[R-sig-eco] relative compositional dissimilarity among sites against a continuous variable

[Jari Oksanen]

On 24/11/10 23:48 PM, "Pekin, Burak K" <bpekin at purdue.edu> wrote:

> Thanks Andy, that's a simple enough calculation. So the larger the average
> dissimilarity, the more the site is dissimilar in its composition to other
> sites. I could then do a regression between the average dissimilarity of sites
> and other site factors and say something like 'compositional dissimilarity
> increases as factor X increases'.
Burak,

If this is really what you need, remember that diagonal elements of the
dissimilarity matrix are zeros (dissimilarity between the object and
itself). This means that the row or column mean of the dissimilarity to
*other* sites is underestimated. You should either make the diagonal to NA:

d <- as.matrix(vegdist(x))
diag(d) <- NA
rowMeans(d, na.rm = TRUE)

or adjust the means for one zero element:

d <- as.matrix(vegdist(x))
rowMeans(d)*(nrow(d)/(nrow(d)-1))

This does not influence the ranks, but it doesn't hurt if your numbers are
correct.


> 
> Is there a method that regresses the relative compositional dissimilarity
> among sites against a continuous variable?
>

The mean dissimilarity is found for your observations, so it is of the same
length as your data vectors. You can use functions like lm(), glm(), nls(),
gam() or some hundred other alternatives in packages to analyse the results.

Cheers, Jari Oksanen
 
> -Burak
> 
> From: Andy Rominger [mailto:ajrominger at gmail.com]
> Sent: Tuesday, November 23, 2010 4:11 PM
> To: Steve_Friedman at nps.gov
> Cc: Pekin, Burak K; r-sig-ecology-bounces at r-project.org;
> r-sig-ecology at r-project.org
> Subject: Re: [R-sig-eco] Dissimilarity ranking
> 
> or perhaps could you use something as simple as the mean dissimilarity?  i'm
> thinking something like this:
> 
> library(vegan)
> 
> ##    some data to play with, 20 sites by 30 spp
> data(dune)
> 
> ##    calculate distance, making it a matrix
> dune.dist <- as.matrix(vegdist(dune,method=
> "bray"))
> 
> ##    take average distance
> dune.avg <- apply(dune.dist,1,mean)
> dune.avg
> 
> not sure what the gurus of dissimilarity analysis might think of this.
> good luck,
> andy
> 
> On Tue, Nov 23, 2010 at 5:43 PM,
> <Steve_Friedman at nps.gov<mailto:Steve_Friedman at nps.gov>> wrote:
> Burak
> 
> You should explore the use of non-metric multidimensional scaling.
> 
> ?cmdscale
> 
> Steve Friedman Ph. D.
> Ecologist  / Spatial Statistical Analyst
> Everglades and Dry Tortugas National Park
> 950 N Krome Ave (3rd Floor)
> Homestead, Florida 33034
> 
> Steve_Friedman at nps.gov<mailto:Steve_Friedman at nps.gov>
> Office (305) 224 - 4282
> Fax     (305) 224 - 4147
> 
> 
> 
>             "Pekin, Burak K"
>             <bpekin at purdue.ed
>             u>                                                         To
>             Sent by:
> "'r-sig-ecology at r-project.org<mailto:r-sig-ecology at r-project.org>'"
>             r-sig-ecology-bou
> <r-sig-ecology at r-project.org<mailto:r-sig-ecology at r-project.org>>
>             nces at r-project.or<mailto:nces at r-project.or>
> cc
>             g
>                                                                   Subject
>                                       [R-sig-eco] Dissimilarity ranking
>             11/23/2010 03:09
>             PM
> 
> 
> 
> 
> 
> 
> 
> 
> Hello, I want to rank the dissimilarity of sites based on their species
> composition. For example, I would like to be able to say that site A is
> less similar in composition to the other sites than site B is similar to
> the other sites. I could do a cluster analysis and look at which sites are
> less closely clustered.
> 
> It would be even better if I could come up with a quantitative scale rather
> than a relative ranking that would give a value for each site based on its
> relative dissimilarity to the rest of the sites. So site A might receive a
> 90 out of 100, whereas site B and C might receive a 60 and a 50 indicating
> the rank as well as 'relative quantity' of dissimilarity for each site.
> 
> Thanks,
> Burak
> 
> --------------------------
> 
> Burak K. Pekin, PhD
> Postdoctoral Research Associate
> Purdue University
> 
> 
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