Re: [R-sig-eco] biplot with most important species

[stephen sefick]

Hi Bjorn,

I have done this in the past using ggplot2. I think that I plotted
everything, but only labeled those that were above some threshold. In other
words, I changed the label in the input data to "". I think that is how I
solved this problem.
HTH,

Stephen

On Wed, Oct 12, 2016 at 5:44 AM, Bjorn  wrote:

> Hi all,
>
> there might be a simple solution to do this, but I don't seem to manage to
> find it. I have done a PCA using vegan. I now want to create a biplot with
> only those species that explain a certain (cumulative) amount of the
> variation along the first 2 PC axes (in order to keep things clear). How
> could this be done?
>
> Thanks in advance!
>
> Kind regards,
>
> Bjorn
>
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-- 
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feel like gods.  We are mammals, and have not exhausted the annoying little
problems of being mammals.

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[R-sig-eco] RLQ appropriate with factor variable in the R matrix?

[stephen sefick]

Hello,

I apologize this is more of a statistics question, but I am unsure where
else to ask.

Does it make sense to analyze data with L=abundances, Q=fuzzy coded traits,
and R=Treatment (factor with 6 levels) with RLQ?
many thanks,

-- 
Stephen Sefick
**
Auburn University
Biological Sciences
331 Funchess Hall
Auburn, Alabama
36849
**
sas0...@auburn.edu
http://www.auburn.edu/~sas0025
**

Let's not spend our time and resources thinking about things that are so
little or so large that all they really do for us is puff us up and make us
feel like gods.  We are mammals, and have not exhausted the annoying little
problems of being mammals.

-K. Mullis

A big computer, a complex algorithm and a long time does not equal
science.

  -Robert Gentleman

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Re: [R-sig-eco] Regression with few observations per factor level

[stephen sefick]

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 b) stanovuje, že smlouva musí mít písemnou formu,
 c) vylučuje přijetí nabídky s dodatkem či odchylkou,
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-- 
Stephen Sefick
**
Auburn University
Biological Sciences
331 Funchess Hall
Auburn, Alabama
36849
**
sas0...@auburn.edu
http://www.auburn.edu/~sas0025
**

Let's not spend our time and resources thinking about things that are so
little or so large that all they really do for us is puff us up and make us
feel like gods.  We are mammals, and have not exhausted the annoying little
problems of being mammals.

-K. Mullis

A big computer, a complex algorithm and a long time does not equal
science.

  -Robert Gentleman

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Re: [R-sig-eco] A beginner's question to constrained ordinations with vegan

[Stephen Sefick]

Tim,

I would take a look at Numerical Ecology with R. This book may not 
address your particular question, but should be useful as a general 
reference for using R for quantitative ecology.


Some questions:
Are you interested in what is structuring the community along an 
environmental gradient? What is the rational for investigating the 
sub-gradients?


My own curiosity:
Is there a literature source with quantitative data demonstrating that 
particular ordinations better uncover true environmental/distance 
relationships?


A couple of comments (please correct my misunderstandings):
RDA and PCA followed by envfit will give different results because they 
are doing very different things. From my understanding, rda uses the 
predicted value matrix from a multivariate regression of Comm_Mat ~ 
Env_Mat and then preforms a PCA on the resulting matrix (mean value 
given the environmental predictors; constrained). A PCA on the 
(appropriately hellinger transformed?) Comm_Mat is unconstrained by the 
environmental variation and projects sites along the direction of 
maximum variance in Comm_Mat only. Therefore, these techniques will give 
very different results.


I hope that helps, and my explanation is not very far from the reality 
of the techniques.


kindest regards,

Stephen

On 10/09/2014 07:26 AM, Tim Richter-Heitmann wrote:

Hi there,

i have a typical ecological problem (modelling abiotic parameters to
bacterial abundances - i have 9 of these explanatory variables (but also
a variety of spatial and biotic parameters, who may serve as
explanators), many bacterial species and hundreds of sites).

My species gradients seem to be very long in the DCA, so i began my
analysis with CCA modelling all 9 abiotic parameters to the species
matrix, and using the triplot as a final result.

However, i have two very distinct bacterial communities in the DCA with
a huge gap on the x-axis between them (one community is defining 90% of
all samples, and the smaller one is found in 10% of the samples), so i
was fiddling around with performing rda's
(which i believe is recommended for small species gradients) on the two
subsets.

Now, a colleague was actually recommending me to use unconstrained
ordinations like PCA and use envfit to fit the explanatory variables later.

ord.OTU - rda(OTU)
ef - envfit(ord.OTU, Env, perm=999)

instead of

ord.OTU - rda(OTU~., Env)

However, i fail to grasp the ideas and differences behind and between
the two approaches - in my case, an envfitted PCA looked different than
the equivalent RDA. As far as i have been taught, constrained
ordination techniques like RDA or CCA search for the best explaining
variables in the direct gradients, so i would use those for problems
like mine per default. So, what are the benefits in using the
unconstrained techniques first?

Since i am new to the field, i lack the experience to evaluate this. Any
advice would make me a very happy student.

Thank you very much, and my apologies if i have asked something that was
asked many times before. In fact, i tried to find the answer online, but
wasnt too successful.





--
Stephen Sefick
**
Auburn University
Biological Sciences
331 Funchess Hall
Auburn, Alabama
36849
**
sas0...@auburn.edu
http://www.auburn.edu/~sas0025
**

Let's not spend our time and resources thinking about things that are so 
little or so large that all they really do for us is puff us up and make 
us feel like gods.  We are mammals, and have not exhausted the annoying 
little problems of being mammals.


-K. Mullis

A big computer, a complex algorithm and a long time does not equal 
science.


  -Robert Gentleman

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Re: [R-sig-eco] NA error in envfit

[Stephen Sefick]

Kendra,

Something is wrong in X or P; find out what the foreign function call is 
 and then you may be able to track down the offending data problem. 
Maybe a logarithm somewhere? This is probably not much help; I don't 
have much experience with envfit.


Stephen

On 12/03/2013 07:06 PM, Mitchell, Kendra wrote:

I'm running a bunch of NMS with vectors fitted (slicing and dicing a large 
dataset in different ways).  I'm suddenly getting an error  from envfit

f.bSBS.org.fit-envfit(f.bSBS.org.nms, f.bSBS.org.env, permutations=999, 
na.rm=TRUE)

Error in vectorfit(X, P, permutations, strata, choices, w = w, ...) :
   NA/NaN/Inf in foreign function call (arg 1)
In addition: Warning message:
In vectorfit(X, P, permutations, strata, choices, w = w, ...) :
   NAs introduced by coercion

I can plot the NMS and even run ordifit on individual env variables, so can't 
figure out what the problem is.   There aren't any NA/NaN/Inf in either of 
those data that I can find.  I've tried running it without na.rm=TRUE and still 
get the error.  Guidance on how to fix this would be appreciated.

Here's the whole slicing process and str for the data


f.bSBS.org-f.env$zone.hor==bSBS.1
f.bSBS.org.tyc-f.tyc[f.bSBS.org,f.bSBS.org]
f.bSBS.org.env-subset(f.env, f.env$zone.hor==bSBS.1)
f.bSBS.org.nms-metaMDS(as.dist(f.bSBS.org.tyc), k=3, trymin=50, trymax=250, 
wascores=FALSE)
f.bSBS.org.fit-envfit(f.bSBS.org.nms, f.bSBS.org.env, permutations=999, 
na.rm=TRUE)


str(f.bSBS.org.env)
'data.frame':63 obs. of  14 variables:
  $ zone : Factor w/ 6 levels bIDF,bSBS,..: 2 2 2 2 2 2 2 2 2 2 ...
  $ site : Factor w/ 18 levels A7,A8,A9,..: 12 12 12 12 12 12 12 
12 12 12 ...
  $ om   : Factor w/ 4 levels 0,1,2,3: 2 2 2 3 3 3 2 2 2 3 ...
  $ compaction   : num  1 1 1 1 1 1 1 1 1 1 ...
  $ herbicide: num  0 0 0 0 0 0 0 0 0 0 ...
  $ horizon  : Factor w/ 2 levels 1,2: 1 1 1 1 1 1 1 1 1 1 ...
  $ Water_content: num  50.3 50.3 50.3 50.1 50.1 ...
  $ DNA_ug_g : num  71.2 71.2 71.2 68.6 68.6 ...
  $ C: num  30.5 30.5 30.5 28.4 28.4 ...
  $ N: num  0.863 0.863 0.863 0.81 0.81 ...
  $ pH_H2O   : num  4.63 4.63 4.63 4.49 4.49 ...
  $ CN   : num  35.3 35.3 35.3 35.1 35.1 ...
  $ f.env$zone   : Factor w/ 6 levels bIDF,bSBS,..: 2 2 2 2 2 2 2 2 2 2 ...
  $ zone.hor : chr  bSBS.1 bSBS.1 bSBS.1 bSBS.1 ...

str(f.bSBS.org.nms)
List of 35
  $ nobj  : int 63
  $ nfix  : int 0
  $ ndim  : num 3
  $ ndis  : int 1953
  $ ngrp  : int 1
  $ diss  : num [1:1953] 0.00424 0.00437 0.05169 0.07522 0.11039 ...
  $ iidx  : int [1:1953] 12 8 55 56 52 7 56 12 59 52 ...
  $ jidx  : int [1:1953] 7 6 18 55 8 3 18 3 12 49 ...
  $ xinit : num [1:189] 0.654 0.837 0.438 0.105 -0.313 ...
  $ istart: int 1
  $ isform: int 1
  $ ities : int 1
  $ iregn : int 1
  $ iscal : int 1
  $ maxits: int 200
  $ sratmx: num 1
  $ strmin: num 1e-04
  $ sfgrmn: num 1e-07
  $ dist  : num [1:1953] 0.0679 0.0231 0.3598 0.1248 0.1422 ...
  $ dhat  : num [1:1953] 0.0455 0.0455 0.2076 0.2076 0.2076 ...
  $ points: num [1:63, 1:3] -0.1256 0.1224 0.267 0.2374 -0.0427 ...
   ..- attr(*, dimnames)=List of 2
   .. ..$ : chr [1:63] LL001 LL002 LL003 LL007 ...
   .. ..$ : chr [1:3] MDS1 MDS2 MDS3
   ..- attr(*, centre)= logi TRUE
   ..- attr(*, pc)= logi TRUE
   ..- attr(*, halfchange)= logi FALSE
  $ stress: num 0.157
  $ grstress  : num 0.157
  $ iters : int 180
  $ icause: int 3
  $ call  : language metaMDS(comm = as.dist(f.bSBS.org.tyc), k = 3, trymax 
= 250, wascores = FALSE, trymin = 50)
  $ model : chr global
  $ distmethod: chr user supplied
  $ distcall  : chr as.dist.default(m = f.bSBS.org.tyc)
  $ distance  : chr user supplied
  $ converged : logi TRUE
  $ tries : num 23
  $ engine: chr monoMDS
  $ species   : logi NA
  $ data  : chr as.dist(f.bSBS.org.tyc)
  - attr(*, class)= chr [1:2] metaMDS monoMDS


--
Kendra Maas Mitchell, Ph.D.
Post Doctoral Research Fellow
University of British Columbia
604-822-5646

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--
Stephen Sefick
**
Auburn University
Biological Sciences
331 Funchess Hall
Auburn, Alabama
36849
**
sas0...@auburn.edu
http://www.auburn.edu/~sas0025
**

Let's not spend our time and resources thinking about things that are so 
little or so large that all they really do for us is puff us up and make 
us feel like gods.  We are mammals, and have not exhausted the annoying 
little problems of being mammals.


-K. Mullis

A big computer, a complex algorithm and a long time does not equal 
science

[R-sig-eco] forward selection RDA after controlling for constraints

[Stephen Sefick]

Hello all,

I would like to run this by everyone and maybe get some hints as to what 
R functions I could use for this.  Ok, so I have macroinvertebrate 
assemblage data from across the SE.  I would like to control for 
geographic distance (lat/long), Watershed area, and year before 
submitting these data to an RDA with the rest of the environmental data 
using a variable selection technique.


Does it make sense to detrend the data using a mlm on hellinger 
transfomed abundances with the above env variables as regressors and 
then submit the residuals to rda with the rest of the env variables I am 
interested in?


Many thanks for all of the help.
kindest regards,


--
Stephen Sefick
**
Auburn University
Biological Sciences
331 Funchess Hall
Auburn, Alabama
36849
**
sas0...@auburn.edu
http://www.auburn.edu/~sas0025
**

Let's not spend our time and resources thinking about things that are so 
little or so large that all they really do for us is puff us up and make 
us feel like gods.  We are mammals, and have not exhausted the annoying 
little problems of being mammals.


-K. Mullis

A big computer, a complex algorithm and a long time does not equal 
science.


  -Robert Gentleman

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Re: [R-sig-eco] forward selection RDA after controlling for constraints

[stephen sefick]

Jari,

Thank you for the quick reply.  Maybe I should use something like PCNM
first with the lat/long data to then use in the rda?  I really appreciate
all of your help.  Are there anyother/better ways to account for spatial
autocorrelation.  I guess I need to show that spatial autocorellation
exists and then if it does account for it?  Any reading etc. would be
greatly appreciated.  I appreciate all of the help.
kindest regards,

Stephen

P.S.  I will let you know about the stepwise selection and scope argument


On Wed, Jul 10, 2013 at 2:28 PM, Jari Oksanen jari.oksa...@oulu.fi wrote:


 On 10/07/2013, at 21:00 PM, Stephen Sefick wrote:

  Hello all,
 
  I would like to run this by everyone and maybe get some hints as to what
 R functions I could use for this.  Ok, so I have macroinvertebrate
 assemblage data from across the SE.  I would like to control for geographic
 distance (lat/long), Watershed area, and year before submitting these data
 to an RDA with the rest of the environmental data using a variable
 selection technique.
 
  Does it make sense to detrend the data using a mlm on hellinger
 transfomed abundances with the above env variables as regressors and then
 submit the residuals to rda with the rest of the env variables I am
 interested in?


 Stephen,

 If you happen to use vegan functions for forward selection, please note
 that they all (should) take a scope argument that can (should) be a list of
 lower and upper scopes. Put your controlled variables (distance???,
 watershed area, year) in the lower scope and these plus other candidate
 variables in the upper scope, and there you go. I have used should,
 because I have rarely used these functions myself, and I'm not sure if
 lower scope really is implemented in all, but is *should* be: file a bug
 report if this fails.

 I have no idea how to have distance RDA. Well, I have ideas, but none that
 I have are very good.

 Using separate mlm and modelling residuals will not work quite correctly,
 because that ignores correlations between groups of variables. Vegan
 functions do not ignore those.

 Cheers, Jari Oksanen
 --
 Jari Oksanen, Dept Biology, Univ Oulu, 90014 Finland
 jari.oksa...@oulu.fi, Ph. +358 400 408593, http://cc.oulu.fi/~jarioksa







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Re: [R-sig-eco] pca or nmds (with which normalization and distance ) for abundance data ?

[Stephen Sefick]

On Thu 13 Dec 2012 09:24:41 AM CST, claire della vedova wrote:


Dear all,

I’m a biostatistician working for a French institute involved in
environmental risk assessment, and I would need help to understand the
results I obtained from several ordination analyses.

I have a dataset of 25 sites. For these 25 sites I have abundance data 
of 38

species and also the measurement of 5 environmental variables.

Here an extract of my abundance data for the 5 first sites:

Anguinidae.ditylenchus Aphelenchidae Aphelenchoididae Aporcelaimidae

12 18 184 0

0 14 154 0

45 0 101 6

20 0 148 0

0 0 118 0



Here the environmental data for the 5 first sites:

ExtPond moist Corg pH DV50

0.946 9.086 4.269 5.24 171.33

0.682 27.139 23.813 3.82 75.45

2.480 14.322 7.191 4.48 230.90

3.069 18.380 11.404 3.58 211.19

2.615 16.693 7.128 4.12 224.45



My aim was to study how the distribution of species is linked with
environmental data.

Firstly, I did a PCA (with vegan library), using a Hellinger 
transformation,

with commands like this :

acp1-rda(decostand(myDataSpec[,c(25:62)], hellinger))




Is the Hellinger transform done on relative proportions?











The first axe represent 19.5% the second one 16.3%. A colleague of me said
it is not so bad with abundance data, but it seems to me quite poor. 
What do

you think about ?




You could use something like the broken stick model or others to access 
how many axes are necessary, but two axes explaining 40% of the 
variation seems low.




Then, I fitted environmental vectors with the envfit function (of vegan
library), with commands like this :

physCInd.fit3-envfit(acp1,MyDataEnv[,c(13,18,20,21,23)], permut=4999,
na.rm=T)

It appeared that pH variable is significantly linked with the ordination,
and the pval of ExtPond is 0.1.

Next I did a RDA which is not significant.

To finish I did two NMDS. For the first one I used the Hellinger
normalization and the Bray-Curtis distance. The stress obtained value is
0.22, Non metric fit R² is 0.952 and Linear fit R2 =0.777. When I 
fitted the

environmental vectors , ExtPond was correlated with the ordination (pval
=0.02) and p-val of pH = 0.23

But then I read in “numerical ecology” page 449 that it’s better to
standardize the data by dividing each value by maximum abundance for 
species
and then use Kulcynski distance. The stress value was 0.23 , Non 
metric fit

R² was 0.948 and Linear fit R2 =0.69. These values are a little less good
than those of the first NMDS, but the stressplot seems to me more
homogenous.

Nevertheless, the results I obtained are very different... When I fitted
the environmental data it appeared that ExtPond was not correlated 
with this

ordination (p-val=0.82) and p-val of pH=0.06. And obviously ExtPond is the
most important variable for us ;-)

With all these results, I’m quite confused, and I don’t know what to 
think.

So, if someone can help me, I would appreciate it very much. Be sure that
all comments will be welcome.

To summarize my questions are :

a) Which ordination method would be better for my data : PCA knowing
that the represented inertia is 35.62% or NMDS with a stress value about
0.22?

My opinion is PCA on hellinger transformed relative proportions means 
more than an NMDS



b) If NMDS is more adapted which one is the better? with Hellinger
normalization and Bray-Curtis distance, or with the normalization
recommended by Legendre and Legendre and Kulcynski distance ?

I sounds like the normalization you are referring to is relative 
proportion which is si/sum(s); s is a vector of taxon at a site.



c) Is there other method to apply? I’m going to try co-inertia with
ade4 package



I am reading about co-inertia analysis now as it may be useful for some 
of the things that I am planning on doing.  This method looks promising.


You are going to have to decide on what type of ordination to use with 
COIA...


HTH,

Stephen


Thanks in advance.

Cheers.

Claire Della Vedova




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Stephen Sefick
**
Auburn University
Biological Sciences
331 Funchess Hall
Auburn, Alabama
36849
**
sas0...@auburn.edu
http://www.auburn.edu/~sas0025
**

Let's not spend our time and resources thinking about things that are so little 
or so large that all they really do for us is puff us up and make us feel like 
gods.  We are mammals, and have not exhausted the annoying little problems of 
being mammals.

 -K. Mullis

A big computer, a complex algorithm and a long time does not equal science.

   -Robert Gentleman



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Re: [R-sig-eco] Example on trend analysis for biological monitoring

[Stephen Sefick]

I don't know of one specifically, but what are you trying to do?

Stephen

On 07/14/2012 10:05 AM, Manuel Spínola wrote:
 Dear list members,

 Is there any simple example for teaching purposes on how to use R to
 analyze a time series in a biological monitoring context?

 Best,

 Manuel



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Re: [R-sig-eco] rarefaction not working with the min species number

[Stephen Sefick]

Brian,

What would you suggest?  Is there any literature that you would suggest 
that I read?  The abundances are estimated from a subsample of the whole 
sample using a volumetric procedure (subsamples out of 1L until at least 
300 individuals are picked).  This results in fractions of individuals 
for some, but not all taxa at a site.

many thanks,

Stephen

On 04/25/2012 09:29 AM, Brian Inouye wrote:
Although rounding to the nearest integer will make the code run 
without an error, it seems like then you are making the implicit 
assumption that every unit of abundance is an independent sample with 
equal probability of occurrence, equivalent to independent 
individuals.  While I can imagine that is justifiable for some 
datasets, in other cases that would be a dubious assumption.

-Brian

On 4/25/2012 6:00 AM, r-sig-ecology-requ...@r-project.org wrote:

From: Stephen Seficksas0...@auburn.edu
To: Jari Oksanenjari.oksa...@oulu.fi
Cc: r-sig-ecologyr-sig-ecology@r-project.org
Subject: Re: [R-sig-eco] rarefaction not working with the min species
number
Message-ID:4f96b0b9.1050...@auburn.edu
Content-Type: text/plain; charset=UTF-8; format=flowed

Jari,

Many thanks, that did the trick.  Here is a little bit of code that
took my non-integer data (abundance estimates), rounds, and turns the
numbers into a data.frame with all integers col classes.

community_round2int- function(L){


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--
Stephen Sefick
**
Auburn University
Biological Sciences
331 Funchess Hall
Auburn, Alabama
36849
**
sas0...@auburn.edu
http://www.auburn.edu/~sas0025
**

Let's not spend our time and resources thinking about things that are so little 
or so large that all they really do for us is puff us up and make us feel like 
gods.  We are mammals, and have not exhausted the annoying little problems of 
being mammals.

-K. Mullis

A big computer, a complex algorithm and a long time does not equal science.

  -Robert Gentleman

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Re: [R-sig-eco] rarefaction not working with the min species number

[Stephen Sefick]

Jari,

Many thanks, that did the trick.  Here is a little bit of code that 
took my non-integer data (abundance estimates), rounds, and turns the 
numbers into a data.frame with all integers col classes.


community_round2int - function(L){

row - rownames(L)

a - as.data.frame(apply(L, 2, round))

b - as.data.frame(apply(a, 2, as.integer))

rownames(b) - row

return(b)

}

kindest regards,

Stephen

On Tue 24 Apr 2012 08:01:16 AM CDT, Jari Oksanen wrote:


On 24/04/2012, at 15:39 PM, Stephen Sefick wrote:


I will provide reproducible code if I need to.

All:

I am trying to set up a 1000 pulls of a community data frame for calculating 
richness measures.  I would like to be able to code the sample number based on 
the minimum of all of the samples.  I can do this but there is an error:

Error in sample(rep(nm, times = x[i, ]), sample[i]) :
  cannot take a sample larger than the population when 'replace = FALSE'

when using:

rrarefy(L, min(apply(L, 1, sum)))

min is returning the lowest sample abundance of all of the samples.  rrarefy 
works if I subtract 5 (arbitrary) from the min(...) statement.  I am sure that 
I am missing something simple.
many thanks,


Stephen,

I can reproduce this if input data ('L') contain non-integer data. The function 
is only able to handle integer data, but it does not check the input. Probably 
it should: the error would still be there, but the message would be more 
informative.

Cheers, Jari

--
Jari Oksanen, Dept Biology, Univ Oulu, 90014 Finland

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Stephen Sefick
**
Auburn University
Biological Sciences
331 Funchess Hall
Auburn, Alabama
36849
**
sas0...@auburn.edu
http://www.auburn.edu/~sas0025
**

Let's not spend our time and resources thinking about things that are 
so little or so large that all they really do for us is puff us up and 
make us feel like gods.  We are mammals, and have not exhausted the 
annoying little problems of being mammals.


   -K. Mullis

A big computer, a complex algorithm and a long time does not equal 
science.


 -Robert Gentleman

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Re: [R-sig-eco] Landscape ecology in R

[Stephen Sefick]

Third hit in a google search for: landscape ecology R

http://nricaribou.cc.umanitoba.ca/R/

On 03/09/2012 04:53 PM, Manuel Spínola wrote:
 Dear list members,

 I am looking for any reference or material on landscape ecology analysis in
 R.

 Thank you very much in advance.

 Best,

 Manuel



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-- 
Stephen Sefick
**
Auburn University
Biological Sciences
331 Funchess Hall
Auburn, Alabama
36849
**
sas0...@auburn.edu
http://www.auburn.edu/~sas0025
**

Let's not spend our time and resources thinking about things that are so little 
or so large that all they really do for us is puff us up and make us feel like 
gods.  We are mammals, and have not exhausted the annoying little problems of 
being mammals.

 -K. Mullis

A big computer, a complex algorithm and a long time does not equal science.

   -Robert Gentleman



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Re: [R-sig-eco] PCA

[Stephen Sefick]

Is it an assemblage that you are trying to ordinate?  If so, you can use 
the hellinger transformation that is avaliable in the decostand function 
of vegan to transform the data so that when you ordinate it you will 
then have euclidean distances.  See Legendre and Gallagher 2001 for the 
relevant discussion.

FWIW,

Stephen

On 03/03/2012 03:45 PM, Sami Rabei wrote:

Dear All

If I have a similarity matrix, It is possible to have a PCA (Principal
component for it.



Sami Rabei

http://mansoura.academia.edu/SamiRabei




--
With my Best Wishes
Sami Hussein Rabei, Ph.D.
Botany Department
Faculty of Science at Damietta
New Damietta , Post Box 34517
Damietta
Egypt .

Tel. Mobile:  002 0127 3601618
Tel. Work:002 057 2403981
Tel. Home:002 057 2403108
Fax:  002 057 2403868

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--
Stephen Sefick
**
Auburn University
Biological Sciences
331 Funchess Hall
Auburn, Alabama
36849
**
sas0...@auburn.edu
http://www.auburn.edu/~sas0025
**

Let's not spend our time and resources thinking about things that are so little 
or so large that all they really do for us is puff us up and make us feel like 
gods.  We are mammals, and have not exhausted the annoying little problems of 
being mammals.

-K. Mullis

A big computer, a complex algorithm and a long time does not equal science.

  -Robert Gentleman

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Re: [R-sig-eco] Fwd: help

[Stephen Sefick]

Mahnaz,

We need this to be reproducible.  Assuming you can get this into R,  use 
dput and copy the results of that into an email.


#copy this into R
set.seed(1000)
x - data.frame(a=rnorm(10), b=rnorm(10), c=rnorm(10))

dput(x)


#here is the output

x - (structure(list(a = c(-0.445778264836677, -1.2058565689643, 
0.0411263138456899,
0.639388407571143, -0.786554355912735, -0.38548929809552, 
-0.475867884077706,
0.719750691474437, -0.018505622887574, -1.37311775893855), b = 
c(-0.982427827680338,
-0.554488701581856, 0.121381188657659, -0.120872315937481, 
-1.33604104855461,
0.170057481208407, 0.155078715940733, 0.0249318673672384, 
-2.04658541402115,

0.213154105608615), c = c(2.67007166431368, -1.22701600631211,
0.834247331947683, 0.532571747050487, -0.646824963186768, 
0.603161260832291,

-1.78384413571217, 0.334942167117471, 0.560975721885086, 1.22093565459939
)), .Names = c(a, b, c), row.names = c(NA, -10L), class = 
data.frame))



notice that I have wrapped the output in x -() to make it copy and 
paste ready.


Now we have something to work with.  If you can't get data into R then 
that is a different question.


Stephen Sefick





On 12/10/2011 12:57 PM, Mahnaz Rabbaniha wrote:

-- Forwarded message --
From: Mahnaz Rabbaniharab.mah...@gmail.com
Date: Sat, 10 Dec 2011 08:31:10 +0330
Subject: Re: [R-sig-eco] help
To: Sarah Gosleesarah.gos...@gmail.com

  Dear Sarah


thank you for your attention,

I want to use PCA with supplementary variables due to correlation
between the independent factor such as salinity ,temperature... with
dependent variable ( fish larva ) therefore changed  dependent
variable with log(x+1) and tested normality with shapiro test but data
haven't changed and remained non normality then i decided to used the
nonparametric analysis MDS   and found its order from net but it
wasn't successfully and plot of matrix was empty.

my question is: for using the MDS have to need the special form of
excel ,


thanks

mahnaz


On 12/9/11, Sarah Gosleesarah.gos...@gmail.com  wrote:

Hi,

On Fri, Dec 9, 2011 at 7:13 AM, Mahnaz Rabbaniharab.mah...@gmail.com
wrote:

Hi

in my project i collected its data in excel sheet that i attached,data
were non normal then i transformed them and check the normality by
shapiro.test that it showed again non-normality therefore i decided
used the MDS analyses in r and download this site
  ( http://www.statmethods.net/advstats/mds.html)

but i haven't done it,  i think  the main problem is the form of excel
matrix for doing it and also i want to know about different between
  CLASSICAL
MDS and isoMDS.

We need to know more about what you're donig to be able to help with
the excel matrix. What is the problem? How are you importing it into R?

?cmdscale
library(MASS)
?isoMDS

offers some information about the algorithms used in the two approaches,
as well as many references for more information

Briefly and simplistically, the difference has to do with whether the
algorithm
attempts to preserve actual dissimilarities or rank-order dissimilarities.


please help me because this method is new in iran

Thanks in advance
--
*Mahnaz Rabbaniha*
*Senior expert of marine ecology *
*Iranian Fisheries Research Organization (IFRO) *
*P.O.Box: 14155-6116 , P.Code: 1411816618*
Tehran, IRAN
Phone:   +98 21 44580953
*Fax:   +98 21 44580583*
*Mobile:  +98 912 5790377*
*Website: http://www.ifro.ir*


--
Sarah Goslee
http://www.functionaldiversity.org


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Stephen Sefick
**
Auburn University
Biological Sciences
331 Funchess Hall
Auburn, Alabama
36849
**
sas0...@auburn.edu
http://www.auburn.edu/~sas0025
**

Let's not spend our time and resources thinking about things that are so little 
or so large that all they really do for us is puff us up and make us feel like 
gods.  We are mammals, and have not exhausted the annoying little problems of 
being mammals.

-K. Mullis

A big computer, a complex algorithm and a long time does not equal science.

  -Robert Gentleman

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Re: [R-sig-eco] NMDS vegan

[stephen sefick]

highlight all of the cells in the data matrix and in the edit menu (?)
you should find find and replace.
HTH,

Stephen

On Tue, Oct 26, 2010 at 1:30 PM, Soumi Ray soumira...@gmail.com wrote:
 Hi all,
 It worked when I replaced the blanks (missing) in my spreadsheet as NA and
 saved it as csv.
 Stephen, my data is in excel spreadsheet, I have no clue how to replace all
 the blank cells in the huge dataset as NA.

 Thank for the help.

 Soumi

 On Mon, Oct 25, 2010 at 2:10 PM, Soumi Ray soumira...@gmail.com wrote:

 Hi listers,

 I am trying to run NMDS in vegan package. I have a species dataset - with
 columns as species and rows as variables. All my data are 0/1
 (presence/absence). My data has missing values. I saved my data in txt file
 and the missing values are blank spaces. I am using the syntax:
 nmds -read.table ('nmds.txt', header=T, rows.names=1, sep=\t)
 nmds.mds.alt - metaMDS (nmds, distance=bray, k=2, autotransform=FALSE)

 But it is showing me the error:
 Error in distfun(comm, method = distance, ...) :
   NA/NaN/Inf in foreign function call (arg 1)
 In addition: Warning message:
 In distfun(comm, method = distance, ...) :
   you have empty rows: their dissimilarities may be meaningless in method
 bray

 Could anyone kindly let me know where am I going wrong? I admit i am new to
 R, trying to self tutor myself.

 Thank you,

 Regards,

 Soumi


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-- 
Stephen Sefick

| Auburn University                                   |
| Department of Biological Sciences           |
| 331 Funchess Hall                                  |
| Auburn, Alabama                                   |
| 36849                                                    |
|___|
| sas0...@auburn.edu                             |
| http://www.auburn.edu/~sas0025             |
|___|

Let's not spend our time and resources thinking about things that are
so little or so large that all they really do for us is puff us up and
make us feel like gods.  We are mammals, and have not exhausted the
annoying little problems of being mammals.

                                -K. Mullis

A big computer, a complex algorithm and a long time does not equal science.

                              -Robert Gentleman

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[R-sig-eco] stream metabolism

[stephen sefick]

I was wondering if anyone had written software for calculating stream
metabolism before I give her a whirl.  The upstream downstream method (Odum)
and the single station method (I don't remember the reference).  I have
fifteen minute DO data and associated travel times.   This would be my first
real programming effort and any and all help would be appreciated.
thanks

Stephen

-- 
Let's not spend our time and resources thinking about things that are so
little or so large that all they really do for us is puff us up and make us
feel like gods. We are mammals, and have not exhausted the annoying little
problems of being mammals.

-K. Mullis

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