non-metric MDS comparison - vegan vs. ecodist packages

On Mon, 2012-02-20 at 14:30 -0500, Sarah Goslee wrote:

References are given in the help for nmds. In particular, nmds() uses
Kruskal's formulation.

And you can read the code. Look at nmds, at the top of the function body
is an inline function sstress() which does the relevant computation,
IIUC.

Sarah
On Feb 20, 2012 1:48 PM, "Gian Maria Niccol Benucci" <
gian.benucci at gmail.com> wrote:

Dear Gavin and Sarah,

I've found how metaMDS() calculate the stress [using monoMDS() of vegan],
but I do not find how nmds() of ecodist do it so. Could you please link me
the page or where I can find it, maybe it escapes me...
Thank you very much for your aid,

Gian


2012/2/20 Gavin Simpson <gavin.simpson at ucl.ac.uk>

On Mon, 2012-02-20 at 17:24 +0100, Gian Maria Niccol Benucci wrote:

Dear Gavin,

Thank you very much. I tried with your advice but the result is almost
unchanged...

Any further advice??

Yeah, what Sarah said. Read the documentation; the two functions use
different definitions of stress.

You could always do a procrustes rotation on the two configurations to
see how well they compare.

G

Gian




2012/2/20 Gavin Simpson <gavin.simpson at ucl.ac.uk>

On Mon, 2012-02-20 at 16:28 +0100, Gian Maria Niccol Benucci wrote:

Hi r-sig-ecology Members,

I would please you to examine the differences I found in metaMDS()

and

nmds() outputs and possibly draw some conclusions. As I wrote in

past

emails I have 24 samples of ectomycorrhizal fungi grouped into two
different communities (12 natural and 12 cultivated).

here is the code:

metaMDS(data_matrix2, distance="bray", k=2, autotransform=F) ->

metaMDS

metaMDS

Call:
metaMDS(comm = data_matrix2, distance = "bray", k = 2,

autotransform

= F)

global Multidimensional Scaling using monoMDS

Data:     data_matrix2
Distance: bray

Dimensions: 2
*Stress:     0.04536661 *
Stress type 1, weak ties
No convergent solutions - best solution after 20 tries

Perhaps you could try running metaMDS with random starts until it

does

find convergent solutions?! Why expect metaMDS() to do better in 20
tries than nmds() with 100 random tries?

add `trymax = 100` and `halfchange = FALSE` to try to make the two
functions more comparable.

HTH

G

Scaling: centring, PC rotation, halfchange scaling
Species: expanded scores based on data_matrix2

nmds <- nmds(dist_bray, mindim = 2, maxdim = 2, nits = 100)

Using random start configuration
Using random start configuration
...

nmds_min

           X1          X2
1  -0.66226262  0.16392824
2 -0.68844987 -0.20891993
3 -0.47717515  0.55323693
4 -0.67213392 -0.09195847
5 0.03305172  0.41844232
6   0.31992006  0.53887910
7  -0.61944875  0.34411146
8 0.07013849  0.65723057
9 0.53053436  0.37444633
10  0.26704705  0.39849341
11 -0.20981416  0.70391983
12 -0.61620355 -0.43568743
13 0.14219660 -0.69571194
14 0.42365981 -0.43308767
15 0.15840330 -0.12872863
16 -0.00492407 -0.06889680
17 -0.20513513 -0.58052156
18 0.46546214  0.12019775
19 0.36753709 -0.25048256
20 -0.09446486 -0.61943616
21  0.15010553 -0.31995009
22  0.41540872 -0.29507200
23  0.42573367 -0.12321584
24  0.48081354 -0.02121688

min(nmds$stress)

[*1] 0.2787161*

nmds$r2[which.min(nmds$stress)]

[1] 0.6338372

Is it possible I got so different stress values??

Thanks for replying,

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