[R-sig-eco] Ranked abundance distribution

[Sheri O'Connor]

Hi Sol,

I am assuming that your samples are stratigraphic? You might want to
check out the rioja package
http://cran.r-project.org/web/packages/rioja/index.html for
constrained hierarchical clustering (chclust function) by
stratigraphic sequence.

Another idea is biclustering using the method discussed in this paper:
http://fossil.earthsci.carleton.ca/~tpatters/pubs2/2005/boudreau2005jpaleolimn33_445-461.pdf
which you can calculate and display with modifications to this:
http://stats.stackexchange.com/questions/12580/interpretation-of-two-way-clustering-in-r

Other methods in the Boudreau paper might be useful to test the
significance of your samples and species.

HTH,
Sheri

On Tue, Dec 17, 2013 at 10:01 AM, Sol Noetinger <s.noetinger at gmail.com> wrote:
> Hello,
>
> I am trying to apply different statistics methods in a field that traditionally is not very keen to it… and in consequence I am trying to learn all that I can.
> To the point, I am studying a palynological succession from the Devonian. I have counts of palynomorphs (around 250) from a set of 17 samples. I use the relative abundance to standardise the counting since there are some samples that did have not enough specimens.
> I have tested cluster analysis with different packages, resulting in two clear groups. I tested the abundance distribution on both groups to see which model fits better.
>
> This is the summary:
>
> Cluster I
> RAD models, family poisson
> No. of species 24, total abundance 100
>
>            par1     par2    par3   Deviance AIC     BIC
> Null                               55.2189      Inf     Inf
> Preemption    0.1                  85.4721      Inf     Inf
> Lognormal   0.20534  1.6811         8.0522      Inf     Inf
> Zipf        0.42497 -1.4264         1.4461      Inf     Inf
> Mandelbrot  1.4285  -1.8885      1  3.4265      Inf     Inf
>
> Cluster II
> RAD models, family poisson
> No. of species 35, total abundance 100
>
>            par1     par2    par3   Deviance AIC     BIC
> Null                               25.7004      Inf     Inf
> Preemption    0.1                  27.8760      Inf     Inf
> Lognormal   0.21756  1.3473         4.7797      Inf     Inf
> Zipf        0.27724 -1.0959         4.9038      Inf     Inf
> Mandelbrot  0.64175 -1.3825      1  4.9181      Inf     Inf
>
> I read from the manual that to see which models fits better you use the AIC values.
> What is the meaning of getting "infinite"?
> Can I use the Deviance value to compare the models?
> And in case I can use the deviance, since there are very close values, should I run a test to see if the differences are significant?… in that case, which  one?.
>
> I apologise if my questions are too basic, or if I should refer to a different kind of forum or thread.
> I hope you can help me, thank you for your time,
> Regards,
>
> Sol
>
>
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>
>
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