Pairs analyses?

[davharris]

Hi @emhart @ngotelli, hope you both had nice weekends.

My Windows box doesn't want to run Dr. Ulrich's binary (it thinks the .exe file might be a virus for some reason). I was wondering if either of you would be interested in looking at my implementation (below) and letting me know if it looks correct to you. If it does, I'd be happy to submit it as a pull request.

Thanks!

library("EcoSimR")

set.seed(1)

# create “observed” data matrix ----------------------------------------------

m = matrix(rbinom(50^2, size = 1, prob = .1), ncol = 100)

n_samples = 999   # number of samples to collect
thin = 1000       # number of iterations between samples

n_species = nrow(m)

# I think this is a correct way to get scaled pairwise C-scores...
scaled_pairwise_c_score = function(x){
  shared = tcrossprod(m)
  sums = rowSums(m)
  upper = upper.tri(shared)
  raw_scores = (sums[row(shared)[upper]] - shared[upper]) *
    (sums[col(shared)[upper]] - shared[upper])

  raw_scores / tcrossprod(sums)[upper]
}

# Record observed c_scores
observed = scaled_pairwise_c_score(m)

# Null --------------------------------------------------------------------

# Create empty matrix to store null results for all n-choose-2 pairs
null = matrix(NA, nrow = choose(n_species, 2), ncol = n_samples)

for(i in 1:n_samples){
  for(j in 1:thin){
    # Update a 
    m = sim9_single(m)
  }
  # Fill in the next column of the null distribution
  null[ , i] = scaled_pairwise_c_score(m)  
}

# Calculate P-values ------------------------------------------------------

# For each observed value, compare it to the null distribution from _ALL_ 
# species pairs to calculate p-values
p = sapply(observed, function(x) mean(x < null))

[emhart]

@davharris I'll let @ngotelli chime in there because he knows this literature way better than I do.

[ngotelli]

Hi @davharris, sorry to be out of touch. but I have been busy with grading. Yes, I would love to be able to have a PAIRS analysis in EcoSimR. You might want to check out the older branch by Adam Clarke, as I think he may have been working on this as well.

I didn't have time to really run your code, but here are a few comments for now:

1) You will want to run a burn_in before you start calling sim9_single. The sim9 algorithm we use is slightly different than Ulrich's, and ours may not need as much thinning. In any case, we should probably add an option later on for thinning of sim9.

2) I didn't run the math, but your code for rescaled c_score looks correct. It should rescale the C-score for each individual pair on a 0-1 range.

3) That is really elegant to calculate the tail probability by taking the average of a boolean vector; I will see about setting that up for the standard model summary. But for the full PAIRS analysis, you are going to need the probability in both tails:

p.upper <- mean(x <= null)
p.lower <- mean(x >= null)

For species pairs with low row totals, the chances of a tie are very large, so it is important to have each inequality include the equals case.

4) The output set up looks good, with each row as a species pair. Take a look at Ulrich's output files (from the help system for PAIRS), as there are a lot of other useful columns to add to this table. Especially nice is to include for species pairij are rowsum(i), rowsum(j), and #shared(ij). Those elements make it much easier to check and interpret the output.

5) This set up looks correct, but you still need to do the binning and sorting. I would include in your functions a parameter for the number of bins, because that can potentially influence the results.

That's all for now, hope it helps.

Thanks,

Nick

[davharris]

Thanks @ngotelli. I've played around with it a bit more, and I think I must be doing something wrong.

If most species are rare, then most of the simulated landscapes involving a given pair will have no overlap, corresponding to a scaled C-score of 1. In one matrix I simulated (250 columns, average of 2-3 occurrences per species), 99.3% of the observed scores were 1 and 99.3% of the null scores were also 1.

Long story short, using a completely random matrix, I ended up with almost 99% of observed values matching the upper limit of 1. If I'm understanding correctly, that's a Type I error rate of 99%.

Any idea what's going on?

[ngotelli]

Hi @davharris. Sorry to be out of touch. It sounds like you are missing some of the steps needed for the empirical Bayes approach:

1. Carry out the null model analysis on the scaled C-scores (as you have done).
2. Assign each pair to one of 20 evenly spaced bins on the 0-1 scale. Also include 2 bins for the 0 and the 1 classes (complete overlap or perfect checkerboard pairs).
3. Within each bin, order the pairs by their C score (or by their p values for the 0 bin and the 1 bin).
4. For each bin, use the null model results to calculate the expected number of species pairs and a 95% confidence interval.
5. For the Bayes Mean method (less conservative), keep only the pairs that are above and beyond the mean number for that bin, and then retain only those that are significant for the individual test. By this criterion, I believe that all of the checkerboards from the random matrix are going to be discarded because they comprise the same fraction of the bin as in the randomized data.
6. For the stricter Bayes confidence interval method, retain only those extreme pairs that are above the 95% confidence interval for that bin and are statistically significant.

The Gotelli and Ulrich (2010) paper gives more details. But from your code, I don't see that you have worked up the mean and confidence interval of species pair per bin for the null assemblages. That's what you need next.

Hope that helps.

[davharris]

Hi Nick, thanks for the response.

1) I was mainly asking if my results so far looked like they matched the CL criterion, since I hadn’t implemented the binning procedure yet.

2) I’m not sure I understand how the Bayes-M procedure would even work if 99% of the data points have identical values. 99% of the data points would end up in the same bin, and there would be no way to sort them.

3) Even if Bayes-M procedure ended up reducing the number of false rejections by a factor of 2 or so, wouldn’t the Type I error rate be unacceptably high?

Thanks again for thinking this through with me.

Dave

On Mar 2, 2015, at 7:10 PM, Nick Gotelli notifications@github.com wrote:

Hi @davharris https://github.com/davharris. Sorry to be out of touch. It sounds like you are missing some of the steps needed for the empirical Bayes approach:

Carry out the null model analysis on the scaled C-scores (as you have done). Assign each pair to one of 20 evenly spaced bins on the 0-1 scale. Also include 2 bins for the 0 and the 1 classes (complete overlap or perfect checkerboard pairs). Within each bin, order the pairs by their C score (or by their p values for the 0 bin and the 1 bin). For each bin, use the null model results to calculate the expected number of species pairs and a 95% confidence interval. For the Bayes Mean method (less conservative), keep only the pairs that are above and beyond the mean number for that bin, and then retain only those that are significant for the individual test. By this criterion, I believe that all of the checkerboards from the random matrix are going to be discarded because they comprise the same fraction of the bin as in the randomized data. For the stricter Bayes confidence interval method, retain only those extreme pairs that are above the 95% confidence interval for that bin and are statistically significant. The Gotelli and Ulrich (2010) paper gives more details. But from your code, I don't see that you have worked up the mean and confidence interval of species pair per bin for the null assemblages. That's what you need next.

Hope that helps.

— Reply to this email directly or view it on GitHub https://github.com/GotelliLab/EcoSimR/issues/54#issuecomment-76879078.

[ngotelli]

Hi Dave:

Here are some quick answers interspersed:

Quoting "David J. Harris" <notifications@github.com>:

Hi Nick, thanks for the response.

1) I was mainly asking if my results so far looked like they
matched the CL criterion, since I hadn’t implemented the binning
procedure yet.

I hadn't run your code, but, yes, this looks correct. You set up a
vector that has the re-scaled C-score and the upper (and lower) tail
p values.

2) I’m not sure I understand how the Bayes-M procedure would even
work if 99% of the data points have identical values. 99% of the
data points would end up in the same bin, and there would be no way
to sort them.

Sort them first by the rescaled C-score, and then sort them by the
upper-tail p values. It would be a pretty strange data set to have
99% of the pairs with identical values. I guess a very sparse random
matrix might give you something like this with the fixed-fixed
algorithm.

3) Even if Bayes-M procedure ended up reducing the number of
false rejections by a factor of 2 or so, wouldn’t the Type I error
rate be unacceptably high?

As we discussed, if all of the pairs in the matrix are random, then
detecting any significant value would represent an FDR of 100%. But
the problem with real data is that there probably is a small core of
species pairs that are statistically significant (because of species
interactions, habitat associations, and/or dispersal limitation),
and we want a way to identify those and screen out false positives.
In the FDR literature, there is much more concern that even these
methods are too conservative, and will miss a lot of the
associations that should be present.

For this reason, it is worthwhile to set up a range of test
criteria. At the most liberal end, you would have the simple
confidence interval test, which keeps every pair for which p < 0.05.
At the other end is the standard Bonferroni test, which takes
alpha/(# pairs tested) as the cut point. In between, you have the
Benjamini and Hochberg test, the Bayes mean, and Bayes confidence
intervals... and your own test. With all of these, there is going to
be an inevitable trade-off between Type I and Type II error, which
you will see when you test them against random versus structured
matrices. It would be great to eventually get all of this into
EcoSimR.

Thanks again for thinking this through with me.

Of course. Keep in touch.

Dave

On Mar 2, 2015, at 7:10 PM, Nick Gotelli
notifications@github.com wrote:

Hi @davharris https://github.com/davharris. Sorry to be out
of touch. It sounds like you are missing some of the steps needed
for the empirical Bayes approach:

Carry out the null model analysis on the scaled C-scores (as
you have done). Assign each pair to one of 20 evenly spaced bins on the 0-1
scale. Also include 2 bins for the 0 and the 1 classes (complete
overlap or perfect checkerboard pairs). Within each bin, order the pairs by their C score (or by their
p values for the 0 bin and the 1 bin). For each bin, use the null model results to calculate the
expected number of species pairs and a 95% confidence interval. For the Bayes Mean method (less conservative), keep only the
pairs that are above and beyond the mean number for that bin, and
then retain only those that are significant for the individual test.
By this criterion, I believe that all of the checkerboards from the
random matrix are going to be discarded because they comprise the
same fraction of the bin as in the randomized data. For the stricter Bayes confidence interval method, retain only
those extreme pairs that are above the 95% confidence interval for
that bin and are statistically significant. The Gotelli and Ulrich (2010) paper gives more details. But
from your code, I don't see that you have worked up the mean and
confidence interval of species pair per bin for the null
assemblages. That's what you need next.

Hope that helps.

— Reply to this email directly or view it on GitHub
https://github.com/GotelliLab/EcoSimR/issues/54#issuecomment-76879078.

 

— Reply to this email directly or view it on GitHub[1].  

 

---

Nicholas J. Gotelli       Office Phone: 802-656-0450
Department of Biology     Lab Phone: 802-656-0451   
University of Vermont     Fax: 802-656-2914
Burlington, VT 05405      e-mail: ngotelli@uvm.edu
********************************************************
Home Page (with manuscript pdfs):

http://www.uvm.edu/~ngotelli/homepage.html

Musician's Corner (with mp3s):

http://www.uvm.edu/~ngotelli/musicpage/music.html

NEW: EcoSimR (free software for null model analysis):

http://www.uvm.edu/~ngotelli/EcoSim/EcoSim.html

---

Links:

[1] https://github.com/GotelliLab/EcoSimR/issues/54#issuecomment-76890740

[davharris]

Hi @ngotelli, hope you had a great weekend.

I was wondering, could you run Pairs on this data set and post the output? The first 18 species were generated randomly, then the 19th and 20th species were added as either a copy or mirror-image of the 18th species.

I think that a set of results on this data set would help me determine if the code I've written so far is on the right track.

Thanks again for all your feedback,

Dave

[ngotelli]

@davharris I ran PAIRS on this matrix with fixed-fixed and all the default settings, and pushed the output files to you.

[davharris]

Thanks @ngotelli. I don't see the output files, though? Could you post a link to them or send me an email? Thanks again.

[ngotelli]

Sorry. I tried to push my results to you, but I don't have permission. Here they are attached. Looks like you have 4 significant pairs by the simple confidence interval, 3 pairs by the Bayesian mean criterion, and 0 pairs by the Bayesian confidence interval. Of those 4 pairs, 3 are truly non-random. So your FDR for the Bayesian mean criterion is 1/3 = 0.333, and 0/3 = 0.000 for the Bayesian confidence interval. However, the Bayesian confidence interval did not identify any of the 3 non-random pairs, and the Bayesian mean identified only 2 of the 3 non-random pairs.

Nick

Quoting "David J. Harris" <notifications@github.com>:

Thanks @ngotelli[1]. I don't see the output files, though? Could you
post a link to them or send me an email? Thanks again.

— Reply to this email directly or view it on GitHub[2].  

 

---

Nicholas J. Gotelli       Office Phone: 802-656-0450
Department of Biology     Lab Phone: 802-656-0451   
University of Vermont     Fax: 802-656-2914
Burlington, VT 05405      e-mail: ngotelli@uvm.edu
********************************************************
Home Page (with manuscript pdfs):

http://www.uvm.edu/~ngotelli/homepage.html

Musician's Corner (with mp3s):

http://www.uvm.edu/~ngotelli/musicpage/music.html

NEW: EcoSimR (free software for null model analysis):

http://www.uvm.edu/~ngotelli/EcoSim/EcoSim.html

---

Links:

[1] https://github.com/ngotelli [2] https://github.com/GotelliLab/EcoSimR/issues/54#issuecomment-78520922

File: m.txt Species: 20 Sites: 100 MatFill: 0.10 Occ: 192 5.00% Confidence limit Model: fixed - fixed
Index File In SimIn StdDevIn Z-In StIndex SkewIn LowerCLIn UpperCLIn Checkerboard m.txt 132 131.60 2.55 0.16 0.0030 0.37 127.00 138.00 Combinations m.txt 1 0.00 0.00 1.00 0.0000 0.00 0.00 0.00 C-Score m.txt 0.006 0.005 0.000 4.313 0.1383 0.301 0.005 0.006 Soerensen m.txt 0.0543 0.0508 0.0022 1.5911 0.0687 0.2453 0.0466 0.0554 Absences m.txt 6461.147 6450.124 4.759 2.316 0.0017 0.058 6445.653 6455.643 BR m.txt 80 82.54 1.71 -1.49 0.4167 -0.20 79.00 86.00 Correlation m.txt 0.5253 0.5012 0.0062 3.8636 0.0480 0.1531 0.4895 0.5138 Variancetest m.txt 2.2818 2.2818 0.0031 0.0009 0.0000 0.0000 2.2818 2.2818

Species site1 site2 site3 site4 site5 site6 site7 site8 site9 site10 site11 site12 site13 site14 site15 site16 site17 site18 site19 site20 site21 site22 site23 site24 site25 site26 site27 site28 site29 site30 site31 site32 site33 site34 site35 site36 site37 site38 site39 site40 site41 site42 site43 site44 site45 site46 site47 site48 site49 site50 site51 site52 site53 site54 site55 site56 site57 site58 site59 site60 site61 site62 site63 site64 site65 site66 site67 site68 site69 site70 site71 site72 site73 site74 site75 site76 site77 site78 site79 site80 site81 site82 site83 site84 site85 site86 site87 site88 site89 site90 site91 site92 site93 site94 site95 site96 site97 site98 site99 site100 sp1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 sp2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 sp3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 sp4 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 sp5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 sp6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 sp7 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 sp8 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 sp9 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 sp10 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 sp11 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 1 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 sp12 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 sp13 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 sp14 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 1 0 0 0 0 0 1 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 sp15 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 sp16 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 sp17 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 sp18 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 1 0 0 0 0 sp19 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 1 0 0 0 0 sp20 0 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 0 1 0 1 1 0 1 1 1 1

Packed matrix: m.txt

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                                 4     4     4     4     4     4     4     4     3     3     3     3     3     3     3     3     3     3     3     3     3     3     3     3     3     2     2     2     2     2     2     2     2     2     2     2     2     2     2     2     2     2     2     2     2     2     2     2     2     2     2     2     2     2     2     2     2     2     2     1     1     1     1     1     1     1     1     1     1     1     1     1     1     1     1     1     1     1     1     1     1     1     1     1     1     1     1     1     1     1     1     1     1     1     1     1     1     1     1     1

Last packed randomized matrix: m.txt No. of Swaps: 20000

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Species site17 site31 site49 site77 site84 site85 site93 site97 site28 site11 site41 site47 site6 site50 site51 site63 site66 site18 site20 site21 site87 site88 site91 site23 site27 site1 site15 site52 site58 site59 site60 site61 site62 site29 site30 site71 site72 site73 site75 site76 site16 site78 site79 site82 site83 site39 site7 site43 site44 site89 site45 site92 site24 site94 site95 site96 site26 site98 site99 site8 site32 site33 site34 site64 site65 site35 site67 site68 site69 site70 site36 site37 site38 site74 site19 site40 site9 site42 site10 site80 site81 site22 site4 site46 site12 site86 site48 site25 site13 site90 site14 site5 site53 site54 site55 site56 site57 site2 site3 site100
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0.0 0.0 0.0 0.0 0.0 sp14 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 sp10 0.0 0.0 0.0 1.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 sp5 0.0 1.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 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0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 sp1 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 sp2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 sp13 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 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File: m.txt Species: 20 Sites: 100 MatFill: 0.10 Occ: 192 5.00% Confidence limit Model: fixed - fixed

No Score ObsNumber ExpNumber StDevExp Skewness LowerCL UpperCL Z-Score OddsRMean OddsRCL 1 0.000 14.000 10.830 1.562 -0.211 8.000 14.000 2.029 0.226 0.000 2 0.025 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 3 0.075 0.000 0.410 0.492 0.377 0.000 1.000 -0.834 0.000 0.000 4 0.125 2.000 2.000 1.095 0.141 0.000 4.000 0.000 0.000 0.000 5 0.175 1.000 2.030 1.081 0.721 0.000 5.000 -0.953 0.000 0.000 6 0.225 0.000 1.510 0.985 0.295 0.000 4.000 -1.533 0.000 0.000 7 0.275 1.000 0.820 0.805 0.941 0.000 3.000 0.224 0.180 0.000 8 0.325 0.000 1.360 1.100 1.042 0.000 4.000 -1.236 0.000 0.000 9 0.375 0.000 1.380 1.147 0.345 0.000 4.000 -1.203 0.000 0.000 10 0.425 4.000 3.530 1.506 0.583 1.000 7.000 0.312 0.118 0.000 11 0.475 1.000 1.920 1.230 0.290 0.000 5.000 -0.748 0.000 0.000 12 0.525 4.000 3.070 1.485 0.405 1.000 6.000 0.626 0.233 0.000 13 0.575 4.000 4.680 1.827 0.503 2.000 9.000 -0.372 0.000 0.000 14 0.625 5.000 6.270 2.019 0.527 3.000 11.000 -0.629 0.000 0.000 15 0.675 4.000 6.470 2.090 0.294 2.000 11.000 -1.182 0.000 0.000 16 0.725 8.000 6.900 2.071 0.416 3.000 11.000 0.531 0.137 0.000 17 0.775 10.000 5.200 2.035 0.564 2.000 10.000 2.359 0.480 0.000 18 0.825 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 19 0.875 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 20 0.925 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 21 0.975 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 22 1.000 132.000 131.620 2.440 0.075 127.000 136.000 0.156 0.003 0.000

No Sp1 Sp2 S1 S2 Com Obs.Score Exp.Score Exp.StDev Skewness LowerCL UpperCL Z-Score Alpha >MeanScore >CLScore >BJScore Alpha 1 sp20 sp11 93 10 8 0.183 0.095 0.083 0.280 0.000 0.277 1.06 0.28918046 0.00 0.00 0.00 1.00000000 2 sp20 sp3 93 8 7 0.116 0.093 0.093 1.217 0.000 0.355 0.24 0.80798608 0.00 0.00 0.00 1.00000000 3 sp20 sp4 93 8 7 0.116 0.099 0.106 1.218 0.000 0.355 0.16 0.87680042 0.00 0.00 0.00 1.00000000 4 sp20 sp18 93 7 0 1.000 0.083 0.101 1.104 0.000 0.270 9.10 0.00000000 9.10 0.00 9.10 0.00000000 5 sp20 sp19 93 7 0 1.000 0.112 0.107 1.004 0.000 0.410 8.30 0.00000000 0.00 0.00 8.30 0.00000000 6 sp20 sp9 93 7 7 0.000 0.117 0.121 0.827 0.000 0.410 -0.97 0.33208853 0.00 0.00 0.00 1.00000000 7 sp20 sp14 93 6 6 0.000 0.103 0.118 1.026 0.000 0.319 -0.87 0.38243902 0.00 0.00 0.00 1.00000000 8 sp20 sp10 93 6 6 0.000 0.076 0.107 1.119 0.000 0.319 -0.71 0.47535962 0.00 0.00 0.00 1.00000000 9 sp20 sp5 93 5 5 0.000 0.104 0.141 1.345 0.000 0.387 -0.74 0.45997190 0.00 0.00 0.00 1.00000000 10 sp20 sp6 93 5 5 0.000 0.113 0.131 0.766 0.000 0.387 -0.87 0.38688040 0.00 0.00 0.00 1.00000000 11 sp20 sp15 93 5 5 0.000 0.087 0.126 1.445 0.000 0.387 -0.69 0.49319541 0.00 0.00 0.00 1.00000000 12 sp20 sp7 93 4 4 0.000 0.104 0.139 0.971 0.000 0.489 -0.75 0.45174241 0.00 0.00 0.00 1.00000000 13 sp20 sp1 93 4 4 0.000 0.095 0.137 1.162 0.000 0.489 -0.69 0.49007499 0.00 0.00 0.00 1.00000000 14 sp20 sp2 93 4 4 0.000 0.124 0.148 0.807 0.000 0.489 -0.84 0.40298373 0.00 0.00 0.00 1.00000000 15 sp20 sp13 93 3 3 0.000 0.114 0.176 1.257 0.000 0.659 -0.65 0.51470387 0.00 0.00 0.00 1.00000000 16 sp20 sp16 93 3 3 0.000 0.091 0.146 1.010 0.000 0.326 -0.62 0.53287095 0.00 0.00 0.00 1.00000000 17 sp20 sp17 93 3 3 0.000 0.141 0.198 1.139 0.000 0.659 -0.71 0.47781467 0.00 0.00 0.00 1.00000000 18 sp20 sp8 93 2 1 0.495 0.094 0.194 1.629 0.000 0.495 2.06 0.03862685 0.00 0.00 0.00 1.00000000 19 sp20 sp12 93 2 2 0.000 0.109 0.228 2.028 0.000 0.495 -0.48 0.63303506 0.00 0.00 0.00 1.00000000 20 sp11 sp3 10 8 1 0.788 0.846 0.156 -0.564 0.600 1.000 -0.38 0.70738900 0.00 0.00 0.00 1.00000000 21 sp11 sp4 10 8 3 0.438 0.839 0.166 -0.733 0.438 1.000 -2.42 0.01549155 0.00 0.00 0.00 1.00000000 22 sp11 sp18 10 7 2 0.571 0.860 0.153 -0.568 0.571 1.000 -1.88 0.05984974 0.00 0.00 0.00 1.00000000 23 sp11 sp19 10 7 2 0.571 0.841 0.161 -0.600 0.571 1.000 -1.68 0.09328717 0.00 0.00 0.00 1.00000000 24 sp11 sp9 10 7 1 0.771 0.837 0.166 -0.849 0.400 1.000 -0.40 0.69217157 0.00 0.00 0.00 1.00000000 25 sp11 sp14 10 6 1 0.750 0.845 0.167 -0.747 0.533 1.000 -0.57 0.57151860 0.00 0.00 0.00 1.00000000 26 sp11 sp10 10 6 1 0.750 0.848 0.155 -0.435 0.533 1.000 -0.63 0.52695560 0.00 0.00 0.00 1.00000000 27 sp11 sp5 10 5 0 1.000 0.850 0.179 -0.717 0.480 1.000 0.84 0.40154290 0.00 0.00 0.00 1.00000000 28 sp11 sp6 10 5 1 0.720 0.881 0.176 -1.260 0.480 1.000 -0.91 0.36158502 0.00 0.00 0.00 1.00000000 29 sp11 sp15 10 5 0 1.000 0.822 0.179 -0.416 0.480 1.000 0.99 0.32006794 0.00 0.00 0.00 1.00000000 30 sp11 sp7 10 4 0 1.000 0.891 0.164 -1.044 0.675 1.000 0.67 0.50432682 0.00 0.00 0.00 1.00000000 31 sp11 sp1 10 4 0 1.000 0.886 0.184 -1.335 0.400 1.000 0.62 0.53611898 0.00 0.00 0.00 1.00000000 32 sp11 sp2 10 4 0 1.000 0.877 0.193 -1.407 0.400 1.000 0.64 0.52448940 0.00 0.00 0.00 1.00000000 33 sp11 sp13 10 3 0 1.000 0.871 0.217 -1.422 0.267 1.000 0.59 0.55340439 0.00 0.00 0.00 1.00000000 34 sp11 sp16 10 3 0 1.000 0.907 0.196 -1.979 0.267 1.000 0.48 0.63386410 0.00 0.00 0.00 1.00000000 35 sp11 sp17 10 3 0 1.000 0.873 0.199 -1.150 0.600 1.000 0.64 0.52395564 0.00 0.00 0.00 1.00000000 36 sp11 sp8 10 2 0 1.000 0.929 0.196 -2.729 0.450 1.000 0.36 0.71868348 0.00 0.00 0.00 1.00000000 37 sp11 sp12 10 2 0 1.000 0.973 0.120 -4.256 0.450 1.000 0.23 0.81854486 0.00 0.00 0.00 1.00000000 38 sp3 sp4 8 8 1 0.766 0.843 0.179 -0.832 0.391 1.000 -0.44 0.66350645 0.00 0.00 0.00 1.00000000 39 sp3 sp18 8 7 1 0.750 0.866 0.176 -0.940 0.536 1.000 -0.66 0.51101506 0.00 0.00 0.00 1.00000000 40 sp3 sp19 8 7 1 0.750 0.882 0.166 -1.228 0.536 1.000 -0.80 0.42625356 0.00 0.00 0.00 1.00000000 41 sp3 sp9 8 7 1 0.750 0.862 0.167 -0.840 0.536 1.000 -0.67 0.50222284 0.00 0.00 0.00 1.00000000 42 sp3 sp14 8 6 1 0.729 0.875 0.158 -0.807 0.500 1.000 -0.92 0.35657346 0.00 0.00 0.00 1.00000000 43 sp3 sp10 8 6 0 1.000 0.871 0.169 -1.129 0.500 1.000 0.76 0.44720387 0.00 0.00 0.00 1.00000000 44 sp3 sp5 8 5 0 1.000 0.892 0.149 -0.781 0.700 1.000 0.72 0.46926945 0.00 0.00 0.00 1.00000000 45 sp3 sp6 8 5 1 0.700 0.842 0.183 -0.637 0.450 1.000 -0.78 0.43738425 0.00 0.00 0.00 1.00000000 46 sp3 sp15 8 5 0 1.00

[davharris]

Thanks Nick!

Dave

On Mar 12, 2015, at 10:58 AM, Nick Gotelli notifications@github.com wrote:

Sorry. I tried to push my results to you, but I don't have permission. Here they are attached. Looks like you have 4 significant pairs by the simple confidence interval, 3 pairs by the Bayesian mean criterion, and 0 pairs by the Bayesian confidence interval. Of those 4 pairs, 3 are truly non-random. So your FDR for the Bayesian mean criterion is 1/3 = 0.333, and 0/3 = 0.000 for the Bayesian confidence interval. However, the Bayesian confidence interval did not identify any of the 3 non-random pairs, and the Bayesian mean identified only 2 of the 3 non-random pairs.

Nick

Quoting "David J. Harris" notifications@github.com:

Thanks @ngotelli[1]. I don't see the output files, though? Could you post a link to them or send me an email? Thanks again.

— Reply to this email directly or view it on GitHub[2].

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Nicholas J. Gotelli Office Phone: 802-656-0450 Department of Biology Lab Phone: 802-656-0451
University of Vermont Fax: 802-656-2914 Burlington, VT 05405 e-mail: ngotelli@uvm.edu

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Home Page (with manuscript pdfs): http://www.uvm.edu/~ngotelli/homepage.html

Musician's Corner (with mp3s): http://www.uvm.edu/~ngotelli/musicpage/music.html

NEW: EcoSimR (free software for null model analysis): http://www.uvm.edu/~ngotelli/EcoSim/EcoSim.html

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Links:

[1] https://github.com/ngotelli [2] https://github.com/GotelliLab/EcoSimR/issues/54#issuecomment-78520922

File: m.txt Species: 20 Sites: 100 MatFill: 0.10 Occ: 192 5.00% Confidence limit Model: fixed - fixed Index File In SimIn StdDevIn Z-In StIndex SkewIn LowerCLIn UpperCLIn Checkerboard m.txt 132 131.60 2.55 0.16 0.0030 0.37 127.00 138.00 Combinations m.txt 1 0.00 0.00 1.00 0.0000 0.00 0.00 0.00 C-Score m.txt 0.006 0.005 0.000 4.313 0.1383 0.301 0.005 0.006 Soerensen m.txt 0.0543 0.0508 0.0022 1.5911 0.0687 0.2453 0.0466 0.0554 Absences m.txt 6461.147 6450.124 4.759 2.316 0.0017 0.058 6445.653 6455.643 BR m.txt 80 82.54 1.71 -1.49 0.4167 -0.20 79.00 86.00 Correlation m.txt 0.5253 0.5012 0.0062 3.8636 0.0480 0.1531 0.4895 0.5138 Variancetest m.txt 2.2818 2.2818 0.0031 0.0009 0.0000 0.0000 2.2818 2.2818

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File: m.txt Species: 20 Sites: 100 MatFill: 0.10 Occ: 192 5.00% Confidence limit Model: fixed - fixed

No Score ObsNumber ExpNumber StDevExp Skewness LowerCL UpperCL Z-Score OddsRMean OddsRCL 1 0.000 14.000 10.830 1.562 -0.211 8.000 14.000 2.029 0.226 0.000 2 0.025 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 3 0.075 0.000 0.410 0.492 0.377 0.000 1.000 -0.834 0.000 0.000 4 0.125 2.000 2.000 1.095 0.141 0.000 4.000 0.000 0.000 0.000 5 0.175 1.000 2.030 1.081 0.721 0.000 5.000 -0.953 0.000 0.000 6 0.225 0.000 1.510 0.985 0.295 0.000 4.000 -1.533 0.000 0.000 7 0.275 1.000 0.820 0.805 0.941 0.000 3.000 0.224 0.180 0.000 8 0.325 0.000 1.360 1.100 1.042 0.000 4.000 -1.236 0.000 0.000 9 0.375 0.000 1.380 1.147 0.345 0.000 4.000 -1.203 0.000 0.000 10 0.425 4.000 3.530 1.506 0.583 1.000 7.000 0.312 0.118 0.000 11 0.475 1.000 1.920 1.230 0.290 0.000 5.000 -0.748 0.000 0.000 12 0.525 4.000 3.070 1.485 0.405 1.000 6.000 0.626 0.233 0.000 13 0.575 4.000 4.680 1.827 0.503 2.000 9.000 -0.372 0.000 0.000 14 0.625 5.000 6.270 2.019 0.527 3.000 11.000 -0.629 0.000 0.000 15 0.675 4.000 6.470 2.090 0.294 2.000 11.000 -1.182 0.000 0.000 16 0.725 8.000 6.900 2.071 0.416 3.000 11.000 0.531 0.137 0.000 17 0.775 10.000 5.200 2.035 0.564 2.000 10.000 2.359 0.480 0.000 18 0.825 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 19 0.875 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 20 0.925 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 21 0.975 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 22 1.000 132.000 131.620 2.440 0.075 127.000 136.000 0.156 0.003 0.000

No Sp1 Sp2 S1 S2 Com Obs.Score Exp.Score Exp.StDev Skewness LowerCL UpperCL Z-Score Alpha >MeanScore >CLScore >BJScore Alpha 1 sp20 sp11 93 10 8 0.183 0.095 0.083 0.280 0.000 0.277 1.06 0.28918046 0.00 0.00 0.00 1.00000000 2 sp20 sp3 93 8 7 0.116 0.093 0.093 1.217 0.000 0.355 0.24 0.80798608 0.00 0.00 0.00 1.00000000 3 sp20 sp4 93 8 7 0.116 0.099 0.106 1.218 0.000 0.355 0.16 0.87680042 0.00 0.00 0.00 1.00000000 4 sp20 sp18 93 7 0 1.000 0.083 0.101 1.104 0.000 0.270 9.10 0.00000000 9.10 0.00 9.10 0.00000000 5 sp20 sp19 93 7 0 1.000 0.112 0.107 1.004 0.000 0.410 8.30 0.00000000 0.00 0.00 8.30 0.00000000 6 sp20 sp9 93 7 7 0.000 0.117 0.121 0.827 0.000 0.410 -0.97 0.33208853 0.00 0.00 0.00 1.00000000 7 sp20 sp14 93 6 6 0.000 0.103 0.118 1.026 0.000 0.319 -0.87 0.38243902 0.00 0.00 0.00 1.00000000 8 sp20 sp10 93 6 6 0.000 0.076 0.107 1.119 0.000 0.319 -0.71 0.47535962 0.00 0.00 0.00 1.00000000 9 sp20 sp5 93 5 5 0.000 0.104 0.141 1.345 0.000 0.387 -0.74 0.45997190 0.00 0.00 0.00 1.00000000 10 sp20 sp6 93 5 5 0.000 0.113 0.131 0.766 0.000 0.387 -0.87 0.38688040 0.00 0.00 0.00 1.00000000 11 sp20 sp15 93 5 5 0.000 0.087 0.126 1.445 0.000 0.387 -0.69 0.49319541 0.00 0.00 0.00 1.00000000 12 sp20 sp7 93 4 4 0.000 0.104 0.139 0.971 0.000 0.489 -0.75 0.45174241 0.00 0.00 0.00 1.00000000 13 sp20 sp1 93 4 4 0.000 0.095 0.137 1.162 0.000 0.489 -0.69 0.49007499 0.00 0.00 0.00 1.00000000 14 sp20 sp2 93 4 4 0.000 0.124 0.148 0.807 0.000 0.489 -0.84 0.40298373 0.00 0.00 0.00 1.00000000 15 sp20 sp13 93 3 3 0.000 0.114 0.176 1.257 0.000 0.659 -0.65 0.51470387 0.00 0.00 0.00 1.00000000 16 sp20 sp16 93 3 3 0.000 0.091 0.146 1.010 0.000 0.326 -0.62 0.53287095 0.00 0.00 0.00 1.00000000 17 sp20 sp17 93 3 3 0.000 0.141 0.198 1.139 0.000 0.659 -0.71 0.47781467 0.00 0.00 0.00 1.00000000 18 sp20 sp8 93 2 1 0.495 0.094 0.194 1.629 0.000 0.495 2.06 0.03862685 0.00 0.00 0.00 1.00000000 19 sp20 sp12 93 2 2 0.000 0.109 0.228 2.028 0.000 0.495 -0.48 0.63303506 0.00 0.00 0.00 1.00000000 20 sp11 sp3 10 8 1 0.788 0.846 0.156 -0.564 0.600 1.000 -0.38 0.70738900 0.00 0.00 0.00 1.00000000 21 sp11 sp4 10 8 3 0.438 0.839 0.166 -0.733 0.438 1.000 -2.42 0.01549155 0.00 0.00 0.00 1.00000000 22 sp11 sp18 10 7 2 0.571 0.860 0.153 -0.568 0.571 1.000 -1.88 0.05984974 0.00 0.00 0.00 1.00000000 23 sp11 sp19 10 7 2 0.571 0.841 0.161 -0.600 0.571 1.000 -1.68 0.09328717 0.00 0.00 0.00 1.00000000 24 sp11 sp9 10 7 1 0.771 0.837 0.166 -0.849 0.400 1.000 -0.40 0.69217157 0.00 0.00 0.00 1.00000000 25 sp11 sp14 10 6 1 0.750 0.845 0.167 -0.747 0.533 1.000 -0.57 0.57151860 0.00 0.00 0.00 1.00000000 26 sp11 sp10 10 6 1 0.750 0.848 0.155 -0.435 0.533 1.000 -0.63 0.52695560 0.00 0.00 0.00 1.00000000 27 sp11 sp5 10 5 0 1.000 0.850 0.179 -0.717 0.480 1.000 0.84 0.40154290 0.00 0.00 0.00 1.00000000 28 sp11 sp6 10 5 1 0.720 0.881 0.176 -1.260 0.480 1.000 -0.91 0.36158502 0.00 0.00 0.00 1.00000000 29 sp11 sp15 10 5 0 1.000 0.822 0.179 -0.416 0.480 1.000 0.99 0.32006794 0.00 0.00 0.00 1.00000000 30 sp11 sp7 10 4 0 1.000 0.891 0.164 -1.044 0.675 1.000 0.67 0.50432682 0.00 0.00 0.00 1.00000000 31 sp11 sp1 10 4 0 1.000 0.886 0.184 -1.335 0.400 1.000 0.62 0.53611898 0.00 0.00 0.00 1.00000000 32 sp11 sp2 10 4 0 1.000 0.877 0.193 -1.407 0.400 1.000 0.64 0.52448940 0.00 0.00 0.00 1.00000000 33 sp11 sp13 10 3 0 1.000 0.871 0.217 -1.422 0.267 1.000 0.59 0.55340439 0.00 0.00 0.00 1.00000000 34 sp11 sp16 10 3 0 1.000 0.907 0.196 -1.979 0.267 1.000 0.48 0.63386410 0.00 0.00 0.00 1.00000000 35 sp11 sp17 10 3 0 1.000 0.873 0.199 -1.150 0.600 1.000 0.64 0.52395564 0.00 0.00 0.00 1.00000000 36 sp11 sp8 10 2 0 1.000 0.929 0.196 -2.729 0.450 1.000 0.36 0.71868348 0.00 0.00 0.00 1.00000000 37 sp11 sp12 10 2 0 1.000 0.973 0.120 -4.256 0.450 1.000 0.23 0.81854486 0.00 0.00 0.00 1.00000000 38 sp3 sp4 8 8 1 0.766 0.843 0.179 -0.832 0.391 1.000 -0.44 0.66350645 0.00 0.00 0.00 1.00000000 39 sp3 sp18 8 7 1 0.750 0.866 0.176 -0.940 0.536 1.000 -0.66 0.51101506 0.00 0.00 0.00 1.00000000 40 sp3 sp19 8 7 1 0.750 0.882 0.166 -1.228 0.536 1.000 -0.80 0.42625356 0.00 0.00 0.00 1.00000000 41 sp3 sp9 8 7 1 0.750 0.862 0.167 -0.840 0.536 1.000 -0.67 0.50222284 0.00 0.00 0.00 1.00000000 42 sp3 sp14 8 6 1 0.729 0.875 0.158 -0.807 0.500 1.000 -0.92 0.35657346 0.00 0.00 0.00 1.00000000 43 sp3 sp10 8 6 0 1.000 0.871 0.169 -1.129 0.500 1.000 0.76 0.44720387 0.00 0.00 0.00 1.00000000 44 sp3 sp5 8 5 0 1.000 0.892 0.149 -0.781 0.700 1.000 0.72 0.46926945 0.00 0.00 0.00 1.00000000 45 sp3 sp6 8 5 1 0.700 0.842 0.183 -0.637 0.450 1.000 -0.78 0.43738425 0.00 0.00 0.00 1.00000000 46 sp3 sp15 8 5 0 1.00 — Reply to this email directly or view it on GitHub https://github.com/GotelliLab/EcoSimR/issues/54#issuecomment-78548563.

[davharris]

Sorry to bother you again, @ngotelli. It looks like Github truncated the pairwise matrix after only 46 of the 190 pairs. Maybe email would work better?

Thanks again for looking into this for me.

[menarest]

@davharris In the end, did you manage to check and implement a pairwise analysis correctly?