Gian,,

I applaud your continued struggle to understand the best route of analysis.
 If I were you, I would ignore the rude comments made by some and focus on
the constructive replies from others.  Good luck!

Chris Habeck
Ph.D. Candidate
Department of Zoology
University of Wisconsin, Madison
http://habeckecology.wikispaces.com/


2009/12/7 Gian Maria Niccolò Benucci <gian.benucci@gmail.com>

> 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]]
> > >
> > > _______________________________________________
> > > R-sig-ecology mailing list
> > > R-sig-ecology@r-project.org
> > > https://stat.ethz.ch/mailman/listinfo/r-sig-ecology
> > --
> > %~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%
> >  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] http://www.ucl.ac.uk/~ucfagls/<
> http://www.ucl.ac.uk/%7Eucfagls/>
> >  UK. WC1E 6BT.                 [w] http://www.freshwaters.org.uk
> > %~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%
> >
> >
> >
>
>
> --
> Dr. Gian Maria Niccolò Benucci
> Department of Applied Biology - University of Perugia
> Borgo XX Giugno, 74
> I-06121 - Perugia, ITALY
> Email: gian.benucci@gmail.com
> Tel: +39.075.5856433
>
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