[R-sig-eco] quantifying directed dependence of environmental factors

[Jay Kerns]

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