Hi Gavin and Hi all,
I will not go in front of a bus for sure, I not mad, at least I am not still
mad... :)
I would like to tell you that I am a Ph.D. student, and for what I know,
Ph.D. student still have to understand things studing those from whom wrote
before them...
Isac Newton became famous not only for his science but also for a famous
phrase that, if I don't remember it bad, act like this :" If I have seen so
much far away is because I stand on shoulders of Giants"... I think that it
needs any comment, and express itself the concept...
So, I am so sorry, I also don't like the "me to" attitude, but you don't
know how is my reality here, and I can assure you that also If I am still a
"student", I am alone in my research, and If have a tutor and boss for
italian rules I don't have a boss for statistics, couse none could help me
on that...
So what could I do if I don't take models in already published literature?
Anyway, I don't want to seem like the victim, I have a brain that works and
I am doing my best to understand and improve my knowledge and at least lean
and grow, for sure, step by step, and with a big humility, in science and in
this case in statistics...
Anyway... For continuing the brainstorm if I can...The Host effect is what I
think is more interesting for the ecological point of view of my trials also
becasue the 4 communities have two by two the same host, I mean A and B,
Corylus, while B and C, Ostrya...
If I plot the factors of the envifit into the graph and the evidence of
separation seems clear...
That's are my metaMDS with 2 and 3 dimensions:
> NMS.1
Call:
metaMDS(comm = sqrtABCD, distance = "bray", k = 2, trymax = 100,
autotransform = F)
Nonmetric Multidimensional Scaling using isoMDS (MASS package)
Data: sqrtABCD
Distance: bray shortest
Dimensions: 2
Stress: 24.54342
Two convergent solutions found after 18 tries
Scaling: centring, PC rotation, halfchange scaling
Species: expanded scores based on sqrtABCD
> NMS.ABCD.2ef
***FACTORS:
Centroids:
NMDS1 NMDS2
CommunityA -0.3271 0.1984
CommunityB -0.1956 0.1768
CommunityC 0.2520 -0.2847
CommunityD 0.2706 -0.0905
HostCorylus -0.2613 0.1876
HostOstrya 0.2613 -0.1876
Goodness of fit:
r2 Pr(>r)
Community 0.1897 0.017982 *
Host 0.1778 0.001998 **
---
Signif. codes: 0 *** 0.001 ** 0.01 * 0.05 . 0.1 1
P values based on 1000 permutations.
> NMS.1.3
Call:
metaMDS(comm = sqrtABCD, distance = "bray", k = 3, trymax = 100,
autotransform = F)
Nonmetric Multidimensional Scaling using isoMDS (MASS package)
Data: sqrtABCD
Distance: bray shortest
Dimensions: 3
Stress: 16.29226
Two convergent solutions found after 6 tries
Scaling: centring, PC rotation, halfchange scaling
Species: expanded scores based on sqrtABCD
> NMS.ABCD.3ef
***FACTORS:
Centroids:
NMDS1 NMDS2 NMDS3
CommunityA 0.3881 -0.2702 0.1536
CommunityB 0.1407 -0.2344 0.0197
CommunityC -0.2053 0.3566 -0.0219
CommunityD -0.3235 0.1480 -0.1514
HostCorylus 0.2644 -0.2523 0.0866
HostOstrya -0.2644 0.2523 -0.0866
Goodness of fit:
r2 Pr(>r)
Community 0.1798 0.005994 **
Host 0.1581 0.000999 ***
---
Signif. codes: 0 *** 0.001 ** 0.01 * 0.05 . 0.1 1
P values based on 1000 permutations.
>
I got 10 sample units for each community data (40 in total)
You said at the end : "*I do wonder if you are not hitting the curse of
dimensionality here?"*
Can you explain me what do you mean for "hitting the curse of
dimensionality" if I am not so demanding...
... and then: *"it would be nice to look at the ordination but how you do
that I don't know".*
I would be glad if you see the graphs of my ordinations, Can I send them to
you? That would be great... let me know about that. I used to plot in this
way:
> plot(NMS.1, type="n", dis= "sp")
> ordisymbol(NMS.1, env.table, "Host", legend=T)
Anyway I have to admit that with 2 and at least 3 dimensions the points into
the ordinantion plot are better separated in reasons to the data matrix, so
what to do? better fittind of points ant bigger stress or the contrary?
I think is enough, thank you so much for your help, I'll appreciate any
comments! :)
Gian
> And thank you all for the kind responses...
> >
> > I do not want to torture myself for sure... :) I red (lot of)
> publications
> > about fungal community ecology studies (soil fungi), my research field
> > indeed, and all uses NMDS or DCA as ordination techniques... So, I am
> only
> > trying to do my best useing R for calculating them...
>
> Would you walk in front of a bus if you saw lots of other people doing
> it? I doubt it. This kind of "me to" attitude to science is quite
> demoralising when reviewing manuscripts and reading the literature.
>
> DCA was invented to solve a specific problem with CA - namely the arch
> artefact. I forget whether this is in Jari's public lecture notes, vegan
> vignettes/tutorials or in one of his lectures on a course we taught
> together, but DCA replaces the arch artefact with other artefacts that
> make the points look like a trumpet or a diamond in ordination space.
>
> Why DCA is used as a default instead of a special case escapes me. You
> really shouldn't use DCA at all if you can get away with it as it is
> doing some nasty things to your data.
>
> Alternatives; i) NMDS ii) PCA after application of a transformation
> (Legendre & Gallagher 2001, Oecologia). And there are probably others...
>
> >
> > What I need now is a good environmental interpretation of my work...
> >
> > Then I found the fantastic Jari's pdf about "Multivariate Analysis of
> > Ecological Communities in R: vegan tutorial" and I went to the passage
> about
> > factors and vectors fitting...
> > That's my R code:
> >
> > > NMS.ABCDsqrt
> >
> > Call:
> > metaMDS(comm = sqrtABCD, distance = "bray", k = 4, trymax = 100,
> > autotransform = F)
> >
> > Nonmetric Multidimensional Scaling using isoMDS (MASS package)
> >
> > Data: sqrtABCD
> > Distance: bray shortest
> >
> > Dimensions: 4
> > Stress: 11.68632
>
> What was the stress with k = 2 and k = 3. As Jari has already mentioned,
> how are you going to interpret and visualise this 4D configuration of
> points (you can't plot NMDS1 vs NMDS2, NMDS1 vs NMDS3 etc. for reasons
> explained to you earlier in this thread).
>
> > Two convergent solutions found after 2 tries
> > Scaling: centring, PC rotation, halfchange scaling
> > Species: expanded scores based on sqrtABCD
> >
> > > envfit(NMS.ABCDsqrt, env.table, permu=1000) ->NMS.ABCDsqrtef
> > > NMS.ABCDsqrtef
> >
> > ***FACTORS:
> >
> > Centroids:
> > NMDS1 NMDS2 NMDS3 NMDS4
> > CommunityA -0.3821 0.3822 -0.1173 -0.1232
> > CommunityB -0.1849 0.2748 0.0076 -0.0720
> > CommunityC 0.2206 -0.4261 -0.0505 0.1197
> > CommunityD 0.3465 -0.2310 0.1603 0.0756
> > HostCorylus -0.2835 0.3285 -0.0549 -0.0976
> > HostOstrya 0.2835 -0.3285 0.0549 0.0976
> >
> > Goodness of fit:
> > r2 Pr(>r)
> > Community 0.2009 0.001998 **
> > Host 0.1818 0.000999 ***
> > ---
> > Signif. codes: 0 *** 0.001 ** 0.01 * 0.05 . 0.1 1
> > P values based on 1000 permutations.
> >
> > > names(NMS.ABCDsqrtef)
> > [1] "vectors" "factors"
> > > NMS.ABCDsqrtef$vectors
> > NULL
> > >
> >
> > I have an enviromental matrix (env.table) that contains only area
> (A,B,C,D)
> > and tree species (Corylus sp. and Ostrya sp. ) differentiation, so I have
> > the area and the tree species of each samples is related to...
> > I could plot factors on the graph but not vectors because there aren't
> > vectors in reason to the absence of numerical data in the env. matrix...
> > isn't it right? Aren't the R2 values too low?
>
> Did you read ?envfit ? It states:
> ....
> (r^2). For factors this is defined as r^2 = 1 - ss_w/ss_t, where
> ss_w and ss_t are within-group and total sums of squares.
>
> So this statistic here is looking at how constrained within the 4D space
> the levels of each factor are in relation to the overall spread of the
> points. This looks to me like some evidence for grouping of your sites
> on basis of Community and stronger evidence for Host. The "effect" is
> small but significant. I do wonder if you are not hitting the curse of
> dimensionality here?
>
> The interpretation will depend on the number of samples. It would be
> nice to look at the ordination but how you do that I don't know.
>
> G
>
> >
> > Many many thank you for answering...
> >
> > Gian
> >
> >
> >
> >
> >
> >
> >
> >
> >
> >
> > > Jari,
> > > >
> > > > I am here again ... :)
> > > > So, to try having a comparison of the real goodness of my metaMDS
> data I
> > > > tried to perform a DCA (with same input table)
> > > > Then please forgive me if I do somethign wrong with it... That's my R
> > > code:
> > >
> > > Why DCA? What lead you to torture your data so?
> > >
> > > > >decorana(sqrtABCD, iweigh=0, ira=0) -> DCA.1
> > > > > DCA.1
> > > >
> > > > Call:
> > > > decorana(veg = sqrtABCD, iweigh = 0, ira = 0)
> > > >
> > > > Detrended correspondence analysis with 26 segments.
> > > > Rescaling of axes with 4 iterations.
> > > >
> > > > DCA1 DCA2 DCA3 DCA4
> > > > Eigenvalues 0.6688 0.5387 0.4822 0.3752
> > > > Decorana values 0.7912 0.5795 0.4145 0.2931
> > > > Axis lengths 5.9974 3.7036 3.6121 3.3802
> > > >
> > > > >
> > > >
> > > > In that situation the graph is still good but the differences between
> the
> > > > two clades are little more confused, maybe in the axe II (I mean the
> > > > vertical one) in this case there is a better separation.
> > > > What do the "Decorana values" really mean?
> > >
> > > ?decorana
> > >
> > > Basically, in the original DECORANA code the Eigenvalues reported were
> > > computed at the wrong stage of the "detrending" processes. Jari
> realised
> > > this when interfacing the old DECORANA code with R. Jari altered the
> > > code to compute the correct Eigenvalues, but chose to also report the
> > > values you'd get from DECORANA or Canoco to stop people complaining
> that
> > > vegan was doing DCA incorrectly.
> > >
> > > > And how about the segments?
> > >
> > > What about them? Do you know how DCA works? The standard detrending
> > > breaks the first (D)CA axis into 26 sequential chunks or segements. the
> > > 26 is the default, but it can be changed. Within each chunk, the mean
> > > trial site score for axis 2 for sites in that chunk is subtracted from
> > > the trial axis 2 site scores of the sites in the chunk. This detrending
> > > is what gets rid of the "arch" found in some CA plots and is the reason
> > > DCA was invented.
> > >
> > > >
> > > > How can I do something better?
> > >
> > > Are you trying to separate the two clades? Do you know a priori which
> > > samples belong to which clade? If so, one of the many classification
> > > methods in R would be more useful as they look to separate the a priori
> > > defined groups best. The methods you have been using thus far aim to
> > > represent the dissimilarities between samples best in a low dimensional
> > > space.
> > >
> > > HTH
> > >
> > > G
> > >
> > > >
> > > > Many thank you in advance,
> > > >
> > > > G.
> > >
> >
> > [[alternative HTML version deleted]]
> >
> > _______________________________________________
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> --
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> Dr. Gavin Simpson [t] +44 (0)20 7679 0522
> ECRC, UCL Geography, [f] +44 (0)20 7679 0565
> Pearson Building, [e] gavin.simpsonATNOSPAMucl.ac.uk
> Gower Street, London [w]
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>
>
>
--
Dr. Gian Maria Niccolò Benucci
Department of Applied Biology - University of Perugia
Borgo XX Giugno, 74
I-06121 - Perugia, ITALY
Email: gian.benu...@gmail.com
Tel: +39.075.5856433
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