Re: [R] Abundance data ordination in R

From: Philippe Grosjean <phgrosjean_at_sciviews.org>
Date: Mon 02 Apr 2007 - 08:48:41 GMT

Gavin Simpson wrote:

> On Sun, 2007-04-01 at 09:20 -0700, Milton Cezar Ribeiro wrote:

>> 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?
> 
> In addition to the other suggestions, there is a Task View on CRAN for
> the topic of Environmetrics. This has a section describing the various
> ordination techniques available in R as well as functions to calculate
> distance/dissimilarity matrices:
> 
> http://cran.r-project.org/src/contrib/Views/Environmetrics.html
> 
> G

... And here are a couple of other suggestions:

  1. Use a distance that does not take couples of zero as information. Typically, the Bray-Curtis distance is one of the most commonly used in such a case.
  2. Possibly transform your data first, depending on the relative importance you want to give to rare species (typically, a log, or double square root transformations increase importance of rare species relative to abundant ones).
  3. One approach is to use MultiDimensional Scaling (see MASS package) on the distance matrix to make the ordination in two or three dimensions. See the Venables & Ripley's MASS book for details.
  4. Another alternative is to use correspondence analysis, which uses the Chi2 distance and is adapted to abundances (it is designed to analyze contingency tables, but table of abundances, station versus species, could be considered as such a double entry contingency table in a way).

Best,

Philippe Grosjean

>> Kind regards
>>
>> Miltinho
>> Brazil
>>
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https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code. Received on Mon Apr 02 18:58:23 2007

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