# [R] Abundance data ordination in R

José Rafael Ferrer Paris jr_frrr at yahoo.de
Sun Apr 1 20:40:13 CEST 2007

There are many ways to do this, really. For example if you use
constrained (~ canonical) correspondence analysis the distance measure
between sites is Chi-square and  absences are not informative to the
analysis. Or you can use an ecological distance measure (similarity
indices like Soerensen, Bray-Curtis, Jaccard, and others) and perform
principal coordinates (=multidimensional scaling), etc. Read the
documentation and tutorials for the packages vegan, ade4 and labdsv.

You might start your search at the page of Jari Oksanen:
http://cc.oulu.fi/~jarioksa/softhelp/vegan.html
or the one from Dave Roberts
http://ecology.msu.montana.edu/labdsv/R/
. The vegan tutorial was useful for me to learn to use vegan:
http://cc.oulu.fi/~jarioksa/opetus/metodi/vegantutor.pdf
If you need more indeep mathemathical details, you should take a look at
Daniel Chessels site:
http://pbil.univ-lyon1.fr/R/perso/pagechessel.html
suited for beginners, others require more background knowledge) . Be
warned: there is a large variety of techniques for multivariate analysis
with different properties and weaknesses, sometimes the most popular
analysis are not the most appropriate. Be sure of what you want and what
you are doing before you perform the analysis, the interpretation will
depend on the techniques applied.

I personally find ade4 implements many different techniques but is
poorly documented and some functionalities are somehow "hidden", while
getting started. I haven't used labdsv yet.

hope this help

JR

El dom, 01-04-2007 a las 09:20 -0700, Milton Cezar Ribeiro escribió:
> Dear R-gurus
>
> I have a data.frame with abundance data for species and sites which looks like:
> mydf<-data.frame(
>  sp1=sample(0:10,5,replace=T),
>  sp2=sample(0:20,5,replace=T),
>  sp3=sample(0:4,5,replace=T),
>  sp4=sample(0:2,5,replace=T))
> rownames(mydf)<-paste("sites",1:5,sep="")
>
> I would like make an ordination analysis of these data and my worries is about the "zeros" (absence of species) into the matrix. Up to I read (Gotelli - A primir of ecological statistics, 2004), when I have abundance data I cant compute Euclidian Distances because the zeros have the meaning of absence of the species and not as zero counting. Gotelli suggests one make "principal coordinates analysis". I would like to here from you what you think about and what is the best packages and functions to I compute my distance matrices and do my ordination analysis. Can I considere zero as NA on my data.frame? Is there a good PDF book available about Multivariate Analysis for abundance data available on the web?
>
> Kind regards
>
> Miltinho
> Brazil
>
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