Dear Mario

Just adding up to the previous suggestions made by Etienne and Tyler:

A paper by Sandrine Pavoine et al. published last year in Oikos

(Pavoine S et al. 2009. On the challenge of treating various types of
variables: application for improving the measurement of functional
diversity. Oikos, 118: 391-402) probably contaings the answers to what you
looking for.

In this paper you“ll find a framework allowing you to use different types of
data (fuzzy, continuous, categorical etc) with a genralization of Gower's
distance (as well how to treat missing data and weight your traits).

The good news is that they were kind enough to provide a step-by-step
example on how to use their approach and the respective R-codes.

Hope you find it helpful!

All the best
Marcus



On Tue, Dec 21, 2010 at 10:14 PM, Etienne Laliberte <
etiennelaliberte@gmail.com> wrote:

> Dear Mario,
>
> For the Gower dissimilarity you may want to check
>
> Library(FD)
> ?gowdis
>
> which allows weights to be assigned to traits/variables (sometimes useful),
> and implements Podani's 1999 approach for ordinal traits/variables.
>
> Cheers
>
> Etienne
>
>
> -----Original Message-----
> From: r-sig-ecology-bounces@r-project.org
> [mailto:r-sig-ecology-bounces@r-project.org] On Behalf Of Tyler Smith
> Sent: Wednesday, 22 December 2010 1:31 AM
> To: r-sig-ecology@r-project.org
> Subject: Re: [R-sig-eco] ordination and clustering with continous and
> categorical variables
>
> Mario Brusadin <mario.brusadin@gmail.com>
> writes:
>
> > I would like to perform a simple ordination on a dataset of species
> > traits, contaning both continous and categorical variables, as well as
> > a cluster analysis on the same dataset. ... I would possibly like to
> > overlay the results of the cluster analysis results onto an ordination
> > plot.
> >
> > ... This function can be set to use the Gower dissimilarity
> > coefficient (1971), to create a distance matrix, from a dataset that
> > contains both continous and categorical variables. Is the best option
> > available?
>
> I'm not sure if it's the best option, but it is certainly reasonable.
>
> > As for the ordination, I am not entirely sure about which ordination
> > method which I should use. Is Correspondence Analysis suitable or
> > would it be better to use Principal coordinates analysis?
>
> Correspondence analysis implicitly preserves the Chi-Square distance
> between individuals, which I don't think is what you want. Since you'll
> be using the Gower distance to create a distance matrix, you can use
> either principal coordinates analysis (via the cmdscale() function), or
> non-metric multidimensional scaling (NMDS, available in the metaMDS()
> function in the vegan package).
>
> If you want the 'best' possible two-dimensional representation of your
> distance matrix to plot on paper, then NMDS is probably better. The
> vegan functions env.fit and ordisurf are very useful for plotting the
> original variables onto your ordination plots. You can use the results
> of the clustering analysis to alter the shape/color of the plotted
> points (using the col or cex arguments to plot() ) in order to 'overlay'
> the clusters on the ordination.
>
> > My aim is to identify, species niche-breadth using the ordination and
> > identify any clusters of similar species, with the cluster analysis.
>
> The above suggestions will give you a potentially useful graphical
> summary of your data. I'm not sure how you'd extract niche-breadth from
> this. Do you have multiple measurements per species? If you've got a
> single summary value for each trait per species, what you'll be plotting
> is the relative niche position of each species, rather than their niche
> breadths.
>
> Regards,
>
> Tyler
>
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-- 
-----------------------------------------------------------------------
Prof. Dr. Marcus Cianciaruso
Department of Ecology
Universidade Federal de Goiįs
http://sites.google.com/site/cianciaruso/

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