Hi everyone,
I was struggling for a while with performing a permutation/crossvalidation test of nonlinear PCA in order to assess the significance of the contribution of the separate variables to the nonlinearPCA solutions. Does anyone have an idea on how/package to perform this. For PCA maybe?
Any hint would be much appreciated.
 
Dragos



Dragos Zaharescu
Animal Anatomy Laboratory
Faculty of Biological Sciences
Vigo University, apd. 137
36310, Vigo (Pontevedra), SPAIN
zaha_dragos@yahoo.com
zdragos@uvigo.es
http://webs.uvigo.es/zdragos/

~ You should be the change you want to see in the world ~ Ghandi




________________________________
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To: r-sig-ecology@r-project.org
Sent: Thu, December 17, 2009 12:00:01 PM
Subject: R-sig-ecology Digest, Vol 21, Issue 16

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Today's Topics:

  1. Re: dissimilarity and species turnover (Michael Denslow)
  2. Re: low predicted vales in GAMs (Anna Renwick) (Anna Renwick)
  3. Re: vegan package: goodness.cca function? (citations needed)
      (Jari Oksanen)
  4. Re: Fwd: Fwd: how to calculate "axis variance" in    metaMDS,
      pakage vegan? (Gian Maria Niccol? Benucci)
  5. Re: dissimilarity and species turnover (Etienne Lalibert?)


----------------------------------------------------------------------

Message: 1
Date: Wed, 16 Dec 2009 06:00:13 -0500
From: Michael Denslow <michael.denslow@gmail.com>
Subject: Re: [R-sig-eco] dissimilarity and species turnover
To: Jari Oksanen <jari.oksanen@oulu.fi>
Cc: Amanda Stanley <amanda@appliedeco.org>,
    r-sig-ecology@r-project.org
Message-ID:
    <b440a3f80912160300y4a81edefi66ec857930d9e24a@mail.gmail.com>
Content-Type: text/plain; charset=ISO-8859-1

Hi Everyone,

On Tue, Dec 15, 2009 at 12:21 PM, Jari Oksanen <jari.oksanen@oulu.fi> wrote:
> On 15/12/09 18:54 PM, "Amanda Stanley" <amanda@appliedeco.org> wrote:
>
>> Robert,
>> check out package ncf by Ottar Bjornstad
>> http://cran.r-project.org/web/packages/ncf/index.html.
>> The spline correlogram function might be what you need. ?From the
>> documentation:
>>
>> "The spline (cross-)correlogram differes from the spatial correlogram (and
>> Mantel correlogram) in
>> that it estimated spatial dependence as a continous functions of distance
>> (rather than binning into
>> distance classes)."
>>
> Howdy,
>
> Actually, the binning is not *the* problem (and basic Mantel test does not
> use binning at all: it is only for the correlograms). The problem is
> partitioning *distances* into *additive* components like is implicitly done
> when you have something like partial Mantel tests.
>
> What you can do for a starter is to go the October archive of R-sig-eco
> which has two threads on the very same issues ("using two distance metrices
> in formula", "Mantel test with skew-symmetric matrices?") The staring
> questions were not exactly identical to this question, but the discussion
> soon radiated to relevant issues. I'd recommend you check Sarah Goslee's
> comments at the minimum. If you want to go deeper here (and you should if
> you are serious), dig up the late 2008 issue of the Ecology with the
> Legendre & mates vs. Tuomisto discussion -- somewhere around pages 3230 to
> 3256 of vol 89).

I think that the discussion that Jari is referring to is posted here:
http://www.bio.umontreal.ca/legendre/reprints/Beta_diversity_comments_2008.pdf

[[elided Yahoo spam]]

Michael

>
> That's for the starter.
>
> Cheers, Jari Oksanen
>
>> I've been exploring using this approach for a similar problem. I'd be
>> curious to know the opinions of this group if it appropriately deals with
>> the issues surrounding Mantel tests.
>>
>> --Amanda Stanley
>>
>> ***
>> Amanda G. Stanley, Ph.D.
>> Project Director
>>
>> Institute for Applied Ecology
>> P.O. Box 2855
>> Corvallis, OR 97339-2855
>> (Phone)541-753-3099 x133
>> (Fax)541-753-3098
>>
>> amanda@appliedeco.org
>> www.appliedeco.org
>>
>> From: Robert Ptacnik <ptacnik@icbm.de>
>> To: r-sig-ecology@r-project.org
>> Date: Mon, 14 Dec 2009 17:26:39 +0100
>> Subject: [R-sig-eco] dissimilarity and species turnover
>> Hi,
>> mantel statistics has repeatedly been criticized. I wonder if there is an
>> (approved) alternative for my problem:
>> I aim to test whether one parameter (productivity, P) affects turnover (t)
>> among ecological communities in time (T) or space (S). ?(?T and ?S will be
>> used as a co-variables).
>> To avoid confusion - I do NOT aim to test whether P affects composition as
>> such (which could be tested by an ordination method), but whether the degree
>> of similarity among samples scales with P.
>> ?My approach so far was to calculate a dissimilarity matrix from my
>> community data, distance matrices for the relevant environmental data (?T,
>> ?S, ?P) and a mean (P) matrix, giving the mean(P) for each pair of
>> observations.
>> The I performed mantel tests whether t correlates with mean(P), taking other
>> variables into account (partialing out). However, mantel and especially
>> partialing out are often criticized (e.g. see documentation in vegan).
>> any views?
>> thanks!
>> Robert
>>
>>
>> Robert Ptacnik, PhD
>>
>> ICBM, Univ. of Oldenburg
>> Schleusenstrasse 1, DE-26382 Wilhelmshaven
>> http://www.icbm.de/planktologie/en/
>>
>> ptacnik@icbm.de
>>
>> --
>>
>> [[alternative HTML version deleted]]
>>
>> _______________________________________________
>> R-sig-ecology mailing list
>> R-sig-ecology@r-project.org
>> https://stat.ethz.ch/mailman/listinfo/r-sig-ecology
>
> _______________________________________________
> R-sig-ecology mailing list
> R-sig-ecology@r-project.org
> https://stat.ethz.ch/mailman/listinfo/r-sig-ecology
>



-- 
Michael Denslow

Graduate Student & Adjunct Instructor
I.W. Carpenter Jr. Herbarium [BOON]
Department of Biology
Appalachian State University
Boone, North Carolina U.S.A.
-- AND --
Communications Manager
Southeast Regional Network of Expertise and Collections
sernec.org

36.214177, -81.681480 +/- 3103 meters



------------------------------

Message: 2
Date: Wed, 16 Dec 2009 12:25:55 -0000
From: "Anna Renwick" <anna.renwick@bto.org>
Subject: Re: [R-sig-eco] low predicted vales in GAMs (Anna Renwick)
To: <r-sig-ecology@r-project.org>
Message-ID: <2A014E6628B441DB91661BE6ECE28209@btodomain.bto.org>
Content-Type: text/plain;    charset="us-ascii"

Dear All

I wanted to thank everyone for their helpful comments. With your help, and
that of Simon Wood, I now realise that the reason I have low predicted
values is because I have so many zeros in my data. As the model structure I
have constructed specifies that the mean must always be positive then the
model over-predicts the zero counts and in order not to predict more counts
that there actually are it under-estimates the non zeros counts (this
underestimation can be quite large due to the high number of zeros).


So one thing I am thinking of is to try a zero-inflated model. I have looked
at the COZIGAM package but you do not seem to be able use an offset with it.
I was wondering if anybody knows of a package where weighted zero-inflated
GAM models with an offset can be run.

Many thanks,

Anna

Dr Anna R. Renwick
Research Ecologist
British Trust for Ornithology, 
The Nunnery, 
Thetford, 
Norfolk, 
IP24 2PU, 
UK
Tel: +44 (0)1842 750050; Fax: +44 (0)1842 750030 

-----Original Message-----
From: r-sig-ecology-bounces@r-project.org
[mailto:r-sig-ecology-bounces@r-project.org] On Behalf Of Highland
Statistics Ltd.
Sent: 12 December 2009 11:28
To: r-sig-ecology@r-project.org
Subject: Re: [R-sig-eco] low predicted vales in GAMs (Anna Renwick)


> ----------------------------------------------------------------------
>
> Message: 1
> Date: Fri, 11 Dec 2009 11:43:40 -0000
> From: "Anna Renwick" <anna.renwick@bto.org>
> Subject: [R-sig-eco] low predicted vales in GAMs
> To: <r-sig-ecology@r-project.org>
> Message-ID: <BFD6DF2C5CA142C58C272652FA017856@btodomain.bto.org>
> Content-Type: text/plain
>
> Dear All
>
>  
>
> I have come across a problem with the GAM models I am running. Basically
the
> predicted values are consistently only about 0.4 of the actual values. 
>
>  
>
> A bit more detail:
>
> MODEL:
>
>
m4<-gam(count~s(east,north,k=10)+ez+cv01+cv03+cv04+cv05+cv07+mtemp+mtotalrai
> n+ez:mtemp+ez:mtotalrain+
>
>            offset(log(fit.vec)),
>
>            weights=wt,
>
>            data=spat6,
>
>            family=quasipoisson,
>
>            start=rep(0,26)
>
> )
>
> MODEL SUMMARY:
>
>  
>
> Family: quasipoisson 
>
> Link function: log 
>
>  
>
> Formula:
>
> count ~ s(east, north, k = 10) + ez + cv01 + cv03 + cv04 + cv05 + 
>
>    cv07 + mtemp + mtotalrain + ez:mtemp + ez:mtotalrain +
> offset(log(fit.vec))
>
>  
>
> Parametric coefficients:
>
>                  Estimate Std. Error  t value Pr(>|t|)    
>
> (Intercept)    -5.296e+00  1.846e+00    -2.869 0.004166 ** 
>
> ezM            1.651e+00  2.102e+00    0.785 0.432397    
>
> ezP            7.358e+00  2.047e+00    3.595 0.000332 ***
>
> ezU            -1.061e+02  1.064e+07 -9.97e-06 0.999992    
>
> cv01            7.405e-02  5.437e-03    13.620  < 2e-16 ***
>
> cv03            2.258e-02  5.145e-03    4.389 1.20e-05 ***
>
> cv04            2.878e-02  4.839e-03    5.949 3.18e-09 ***
>
> cv05            3.634e-02  5.326e-03    6.823 1.17e-11 ***
>
> cv07            2.370e-02  5.712e-03    4.149 3.48e-05 ***
>
> mtemp          -1.838e-01  1.750e-01    -1.050 0.293900    
>
> mtotalrain      1.872e-02  5.072e-03    3.692 0.000229 ***
>
> ezM:mtemp      6.181e-02  2.204e-01    0.280 0.779197    
>
> ezP:mtemp      -7.028e-01  2.050e-01    -3.429 0.000619 ***
>
> ezU:mtemp      8.697e-01  1.371e+06  6.34e-07 0.999999    
>
> ezM:mtotalrain -3.393e-02  5.799e-03    -5.851 5.68e-09 ***
>
> ezP:mtotalrain -1.901e-02  5.379e-03    -3.535 0.000417 ***
>
> ezU:mtotalrain  3.510e-02  4.074e+04  8.62e-07 0.999999    
>
> ---
>
> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 
>
>  
>
> Approximate significance of smooth terms:
>
>                edf Ref.df    F p-value    
>
> s(east,north) 8.736  8.736 28.88  <2e-16 ***
>
> ---
>
> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 
>
>  
>
> R-sq.(adj) =  0.324  Deviance explained = -5.12e+03%
>
> GCV score = 39.556  Scale est. = 39.056    n = 2038
>
>  
>
>  
>
> Count = bird counts/square
>
>  

Is this really an integer?


> ez=environmental zone
>
> cv = habitat types
>
> mtemp = mean annual temperature
>
> mtotalrain= mean total rain/year
>
>  
>
> Sample size is approximately 2000.
>
>  
>
> The offset fit.vec is bird detectability and the weighting is based on the
> number of squares in each area surveyed. I belief that the strange
deviance
> explained is due to the weighting we have added into the model.
>
>  
Why would you use a weighting factor in a Poisson/quasi-Poisson GLM/GAM? 
See also the weights text for the help file for glm. Not sure what it 
would be doing.

>  
>
> I would have assumed that the predicted values divided by the real counts
> should be around 1, however they are much lower and hence the model is
> consistently predicting lower counts than were observed. I was wondering
if
> there is anything obvious which I am missing when carrying out these
models.
>
>  

you seem to have a very large overdispersion. But that is another 
problem. I think your number of squares should actually be used in the 
offset (the log obviously).

Alain

>  
>
> Many thanks,
>
> Anna
>
>  
>
> Dr Anna R. Renwick
> Research Ecologist
> British Trust for Ornithology, 
> The Nunnery, 
> Thetford, 
> Norfolk, 
> IP24 2PU, 
> UK
> Tel: +44 (0)1842 750050; Fax: +44 (0)1842 750030 
>
>  
>
>
>     [[alternative HTML version deleted]]
>
>
>
> ------------------------------
>
> _______________________________________________
> R-sig-ecology mailing list
> R-sig-ecology@r-project.org
> https://stat.ethz.ch/mailman/listinfo/r-sig-ecology
>
>
> End of R-sig-ecology Digest, Vol 21, Issue 12
> *********************************************
>
>  


-- 


Dr. Alain F. Zuur
First author of:

1. Analysing Ecological Data (2007).
Zuur, AF, Ieno, EN and Smith, GM. Springer. 680 p.
URL: www.springer.com/0-387-45967-7


2. Mixed effects models and extensions in ecology with R. (2009).
Zuur, AF, Ieno, EN, Walker, N, Saveliev, AA, and Smith, GM. Springer.
http://www.springer.com/life+sci/ecology/book/978-0-387-87457-9


3. A Beginner's Guide to R (2009).
Zuur, AF, Ieno, EN, Meesters, EHWG. Springer
http://www.springer.com/statistics/computational/book/978-0-387-93836-3


Other books: http://www.highstat.com/books.htm


Statistical consultancy, courses, data analysis and software
Highland Statistics Ltd.
6 Laverock road
UK - AB41 6FN Newburgh
Tel: 0044 1358 788177
Email: highstat@highstat.com
URL: www.highstat.com
URL: www.brodgar.com

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------------------------------

Message: 3
Date: Wed, 16 Dec 2009 19:28:36 +0200
From: Jari Oksanen <jari.oksanen@oulu.fi>
Subject: Re: [R-sig-eco] vegan package: goodness.cca function?
    (citations needed)
To: L Quinn <lquinn@hotmail.com>, <r-sig-ecology@r-project.org>
Message-ID: <C74EE564.991D%jari.oksanen@oulu.fi>
Content-Type: text/plain;    charset="US-ASCII"




On 25/11/2009 16:56, "L Quinn" <lquinn@hotmail.com> wrote:

> 
> Hello,
> 
> I posted this R-help and Ecolog-L before I knew about R-sig-ecology.
> I hope you will forgive the cross-posting, if you subscribe to those lists
> too.
> 
> I deleted a number of species
> from a canonical correspondence analysis (CCA) model after using the
> function goodness.cca in vegan.  In his explanation of the diagnostic
> tools for CCA
> (http://cc.oulu.fi/~jarioksa/softhelp/vegan/html/goodness.cca.html),
> Jari Oksanen states that "It is a common practise to use goodness
> statistics to remove
> species from ordination plots". However, I have not been able to find
> references on this "common practice" in the literature. I may be using
> inappropriate search terms?? However, if anybody can point me towards
> some cite-able references/rules of thumb on this, I would very much
> appreciate it.
> 
Hello,

CanoDraw does so as default (or did when I last used several years ago), and
since people commonly use CanoDraw, they commonly omit "badly fitting"
species. This doesn't mean that they are aware of doing so. I don't know how
well this is documented, but check CanoDraw manual and books about CANOCO.

Cheers, Jari Oksanen

> And yes, I am also aware that Jari goes on to say that deleting species isn't
> always a good idea.
> 
> Thank you!
> 
> Lauren Quinn          
> _________________________________________________________________
> Bing brings you maps, menus, and reviews organized in one place.
> 
> [[alternative HTML version deleted]]
> 
> _______________________________________________
> R-sig-ecology mailing list
> R-sig-ecology@r-project.org
> https://stat.ethz.ch/mailman/listinfo/r-sig-ecology



------------------------------

Message: 4
Date: Wed, 16 Dec 2009 20:53:21 +0100
From: Gian Maria Niccol? Benucci <gian.benucci@gmail.com>
Subject: Re: [R-sig-eco] Fwd: Fwd: how to calculate "axis variance" in
    metaMDS, pakage vegan?
To: r-sig-ecology@r-project.org
Message-ID:
    <77e46e110912161153p78fdf5a5nbb7e000591ba2449@mail.gmail.com>
Content-Type: text/plain

Hi Jari and hi all again,

The documentation is sintetic and clear... I don't know, maybe is rather
difficutl for me also because I am studing statistics since few time ago and
also maybe english lenguage didn't help me with that...
I would ask something about the permutation in adonis().
As every permutational tests do, also adonis() systematically recomputing
the statistic estimate leaving out one observation at a time from the data
set. The* F*-tests are then based on sequential data that are choosen from
the original one...

In this case what is the correct choice for the permutation number?

Why I obtain the same output when I try "Community" and "Host" interaction
and "Community" effect nested within "Host"???

> adonis(sqrtABCD ~ Community*Host, method="bray", data=env.table,
permutations=99)

Call:
adonis(formula = sqrtABCD ~ Community * Host, data = env.table,
permutations = 99, method = "bray")

                Df SumsOfSqs  MeanSqs  F.Model    R2 Pr(>F)
Community  3.00000  2.43264  0.81088  2.70182 0.1838  0.01 **
Residuals 36.00000  10.80441  0.30012          0.8162
Total    39.00000  13.23705                  1.0000
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

> adonis(sqrtABCD ~ Community/Host, method="bray", data=env.table,
permutations=99)

Call:
adonis(formula = sqrtABCD ~ Community/Host, data = env.table,
permutations = 99, method = "bray")

                Df SumsOfSqs  MeanSqs  F.Model    R2 Pr(>F)
Community  3.00000  2.43264  0.81088  2.70182 0.1838  0.01 **
Residuals 36.00000  10.80441  0.30012          0.8162
Total    39.00000  13.23705                  1.0000
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
>


Anyway, looking at the graph, that't great and I think I'll go for this one:


>
> NMS.2
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:    24.53723
Two convergent solutions found after 60 tries
Scaling: centring, PC rotation, halfchange scaling
Species: expanded scores based on ‘sqrtABCD’
>
>
> stressplot(NMS.2)
Using step-across dissimilarities:
Too long or NA distances: 230 out of 780 (29.5%)
Stepping across 780 dissimilarities..

Non-metric fit, R2=0.94
Linear fit, R2=0.719
>
> envfit(NMS.2, env.table, permu=1000) -> NMS.2ef
> NMS.2ef

***FACTORS:

Centroids:
              NMDS1  NMDS2
CommunityA  0.3268 -0.2131
CommunityB  0.2055 -0.2042
CommunityC  -0.2584  0.3133
CommunityD  -0.2738  0.1040
HostCorylus  0.2661 -0.2087
HostOstrya  -0.2661  0.2087

Goodness of fit:
              r2  Pr(>r)
Community 0.2061 0.006993 **
Host      0.1937 0.001998 **
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
P values based on 1000 permutations.
>
>
> adonis(sqrtABCD ~ Host*Community, method="bray", data=env.table,
permutations=99)
Call:adonis(formula = sqrtABCD ~ Host * Community, data = env.table,
permutations = 99, method = "bray")

                Df SumsOfSqs  MeanSqs  F.Model    R2 Pr(>F)
Host      1.00000  1.64429  1.64429  5.47874 0.1242  0.01 **
Community  2.00000  0.78834  0.39417  1.31337 0.0596  0.14
Residuals 36.00000  10.80441  0.30012          0.8162
Total    39.00000  13.23705                  1.0000
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
>
>


What do you think?

Cheers,

Gian



2009/12/13 Jari Oksanen <jari.oksanen@oulu.fi>

> On 13/12/09 12:44 PM, "Gian Maria Niccolò Benucci" <gian.benucci@gmail.com
> >
> wrote:
>
> > Does a better explanation of what adonis() do already exist?
>
> "In extreme cases, read the documentation" (Duncan Murdoch in R fortunes).
>
> > So briefly and to summerize... What are, at the end, the differences
> about
> > these different following functions?
> >
> > adonis(ABCDsqrt ~ Host, env.table)
> >
> ... by 'Host'
>
> > adonis(ABCDsqrt ~ Host/Community, env.table)
> >
>  ... by 'Host' nested within 'Community'.
>
> > adonis(ABCDsqrt ~ Host*Community, env.table)
> >
>  ... by 'Host' and 'Community' and their interaction.
>
> > adonis(ABCDsqrt ~ Host, strata = env.table$Community, env.table)
> >
>  ... by 'Host' but permutations statified within 'Community'. This makes no
> sense because you restrict your permutations within the only variable you
> use to explain the results.
>
> > Thank you,
> >
>
> Thanks, but which of these cases was *not* documented? Or alternatively, if
> you tried different formulae without reading the documentation of
> 'formula',
> which cases were not obvious from the output *when* you tried it? (Or did
> you try?)
>
> Cheers, jari oksanen
>
>


-- 
Dr. Gian Maria Niccolò Benucci
Ph.D. Candidate
Department of Applied Biology - University of Perugia
Borgo XX Giugno, 74
I-06121 - Perugia, ITALY
Email: gian.benucci@gmail.com
Tel: +39.075.5856433
http://www.unipg.it/

    [[alternative HTML version deleted]]



------------------------------

Message: 5
Date: Thu, 17 Dec 2009 10:46:35 +1300
From: Etienne Lalibert? <etiennelaliberte@gmail.com>
Subject: Re: [R-sig-eco] dissimilarity and species turnover
To: r-sig-ecology@r-project.org
Message-ID: <1260999995.2093.23.camel@globetrotter>
Content-Type: text/plain; charset="UTF-8"

Robert,

Jari explained well the main mathematical problem with the method, which
is convincing enough in itself. Reading the Legendre and others vs
Tuomisto discussion (as Jari pointed out) would be useful (the whole
thing is available as a single PDF file from
http://www.elaliberte.info/Discussion_analyzing_beta_diversity.pdf), but
to make things short, consider also this additional point.

Your initial question (i.e. whether productivity affects species
turnover) could be directly tested using the "raw-data" approach (e.g.
through RDA after species transformations, or maybe CCA) - there is
simply no need to transform your response and explanatory variables into
distance matrices to test your hypothesis in the "distance world" (.
Legendre et al (2005, Ecol Monogr) had shown that doing so (on top of
being mathematically doubtful) leads to less powerful tests (i.e.
greater type-II error).

In conclusion, do not use it because of what Jari said. But to convince
you further, even if you were willing to ignore the non-additivity
problem (which you shouldn't), you'd be using less powerful tests
anyway. Bottom line: I'd say use RDA or CCA for your question.

-- 
Etienne Lalibert?
================================
School of Forestry
University of Canterbury
Private Bag 4800
Christchurch 8140, New Zealand
Phone: +64 3 366 7001 ext. 8365
Fax: +64 3 364 2124
www.elaliberte.info



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