-
Notifications
You must be signed in to change notification settings - Fork 18
New issue
Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.
By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.
Already on GitHub? Sign in to your account
log vs arithmetic differences and curve CI's #165
Comments
Hey Niv, Thank you so much for your very nice email. I am really glad that you are finding the package useful. You're questions are great and very natural. Do you mind if I post your questions and my reply on github (https://github.com/MoBiodiv/mobr/issues) ?
Let me know if you still have questions and if you feel ok having this conversation publicly on github where my coauthors and others can more easily chime in. Also we can tag PR's to your questions during future development. |
Hi Dan,
I understand that my suggestions are probably not on the top of your priority list, but maybe they will be in the future... Anyway thanks for building this package best, P.S. You can post any part of this discussion on github... |
Sorry that was a bit vague about our theoretical construct it is simply that changes in species richness between two communities are driven by changes in either the SAD, numbers of individuals (N), or spatial patchiness, and that these components can be additively partitioning out of differences in species richness. The individual based and non-spatial based rarefaction curves (i.e., curves used to derive N and SAD effects) are analytically derived based on the mean expected value. CI's have been derived by other authors and we could build those into the package but as of yet we have no plans for this. The spatial rarefaction curve is deterministic (not stochastic) with the exception that we average over all possible starting plots. Also we have a draft of a paper describing the package circulating with coauthors that we hope to submit soon. If you would like I'm happy to send you. Dan |
@dmcglinn thanks for responding to the question. It's super awesome to hear that other folks are using our package (and finding it helpful)! Totally made my day. I think you've already provided great answers, but I have an idea regarding Niv's first point, which could be very easy to implement (without even changing our code maybe). Imagine at a particular scale the treatment has 18 species while the control has 20. So the treatment reduces S by 2, or 10% (which I think is what Niv meant by log-difference). Our analysis could show that 1 of the loss species is due to aggregation, -7 is due to SAD (ie SAD actually is more even in treatment), 8 is due to N. As you said, the framework is completely additive, and we have 2 = 1 + (-7) + 8. Now, if someone like Niv wants to know the log-difference, wouldn't it be the same as setting the control as the baseline, and directly compare those delta-S values to the control? ie. instead of talking about absolute numbers we could say aggregation reduces S by 5%, SAD increases S by 35%, and N reduces S by 40%, and the net change is 10%. Does that make sense or am I over-simplifying the question? |
Great idea! this is what I was looking for... |
hey @rueuntal that does seem like a really simple elegant solution! That is pretty similar to what our proportional stacked bar plots already do but I think it is worth implementing. I'll try to carve out time to add this week. |
Sounds great @dmcglinn thanks! |
This comment was submitted to me from Niv de Malach [email protected]. Below I have also posted my replies each email as its own entry.
The text was updated successfully, but these errors were encountered: