[R-sig-eco] relative compositional dissimilarity among sites against a continuous variable
Jari Oksanen
jari.oksanen at oulu.fi
Wed Nov 24 23:43:19 CET 2010
On 25/11/10 00:37 AM, "Pekin, Burak K" <bpekin at purdue.edu> wrote:
> Thanks Jari, so in essence I would be doing something like a PERMANOVA.
Burak,
Very vaguely similar (some people would say "no, not at all"). If you want
to do something like PERMANOVA and do it in R (instead of using Marti
Anderson's PERMANOVA standalone software binaries in Windows), you can use
adonis() of vegan which does this like it should be done.
Cheers, Jari Oksanen
>
> -----Original Message-----
> From: Jari Oksanen [mailto:jari.oksanen at oulu.fi]
> Sent: Wednesday, November 24, 2010 5:35 PM
> To: Pekin, Burak K; 'Andy Rominger'
> Cc: 'r-sig-ecology at r-project.org'
> Subject: Re: [R-sig-eco] relative compositional dissimilarity among sites
> against a continuous variable
>
> 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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>>
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