Hi!

Thank you for such an elegant toolbox!

Unfortunately I’m experiencing some error when trying to proceed with third part of tutorial concerning Network Based Statistics (Raw Conn Matrix and Network Based Statistics). I’m setting ep everything as shown in tutorial pdf but some kind of error is happening (please see below) and results that I get are different from those presented in pdf. Here I am using matlab 2016b.

I tried running GraphVar on different machine and on different version of matlab (2015b) but same error occurs…

Seeing that error might be connected to parallel toolbox I tried running analyses with and without parallel workers specified but effect is the same in both situations.

Do you have any idea what might be going on here?

Johann Kruschwitz

Fri, 12/02/2016 - 12:03

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## Bugfix continuous by continous interactions

thanks a lot for noticing the discrepancy between the tutorial and the actual output. After I put these slides together we did some small changes on the GLM model with respect to standardizing the regression weights (in the tutorial they are still unstandardized). These changes led to a bug in the case of continuous by continous interactions where in some models the direction of the effect was reversed. Importantly, this did not affect the p-values of the regressors.

dorgar_at_rest

Fri, 12/02/2016 - 17:35

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## Thank you very much for swift reply and fast bug fixes!

Unfortunately I’m still getting very different results after correction using Random Networks/Groups(Bonferroni) option (tutorial example) with every recalculation of results. Although I’m not sure if this is some more general issue or has something to do with error that I mentioned earlier which is still occurring (my previous post). Do you have any insight into this problem?

Johann Kruschwitz

Mon, 12/05/2016 - 12:35

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## more permutations needed

Hi,

as you are doing "permutation-testing" the p-values are directly derived from the null-distribution. The null-distribution can be "inaccurate" if there are not enough samples (i.e., permutations) and the outcome can differ. In this case with the data from the sample workspace it seems as 1000 permutations are not enough for reliable estimation of effects. Thus, you simply need more permutations to be more accurate.

This is mainly the reason why we put effort in implementing the permutations in C-code that ultimately allows for larg-scale and high-repetition permutation testing.

I just tried 20.000 permutations with the example from the tutorial and the outcome is nearly 100% identical when re-running the analysis multiple times (e.g. have a look at the non-parametric p-values via "Random Networks/Groups". It can be the case that single cells are at the border of signigicance with 0.051 and the next time 0.050 ... but this is how "random" permutations work.

dorgar_at_rest

Tue, 12/20/2016 - 22:30

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## one more concern

Hi,

Thank you for your response and highlighting some important aspects of permutation testing. The reason I was warried (being familiar with permutation testing) is that I thought that calculations should be close to the tutorial examples.

I have one more question regarding tutorial data/examples. I followed tutorial dealing with “Within design II (change of efficiency and behavior T1 T2)” but the result that I keep getting is again somehow reversed (maybe it is a problem connected to the previous one concerning reversed betas?):

Also I would greatly appreciate if you could confirm what is the correct method of entering within factor (within ID). Tutorial shows that “file identifier” has to be selected in the window appearing after loading variables from the file. But now there is no such option. So I managed to choose Subj ID (tutorial data) as “within ID” by entering into Scan ID into within covariance window. Is it the right way to do it now?

I would really appreciate clearing these issues.

wwwlea

Fri, 12/23/2016 - 10:51

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## Looks good!

Dear user,

thank you for alerting us to this issue. The error we corrected earlier this month does not affect simple linear regression. In fact, I believe the screenshot in the tutorial shows mismatched results. We likely used different data when we made the screenshots, and forgot to update them before we uploaded the new GraphVar version. We will update the tutorial with corrected images soon!

Furthermore, I have verified that your result is correct by manually calculating the values for transitivity and beer_pong_score and then running the linear regression using only the MATLAB corr function. This yields the same results as shown in your screenshot.

Concerning your second issue, it actually happens sometimes on my computer as well that the checkboxes for the "file identifier" are missing. This is a MATLAB issue that we have been unable to solve. However, if the field is missing, GraphVar automatically assumes that the "file identifier" is the first column of the variable sheet, so you can work around the issue by arranging your data this way. I'll keep you posted if we ever find a way around it!

Thank you for using GraphVar!

Best regards,

Lea Waller

dorgar_at_rest

Wed, 01/18/2017 - 01:23

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## Thank you!

Thank you very much for clarification!

As for tutorials and documentation I think that it would be indeed a good idea to update them as for now these are unfortunately somewhat misleading.

Thank you very much for your great work on this very handy toolbox!