Re: [R-sig-eco] help with analysis

[Dave Roberts]

Hi Kátia,

   Sorry to be so late; just back in the office.

   Pedro is correct that adonis will help establish statistical 
significance to potential differences, but I think NMDS could still be 
very helpful.  One approach would be to code the sites by glyph (e.g. 
site 1 = circle, site 2 = triangle, etc.) and then to draw arrows from 
the first date to the second, second to third, etc, for each site.


   In labdsv you could do this if your nmds is called nmds.object

> plot(nmds.object,type=n')

to draw the axes but not the points, and then use

> points(nmds.object,site==1,pch=1)
> points(nmds.object,site==2,pch=2)

etc.  Then, depending on how the sites are sorted, the arrows could be drawn

> 
arrows(nmds.object$points[1,1],nmds.object$points[1,2],nmds.object$points[2,1],

  nmds.object$points[2,2])

to draw an arrow form the first point to the second.  YOu might need to 
mess with the parameters of arrows() to get the arrow sizes you want.


   In vegan you could do

> nmds.plot <- plot(metaMDS(dissimilarity or taxon matrix),type='n')
> points(nmds.plot$sites[site==1])
> points(nmds.plot$sites[site==2],pch=2)
etc
> arrows(nmds.plot$sites[1,1],nmds.plot$sites[1,2],
   nmds.plot$sites[2,1],nmds.plot$sites[2,2])

etc.

Hope that helps, Dave
--

David W. Roberts office 406-994-4548
Professor and Head  FAX 406-994-3190
Department of Ecology email drobe...@montana.edu
Montana State University
Bozeman, MT 59717-3460

On 08/08/2011 08:20 AM, Kátia Emidio wrote:

Dear all,
I have a data from inventory of plants in 6 fragmented forests of the same
size,randomly selected, measured during 3 periods of time, and I'd like to
know about changes in species composition over time. It is a good idea to
use NMDS for each period of time
and then to compare the changes in the ordination diagram?? What kind of
analysis could be better??
Cheers,
Katia

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[R-sig-eco] random effects in a linear mixed model

[Elizabeth Green]

On Aug 19, 2011, at 2:00 AM, r-sig-ecology-requ...@r-project.org wrote:



  1. Random effects in a linear mix model (Edgar Torres)



Edgar, the random effects represent the variable that has an effect,  
but not an effect that is likely to be biologically interesting, in  
this case, your fishing vessel. The effects you want to test are the  
fixed effects, in this case time, because you want to see how your  
variable varies by year, accounting for the random effects of the  
different vessels fishing.


bessie


Bessie Green
 907-388-4496
http://mercury.bio.uaf.edu/~elizabeth_green/

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