Hi Manuel,

As Prof. Jari Oksanen anwered about the metaMDS, permit me comment about the
usage (or not usage) of NMDS method to reduce the dimensionality of your
landscape metrics. I think that your are planning keep the firsts dimensions
for other analyses. Case yes, and in the case you use the axis on
regression, if need to be care because the axis 1, 2 etc are not ortogonal,
and the results could be a little bit difficult of explain. There are three
papers very interesting on this regard of choice (or reduction of the
number) of landscape metrics:

Griffith, J.A., Martinko, E.A., Price, K.P., 2000. Landscape structure
analysis of Kansas at three scales. Landscape and Urban Planning 52, 45-61.

Neel, M.C., McGarigal, K., Cushman, S.A., 2004. Behaviour of class-level
landscape metrics across gradients of class aggregation and area. Landscape
Ecology 19, 435-455.

Cushman, S.A.; McGarigal, K.; Neel, M.C. 2008. Parsimony in landscape
metrics: strength, universality and consistency. Ecological Indicators,
8:691-703.

We use them as good reference on some of our studies. Good luck on your
research!

Saludos,

Miltinho



2008/6/11, Manuel Spínola <mspinola10@gmail.com>:
>
> Dear list,
>
> I am trying to run a NMDS in vegan.  I am using standardized variables, so
> I have negative values.  This is my output:
>
> > metricas =read.table("metricas_std.txt", header=TRUE)
> > attach(metricas)
>
>       The following object(s) are masked from metricas ( position 3 ) :
>
>        LPI MNN MSI NUMP
>
> > metricas
>     NUMP   LPI   MSI   MNN
> 1945 -1.72  0.10 -1.20 -1.03
> 1960  0.83  0.62 -0.90 -1.13
> 1980  0.55  1.27  0.12  0.45
> 1997  0.09 -1.00  0.90  0.97
> 2005  0.24 -1.00  0.72  0.72
> > metricas.dis <- vegdist(metricas, "euclidean")
> > metricas.dis
>         1945      1960      1980      1997
> 1960 2.6216216                             1980 3.2333574 2.0094029
>           1997 3.5911140 3.2896808 2.4986596         2005 3.4351856
> 3.0032316 2.3836736 0.3426368
> > metamds = metaMDS(metricas, distance="euclidean")
> Using step-across dissimilarities:
> Too long or NA distances: 9 out of 10 (90.0%)
> Stepping across 10 dissimilarities...
> Error in isoMDS(dist, k = k, trace = isotrace) :
>  an initial configuration must be supplied with NA/Infs in 'd'
> In addition: Warning message:
> In stepacross(dis, trace = trace, ...) :
>  Disconnected data: Result will contain NAs
>
> Do you know what is going on?
> Thank you very much in advance.
> Best,
>
> Manuel Spínola
>
> --
> Manuel Spínola, Ph.D.
> Instituto Internacional en Conservación y Manejo de Vida Silvestre
> Universidad Nacional
> Apartado 1350-3000
> Heredia
> COSTA RICA
> mspinola@una.ac.cr
> Teléfono: 277-3598
> Fax: 237-7036
>
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