teixeirak
commented
5 years ago @mcgregorian1, I don't think we're so far off with the models. We haven't been graphing response curves, but we have been paying attention to the coefficients all along, and they've been consistent (except for the ones thrown out because we had too many many variables in the model).
I think its worth revisiting the model options within 2 dAIC of the ones you've identified as top to see whether there are other candidate models that should be considered, particularly in our testing of H3.2. Predictor variables varied across droughts. Make a table containing all models withing 2 dAIC of the top model, which can go into the SI. We can then reference this table in the results and discussion sections.
mcgregorian1
Hi @teixeirak
Regarding the model averaging, we also asked Valentine about this and she mentioned that she wasn’t sure about using it because she doesn’t have experience with it, but she knows of it as a tactic.
That being said, Valentine was stressing the importance of not disregarding models that are <2 dAIC from the top model. Currently what I’ve been doing (and Becky?) is that we run the covariates against each other, and determine the top model from that with a dAIC of 0.00. Valentine was mentioning that I should really also be looking at the AIC weight of the different models (which are tied to the dAIC), and noting that I shouldn't be ignoring any models that are within the range of 2 dAIC from the top model. She mentioned that it's not best to always be looking only at the model parsing through averages or otherwise, and instead that it would be good to be looking at the response curves for each covariate against the response variable (what Becky is doing?), plus potentially looking at the variance. This way, I should be focusing more on explaining and fully understanding what each model is saying for myself, instead of letting the numbers dictate my understanding.
She also mentioned that having my marginal and conditional r2 values be close together is encouraging (meaning that the covariates are indeed responsible for variation), even if the conditional (overall) values are low.
I recognize that what Valentine is saying is important, and I put it down to my lack of stats background that I didn't realize this would be more of an issue to get around from the start, in terms of making sure I fully understood the models and being able to describe the response curves. I'm fine attempting to do this next week seeing as it's something that needs to get done in order to throw out the models that are within 2 dAIC, but I also am cognizant of my reality. Before I leave I have 7 work days, 2 of which will be doing dendrobands. That leaves me 5 days for the following
After that, there are two scenarios:
I will do the best I can to get done what I can in my time left, but I wanted to give you a heads-up given that I don't expect to have much time once I officially start in Raleigh. Sorry I didn't realize this would be an issue earlier.