello [at] mobot.org
This code has been developed for published or ongoing projects. If the code has been used in a publication, I would appreciate a citation; the documentation includes a "how to cite" section in cases where this is possible. For the code to run correctly, you will need to download and then load into R the code for the main function and for any additional supporting functions.
If you have questions, run into problems or find bugs, send me a message at sebastian.tello [at] mobot.org
NULL MODELS AND RANDOMIZATION TESTS
assemble.from.pool.randA |
A randomization algorithm that produces matrices of species composition expected by randomly re-assigning individuals from the species pool into local assemblages.
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partition.div.nullM |
A function to partition diversity into alpha, gamma and beta-components from an empirical matrix of species composition and from a series of null matrices produced by a randomization algorithm.
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Main function
Supporting functions Last modified: 18-November-2014 |
compo.dists.nullM
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A function to calculate dissimilarities
among sites of an empirical matrix of species composition and from a series of null matrices produced by a randomization algorithm. |
omega.randT |
A function that calculates the Omega statistic of aggregation for a point pattern, and tests whether the values are significantly different than expected by complete spatial randomness.
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