quantifying directed dependence of environmental factors

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.

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



On Thu, Mar 7, 2013 at 6:50 AM, Jay Kerns <gjkernsysu at gmail.com> wrote:

Dear Sarah,

On Thu, Mar 7, 2013 at 9:32 AM, Sarah Goslee <sarah.goslee at gmail.com>
wrote:

That sounds like a job for path analysis or for structural equation
modeling, depending on the level of sophistication desired and the
hypotheses to be tested.

*Yes!*  I said almost the exact same thing (I didn't say anything
about Path Analysis because I don't know much about it), but I had it
in my mind that SEM was targeted more to sociological things and
didn't know if/that it was common in ecological contexts.  Anyway,
it's nice to hear that word coming from somebody else.

There are plenty of good resources for both, in and out of R.

Indeed.  I have some work to do.  Thank you.

--
Jay