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Bálint Czúcz | 6 Sep 19:06
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Re: glm for ratio [0,1] data

Thanks to everyone for the responses!
I think I will try first with the betareg approach, but it might not
be easy to implement since the data set in question also exhibits
symptoms of zero inflation. :(
I'll see.

Best regards,
Bálint

--
Bálint Czúcz
Institute of Ecology and Botany of the Hungarian Academy of Sciences
H-2163 Vácrátót, Alkotmány u. 2-4. HUNGARY
Tel: +36 28 360122/137  +36 70 7034692
magyar nyelvű blog: http://atermeszettorvenye.blogspot.com/

On Mon, Aug 31, 2009 at 18:10, <Farrar.David <at> epamail.epa.gov> wrote:
> All,
>
> I wonder if glm with a quasibinomial option would work.  The variance
> would depend qualitatively on
> the mean in a seemingly reasonable way, but would be adjusted using a
> factor determined by the data.
>
> David Farrar,
> National Center for Environmental Assessment, U.S.EPA, Cincinnati
>
> r-sig-ecology-bounces <at> r-project.org wrote on 08/31/2009 10:06:19 AM:
>
>> [image removed]
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Alexandre VILLERS | 7 Sep 09:30
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Producing ENFA derived suitability maps with adehabitat

Dear list(s) 'members,

I have a dataset representing the position of a species over a large 
region for 3 different years (2000, 2004 and 2008) and seven 
ecogeographical variables. Given that the study takes place on an 
agricultural region, the landscape changes every year at fine scale and 
there is a long term trend in crops sowed, leading me to account for 
between years variability.
I would like to use the niche determined by the landscape and location 
of birds in 2000 and then, appply this niche over the landscape in 2004 
and 2008 (this would, I believe, give me a first answer on whether 
changes in birds' location are due to a decrease in habitat suitability 
or birds).
I have already computed ENFA with adehabitat (using the doc provided 
with the package "adehabitat" and the www.spatial-analyst.net of T. 
Hengl) but I don't see how exactly using the result of an ENFA on a 
"new" landscape...

Any link or help would be welcome.

Alex

--

-- 
Alexandre Villers
PhD Student
Team "Biodiversity"
Centre d'Etudes Biologiques de Chizé-CNRS UPR1934
79360 Beauvoir sur Niort

Phone +33 (0)5 49 09 96 13
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Volker Bahn | 9 Sep 04:40
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Re: Teaching materials

Here is a compilation of the answers I received to my question about 
teaching materials for a biostats course based on R. I also added 
resources I found searching the web:

Ron Burns sent links to Jack Weiss’ courses at University of North 
Carolina, Chapel Hill
http://www.unc.edu/courses/2007spring/enst/562/001/docs/lectures.htm
http://www.unc.edu/courses/2008fall/ecol/563/001/docs/lectures.htm
http://www.unc.edu/courses/2006spring/ecol/145/001/docs/lectures.htm

Richie Erickson pointed me to Stephen Cox’s course: Statistical 
Applications in Environmental Toxicology
http://www.tiehh.ttu.edu/scox/Site/Stats.html

David Roberts pointed me to his own material at
http://ecology.msu.montana.edu/labdsv/R/

Alain Zuur pointed me to datasets that accompany his books at
http://www.highstat.com/books.htm
These are all real data sets (coming from PhD projects of commercial 
projects). They are fully worked out in the case study chapters of:
Analysing Ecological Data (2007).
Zuur, AF, Ieno, EN and Smith, GM. Springer. 680 p.
URL: www.springer.com/0-387-45967-7
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

Dave Roberts pointed me to his lab pages:
http://ecology.msu.montana.edu/labdsv/R
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Mathieu Basille | 13 Sep 22:57
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Re: Producing ENFA derived suitability maps with adehabitat

Dear all,

[This is a slightly modified version of a message posted on R-SIG-Geo. I
post here too for the sake of consistency]

The so-called predictions of the ENFA (function 'predict.enfa') are
based on the row coordinates ($li) and the vector of presence ($pr).
>From the row coordinates, the function computes Mahalanobis distances
from the center of the niche to every pixel (row), given the niche
covariance structure.

The row coordinates gives you the coordinates of every pixel projected
into the new space created by the ENFA. You can compute them by hand by
multiplying the (scaled) table of environmental variables with the
columns coordinates. For example, following the example of the function
'enfa':

## We load the data
data(lynxjura)
map <- lynxjura$map
tmp <- lynxjura$locs[,4]!="D"
locs <- lynxjura$locs[tmp, c("X","Y")]
map[,4] <- sqrt(map[,4])

## We perform the ENFA
dataenfa1 <- data2enfa(map, locs)
pc <- dudi.pca(dataenfa1$tab, scannf = FALSE)
(enfa1 <- enfa(pc, dataenfa1$pr, scannf = FALSE))

## We compute the row coordinates by hand and check that this is
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Manuel Spínola | 15 Sep 15:47
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Repeated measures model in R

Dear list members,

I would like to run a repeated measure model in R and I would like to 
have your advice on how to parameterize the model and which package and 
function should I use..

I have:

Disease rate (number of cases x 1000 people): the response variable
County (it would be the subject)
Year: from 2002 to 2007

My data set is unbalanced (the counties do not have information for all 
the years).
Thank you very much in advance.
Best,

Manuel Spínola

--

-- 
Manuel Spínola, Ph.D.
Instituto Internacional en Conservación y Manejo de Vida Silvestre
Universidad Nacional
Apartado 1350-3000
Heredia
COSTA RICA
mspinola@...
mspinola10@...
Teléfono: (506) 2277-3598
Fax: (506) 2237-7036
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gabriel singer | 15 Sep 17:02
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vegan: envfit (vectorfit)

Hi vegan-users and programmers,

Can anybody tell me how the function vectorfit (envfit) computes arrow 
lengths (as fits of a metric variable onto an ordination) exactly? I 
understand the scaling bit in the end, but have troubles to understand 
how actually the direction and strength of gradient of the environmental 
variable with the ordination is identified. Obviously it´s not a mere 
correlation between the environment variable and ordination scores, as 
is usually done for a PCA for example (the "loadings" as opposed to the 
eigenvectors).

thanks a lot for any good ideas..

gabriel

--

-- 
Dr. Gabriel Singer
Department of Freshwater Ecology - University of Vienna
and Wassercluster Lunz Biologische Station GmbH
+43-(0)664-1266747
gabriel.singer@...
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Gavin Simpson | 15 Sep 17:25
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Re: vegan: envfit (vectorfit)

On Tue, 2009-09-15 at 17:02 +0200, gabriel singer wrote:
> Hi vegan-users and programmers,
> 
> Can anybody tell me how the function vectorfit (envfit) computes arrow 
> lengths (as fits of a metric variable onto an ordination) exactly? I 
> understand the scaling bit in the end, but have troubles to understand 
> how actually the direction and strength of gradient of the environmental 
> variable with the ordination is identified. Obviously it´s not a mere 
> correlation between the environment variable and ordination scores, as 
> is usually done for a PCA for example (the "loadings" as opposed to the 
> eigenvectors).

It is a least squares fit of the following form:

Y ~ scores1 + scores2

where Y is the vector or matrix of numeric variables you wish to have
vectors for, and scores1 and scores2 are the user-selected axes of the
ordination configuration. If Y is a matrix then each variable (column)
in that matrix enters as a separate regression.

Effectively, it uses the locations of the points (sites) in the selected
2D ordination space to predict the observed values of the variables for
which vectors are being fitted.

The arrow heads are the normalised coefficients for scores1 and scores2,
and hence represent the normalised change in response for a unit change
in the scores1 and scores2 (the axis or site scores). As these are
normalised, the large the coefficient (change in response for unit
change in the site scores) the stringer the relationship between the
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gabriel singer | 15 Sep 19:14
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Re: vegan: envfit (vectorfit)

gavin and jari,

thanks, all makes sense.... I have to state that remembering the 
discussion we had some weeks ago about fitting underlying (or 
environmental) variables to a MDS ordination, that using vectorfit for 
this purpose indeed would make sense for me, too. As long as before 
choosing the representation as a vector (which would indeed suggest 
linear behaviour over ordination space), a linear or at least monotonic 
behaviour of the metric variable over ordination space is checked (e.g. 
given using ordisurf).... or different opinions?

cheers, g

Gavin Simpson wrote:
> On Tue, 2009-09-15 at 17:02 +0200, gabriel singer wrote:
>   
>> Hi vegan-users and programmers,
>>
>> Can anybody tell me how the function vectorfit (envfit) computes arrow 
>> lengths (as fits of a metric variable onto an ordination) exactly? I 
>> understand the scaling bit in the end, but have troubles to understand 
>> how actually the direction and strength of gradient of the environmental 
>> variable with the ordination is identified. Obviously it´s not a mere 
>> correlation between the environment variable and ordination scores, as 
>> is usually done for a PCA for example (the "loadings" as opposed to the 
>> eigenvectors).
>>     
>
> It is a least squares fit of the following form:
>
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v_coudrain | 15 Sep 18:07
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(no subject)

Hello,
I would like to post a message, but I was wondering if I had to subscribe first?
Here is my message:
II performed over the seanson a census of butterflies on 8 sites . I have for each date un number of
individuals and of species. I would like to know which function I could use to test if species number is
correlated to individual number.
Here is a view of my data: 
Number of individuals

P1I P2I P3I P4I P5I P8I 
[1,] 9.0 4.0 2.0 5.0 8 5.0 
[2,] 2.0 7.0 2.0 11.0 7 3.0 
[3,] 8.0 6.0 4.0 9.0 8 4.0 
[4,] 3.0 7.0 6.0 8.0 6 2.0 
[5,] 8.0 5.0 2.0 7.0 11 3.0 
[6,] 3.5 4.5 1.0 3.0 7 1.0 
[7,] 8.0 3.0 4.0 5.0 3 0.0 
[8,] 3.0 3.5 3.0 3.5 4 1.0 
[9,] 4.0 4.5 2.0 4.0 4 0.5 
[10,] 3.0 7.0 1.0 3.0 3 0.0 
[11,] 3.0 4.0 3.0 7.0 4 1.5 
[12,] 4.0 8.0 7.5 7.0 7 2.0 
[13,] 9.0 6.5 6.0 7.5 5 1.5 
[14,] 7.0 9.0 7.0 14.0 14 6.0 
[15,] 14.0 14.0 14.5 17.5 10 3.0 
[16,] 18.0 18.0 10.0 26.0 21 11.0 
[17,] 23.0 20.0 20.0 32.0 37 9.0 

number of species:
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Rainer M Krug | 16 Sep 11:53
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How to transform the parameter from SSlogis to "Capacity" and Growth rate"?

Hi

This might be a simple question, but I can't find an answer:

I have fitted a logistic model using

f <- nls( cove ~ SSlogis(year, Asym, xmid, scal), data = dat, model=TRUE)

and I get the following coefficients:

coef(f)
     Asym       xmid       scal
45248.8469  2008.2121     8.4025

Now, the "ecological" logistic growth has usually the coefficients
"Capacity" (K) and "growth rate" r (see e.g.
http://en.wikipedia.org/wiki/Logistic_function#In_ecology:_modeling_population_growth
).

Now it is obvious, (at least I hope ...) that Asym is equal to K.

But how can I get r?

Thanks,

Rainer

--

-- 
Rainer M. Krug, Centre of Excellence for Invasion Biology, Stellenbosch
University, South Africa
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Gmane