quantifying directed dependence of environmental factors
Hello,
On Thu, Mar 7, 2013 at 2:20 PM, Sarah Goslee <sarah.goslee at gmail.com> wrote:
Hi,
I'm not sure how one would combine SEM / graphical models with compositional dissimilarity as a response. You might be able to fit a series of models in adonis() or capscale(), comparing just direct factors to direct + intermediate, etc.. I don't have any good ideas on how you might test more complex causal structures.
Tom: thanks for chiming in. If I understand you correctly, your idea is similar to my colleague's first thought: he was asking about a sort of nested ANOVA/model approach (he didn't call it that, but that was what he was getting at).
There's a fair bit of literature on Mantel-based path analysis, and other similar dissimilarity-based approaches. SEM can be used with composition as well, although not (I think) with the intermediate step of calculating dissimilarities. Besides journal articles employing those techniques, I like both of these: J. B. Grace, Structural Equation Modeling and Natural Systems, Cambridge University Press, Cambridge, UK, 2006. B. Shipley, Cause and Correlation in Biology: A User?s Guide to Path Analysis, Structural Equations and Causal Inference, Cambridge University Press, Cambridge, UK, 2000.
Sarah: thank you again! I will definitely check these out.
Given that you are dealing with diatoms across space (with environmental measurements) and down time (in cores, often without environmental measures), there may be an alternate approach possible based on calibration approaches to inferred environments (e.g., WACAL) or modern analogs. I would look at packages bio.infer, paltran, fossil, and analogue, and search to see if anyone has pushed them in the direction you want to go.
Tom: many, many thanks. I have not used any of those packages before. I will investigate every single one. Jay