How to evaluate the different results resulting from different methods about icamp1 HOT 4 CLOSED

The absolute value of 'relative importance of stochastic processes' can rarely be the same. When different methods showed the same relative trend, e.g., stochasticity increased or decreased, you may draw some reliable conclusion. Generally, pNST is better than tNST, since dispersal limitation or homogenizing dispersal, which are stochastic processes, may be counted as deterministic in tNST. If different phylogenetic groups are governed by totally different processes and their relative abundances are comparable, iCAMP is theoretically better than QPEN. However, each statistical method has its special assumptions which can be not valid in real cases with complex situations, thus we say it will be more reliable if multiple methods show the same relative change trend. But if anyway you cannot get consistent results even in the relative trend, you may examine the assumptions of each method and how reasonable their results are, to decide which one should be more reliable.

In your case, i would suggest you try some lower bin.size.limit values (e.g. 24). If the environmental factor is not the key one, RAsig may not be a good standard; instead, it may be better to make stochasticity at a comparable level as NST.

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About qpen function: yes, you can use qpen.test to get summary of each group. the function qpen.cm means to assign different metacommunities for different samples.

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Dear Ning
Thanks a lot ! AND I was wondering if there was a way to get the bin tree conveniently from the results of iCAMP, can you give me a suggestion?
Sincerely

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You may use the most abundant taxon (top taxon) in each bin to represent the bin then extract the bin-top-taxa tree by function keep.tip in R package ape. Top taxon of each bin is available in the element Bin.TopClass of the output of function icamp.bins (see step 10 in example R code https://github.com/DaliangNing/iCAMP1/blob/master/Examples/SimpleOTU/icamp.test.r)

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