On Thu, May 20, 2010 at 3:20 PM, Lucia Rueda <lucia.rueda@ba.ieo.es> wrote:

>
> Hi,
>
> Thanks for the inputs. I talked to my coworker, who has been the one doing
> the analysis. Perhaps I wasn't making myself clear about the “differences
> in
> spatial scales”.  Here is what he says:
>
> "The truth is that measuring scales (i.e all area related variable are
> measured in m2) and spatial definition of initial cartography are
> homogeneous among extracted variables. But all variables (ie. sum of the
> total rocky bottom in the surrounding area) are computed for each different
> integration areas (buffer) (i.e in an area of 40squaremeters around the
> sample, in an area of 80m2, …).
> The question is then if we can build a model that includes variables
> measured at different buffers (for example a model that includes 3
> variables:  1.-  the amount of rocky bottom in an area of 80m2 ; 2- the
> amount of sandy bottom in an area of 200m2; and the mean depth calculated
> in
> a surrounding area of 50m2) considering that each variable may be
> expressing
> different ecological processes. I believe that if there is not an
> ecological
> constrain in the interpretation of the variables (and their ecological
> effect over the specie), including them in a model is correct, unless there
> is not a mathematical constrain."
>

If you look upon it that way, you might indeed consider using them in
different buffers, but as you said, you should be able to interprete them in
an ecological way. I'd be surprised if depth and bottom have a different
effect-scale, as they both are related to the territorium of the animal.
Plus, you cannot conclude anything from the difference in deviance
explained. You can't say anything about the homerange or so based on the
observation that more deviance is explained when looking on a scale of 200m2
for example. So if you have good ecological reasons to include them, you
can, but if it's merely because on one scale they explain more of the
deviance, I still believe it is a very dangerous approach...


> Also, about the spatial correlation I thought from what I've read so far
> that I had to build the model and then check if there was spatial
> correlation in the residuals since they are supposed to be i.i.d. And if it
> turns out that they are then I have to do something about it like gamm,
> gee,
> sar, car, etc.
>
That's an approach that is often used. In essence, that's true. Correlation
between the raw data can be due to cocorrelation with some other factor in
space or time. But a pre-analysis of correlations and autocorrelations can
tell you already quite some. In any case, you always have to check the
residuals after the model building. My main point was that using the
correlation will definitely influence the significance of the parameters.

Anyway, good luck with it. I learnt pretty fast that as long as you can
explain what you're doing and why you're doing it, there's a big grey zone
between right and wrong. Otherwise it wouldn't be statistics, would it? ;-)

Cheers
Joris

>
> Cheers,
>
> Lucia
>
>
> --
> View this message in context:
> http://r.789695.n4.nabble.com/offset-in-gam-and-spatial-scale-of-variables-tp2222483p2224528.html
> Sent from the R help mailing list archive at Nabble.com.
>
> ______________________________________________
> R-help@r-project.org mailing list
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide
> http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.
>



-- 
Joris Meys
Statistical Consultant

Ghent University
Faculty of Bioscience Engineering
Department of Applied mathematics, biometrics and process control

Coupure Links 653
B-9000 Gent

tel : +32 9 264 59 87
Joris.Meys@Ugent.be
-------------------------------
Disclaimer : http://helpdesk.ugent.be/e-maildisclaimer.php

	[[alternative HTML version deleted]]

