On 10/10/2011 02:15 PM, Gavin Simpson wrote:
On Mon, 2011-10-10 at 09:11 -0700, Rich Shepard wrote:
On Mon, 10 Oct 2011, Dave Roberts wrote:
I want to compare the results of the two sampling exercises in order to
test the performance of the two sampling techniques.
I would try something pretty direct. Any appeal to differences in
dissimilarities confounds the effects with the particular
dissimilarity/distance matrix you use. Assuming the samples and species
are in the same order, and that the data.frames are the same size, you
might try
I did not read the original message, so I hope you'll allow me to join the
thread. My recommendation is to use univariate tree models, particularly a
classification tree (for ordinal explanatory variables; i.e., ST1 and ST2).
But the response here is *multivariate* - of course, one could use Glen
De'Ath's multivariate regression trees (despite the name it is really a
constrained clustering/classification) - but I think there are better
ways of solving this particular problem. And unless one has many 100s of
observations, the model will need some sort of variance reduction
applied (via bagging, or some such) as the one fitted model is
potentially highly unstable.
G
This is fully, carefully, and non-technically explained in Chapter 9
(particularly Sections 9.3 and 9.4) in Zuur, Ieno, and Smith "Analysing
Ecological Data." For that matter, I highly recommend reading the whole
book.
Rich
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It would be fairly simple to boil down to a univariate question. You
could do something as simple as a paired t-test of plot-level species
richness or the number of individuals sampled (to compare sampling
efficiency), but I still don't see an independent and a dependent variable.
Dave
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