Okey, I understood...
I have a matrix of 40 rows (samples) and 29 columns (species). In the
ordination graph the data divide in two clades ( as i supposed they must
to)... and that's my best solution for reduce the Stress...

> metaMDS(sqrtABCD, distance = "bray", k = 23, trymax = 50, autotransform
=F) -> NMS.trial
>NMS.trial

Call:
metaMDS(comm = sqrtABCD, distance = "bray", k = 23, trymax = 100,
autotransform = F)

Nonmetric Multidimensional Scaling using isoMDS (MASS package)

Data:     sqrtABCD
Distance: bray shortest

Dimensions: 23
Stress:     0.2548688
Two convergent solutions found after 8 tries
Scaling: centring, PC rotation, halfchange scaling
Species: expanded scores based on ‘sqrtABCD’

With more than 23 dimensions R gave me that result:

> metaMDS(sqrtABCD, distance = "bray", k = 30, trymax = 50,
Using step-across dissimilarities:
Too long or NA distances: 230 out of 780 (29.5%)
Stepping across 780 dissimilarities...
Errore in isoMDS(dist, k = k, trace = isotrace) :
  initial configuration must be complete
Inoltre: Warning messages:
1: In cmdscale(d, k) : some of the first 30 eigenvalues are < 0
2: In sqrt(ev) : Si è prodotto un NaN
>

...Is normal I got better ordination (sepatation of different samples, that
I know they're different) with few dimension also if the Stress is high?

... I supposed, that If we use as many dimensions as there are variables,
then we can perfectly reproduce the observed distance matrix. Isn't it? But,
of course, our goal is to reduce the observed complexity of nature, that is,
to explain the distance matrix in terms of fewer underlying dimensions...
So what is best at the end??
And also wich is the function for plotting the stress values versus the
number of dimnsions and how to read the plot?
I hope I was clear, thank you so much!
Yours,

G.

	[[alternative HTML version deleted]]

