[R-sig-Geo] Comparing abundances at fixed locations in space - Syrjala test

[Paulo Justiniano Ribeiro Jr]

Just to add to Barry email that geoRglm code
can indeed be used just to access covariate effects, without necessarily
perform any spatial interpolation.
Typicallyin tis implementation this will be via Bayesian framework
obtaining samples from the posterior distribution of the coefficients.


Paulo Justiniano Ribeiro Jr
LEG (Laboratorio de Estatistica e Geoinformacao)
Universidade Federal do Parana
Caixa Postal 19.081
CEP 81.531-990
Curitiba, PR  -  Brasil
Tel: (+55) 41 3361 3573
Fax: (+55) 41 3361 3141
e-mail: paulojus AT  ufpr  br
http://www.leg.ufpr.br/~paulojus

-------------------------------------------------------------------------
53a Reuniao Anual da Regiao Brasileira da Soc. Internacional de Biometria
14 a 16/05/2008, UFLA, Lavras,MG
http://www.rbras.org.br/rbras53
-------------------------------------------------------------------------


On Mon, 11 Feb 2008, Barry Rowlingson wrote:

> Jean-Olivier Irisson wrote:
>
> > Thank you for the pointer. The vignette of geoRglm seems promising,
> > though much is about prediction from a given model while I am most
> > interested in which terms are in the model, i.e. which variables have a
> > notable influence on the repartition of the organisms. My scenario seems
> > simpler than those presented however, since the data are standardized by
> > the sampling effort, meaning that the same Poisson law applies to all
> > points.
>
>   I think you still need to fit a model, and then you can test how
> useful your covariates are with standard techniques.
>
> > A continuous variable than would represent the spatiality in this
> > dataset could simply be the distance from the lower-left corner of the
> > sampling grid for example, or the distance from the island around which
> > the sampling grid is designed (such a distance would have a biological
> > meaning since we expect the abundances to be inversely proportional to
> > it). Is that something that could fit your definition of a
> > "smoothly-varying spatial error term" or am I completely mistaken?
>
>   Think about fitting a straight line through some points. You find the
> line that best fits your points. Then you look at the residual
> differences between the line and your points. All the usual linear model
> theory about predictions and significance depends on those residuals
> being uncorrelated and independent. If you are fitting a straight line
> to a curve then that won't be true, and if you then say something about
> your straight line based on the linear model theory you'll be wrong.
>
>   Now, you could fit a non-spatial generalised linear model to your data
> using glm() in R and then map the residuals. If the residual map shows
> structure, then there's something else going on that your model hasn't
> accounted for. Perhaps there is an obvious trend due to a covariate
> you've not included, such as elevation above sea level. You could then
> add this to your model. If the residual surface looks like random noise
> then you can use standard linear model theory to make conclusions about
> your covariate parameters.
>
>   If the residual surface doesn't look like random noise then that's
> when you get into geoRglm functions which (I think) fit a GLM where the
> error surface (that's your residuals) is defined by a gaussian random
> field with a fitted covariance structure. Once that's done, the geoRglm
> code will tell you about your covariate parameter significance (I think!
> It's been a while since I've used it. Maybe Paulo and Ole can expand on
> this).
>
>   So what I'd do is:
>
>   * fit a simple GLM using glm.
>   * Look at parameter estimates and significance.
>   * Draw a map of residuals.
>   * Then worry about spatial correlation.
>
>   Oh, I'd also, if I were you, try and find a local statistician expert!
>
> Barry
>
> _______________________________________________
> R-sig-Geo mailing list
> R-sig-Geo at stat.math.ethz.ch
> https://stat.ethz.ch/mailman/listinfo/r-sig-geo
>