Dear Karen,
seconding the comments of Phil and Etienne: One key question is whether
you can assume no error on the values of your predictors (i.e. run a
model 1-regression). If you can, Ben Bolker's comments point in the
right way; if you cannot, my heart goes out for the "simplistic"
approach of Etienne and try to pad your results with a bit of
"robustness testing".
(E.g. perturb/jitter your values and see if it makes a difference to
your regression. This may not be "official" stats, but should show clear
differences when the pattern is not robust. For example, the many 0s in
your data may be caused by detection problems (rather than true
absences) and hence giving them a random low cover/abundance (e.g. 1/2
of the respective minimum value) should NOT change your results. If it
does, I would interpret this as the data not supporting a clear
correlation between abundance and cover.)
HTH,
Carsten
On 26.10.10 11:27, Karen Kotschy wrote:
Dear list
This seems like something I really should know by now, but I'm getting so
confused, I'd really appreciate a little help!
I am trying to model the relationship between relative abundance (%) and
relative cover (%) data for plant species. I want to know to
what extent the 2 measures correlate, and to compare the extent of this
correlation at different sites. Obviously, both sets of data are
zero-inflated and highly skewed.
The "traditional" thing to do would be to log-transform both of them and
use lm(). However, a recent paper (O'Hara& Kotze, 2010) argues that a
much better approach is to use glm() and to specify Poisson or negative
binomial models, rather than using transformations. This does make a lot
of sense, I think!
I have tried using "quasipoisson" and "quasibinomial" families in glm(),
but I am left with a number of questions:
1) Should relative abundance and relative cover be treated as "count"
data, given that the values are not actually integers but rather
percentages?
2) Which parts of the output of glm(...family=quasipoisson(link=log)) do I
use to evaluate the fit? Just residual deviance and the p value?
3) How do I plot the data so as to graphically represent the model? If I
am using a log link should I use log axes for x and y?
Thanks so much for any help!
Karen
---
Karen Kotschy
Centre for Water in the Environment
University of the Witwatersrand, Johannesburg
Tel: +2711 717-6425
--
Dr. Carsten F. Dormann
Department of Computational Landscape Ecology
Helmholtz Centre for Environmental Research-UFZ
(Department Landschaftsökologie)
(Helmholtz Zentrum für Umweltforschung - UFZ)
Permoserstr. 15
04318 Leipzig
Germany
Tel: ++49(0)341 2351946
Fax: ++49(0)341 2351939
Email: carsten.dorm...@ufz.de
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