[R-sig-Geo] question about regression kriging

[ONKELINX, Thierry]

Dear Vanessa,

What residuals did you use? The ones in the original scale or in the logit scale? Interpolate the residuals in the logit scale and add these to the model predictions in the logit scale. And the transform those values back to the original scale. This will prevent values outside the 0-1 range.

Maybe you should have a loot at the geoRglm package.

HTH,

Thierry

----------------------------------------------------------------------------
ir. Thierry Onkelinx
Instituut voor natuur- en bosonderzoek / Research Institute for Nature and Forest
Cel biometrie, methodologie en kwaliteitszorg / Section biometrics, methodology and quality assurance
Gaverstraat 4
9500 Geraardsbergen
Belgium 
tel. + 32 54/436 185
Thierry.Onkelinx at inbo.be 
www.inbo.be 

To call in the statistician after the experiment is done may be no more than asking him to perform a post-mortem examination: he may be able to say what the experiment died of.
~ Sir Ronald Aylmer Fisher

The plural of anecdote is not data.
~ Roger Brinner

The combination of some data and an aching desire for an answer does not ensure that a reasonable answer can be extracted from a given body of data.
~ John Tukey

-----Oorspronkelijk bericht-----
Van: r-sig-geo-bounces at stat.math.ethz.ch [mailto:r-sig-geo-bounces at stat.math.ethz.ch] Namens Vanessa Stelzenmuller (Cefas)
Verzonden: woensdag 2 april 2008 11:32
Aan: r-sig-geo at stat.math.ethz.ch
Onderwerp: [R-sig-Geo] question about regression kriging

Hello,



We work on the application of regression kriging to presence / absence data in the context of species distribution modelling. In R in a first step we fit the trend surfaces with logistic regression models. Then we fit a variogram to the regression residuals and interpolate the residuals with OK. Now we face the situation that when combining trend surfaces with residual surfaces for some locations our occurrence probability is <0 or >1. Thus taking into account the spatial structure of the data (residuals)  has the potential to convert a predicted high occurrence probability into a low occurrence probability or vice versa. Are there some restriction for presence/ absence data for this approach? How to deal with these estimations (<0 and >1)?



Many thanks

Vanessa  





________________________________

Dr. Vanessa Stelzenmüller

Marine Scientist (GIS), CEFAS

Pakefield Road, Lowestoft, NR33 0HT, UK



Tel.: +44 (0)1502 527779



www.cefas.co.uk





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