Dear David,

the autocovariate calculated from the response yielded consistently 
biased results as Dormann and colleagues showed. The RAC approach seem 
to be much better. Nevertheless, you need to calculate dthe residual 
autocovariate from the model you are going to use, i.e., RAC of the BM 
for the BM and RAC of the PM for the PM. So you will need two 
autocovariates for the two approaches.

Kind regards
Ingolf


Am 21.01.2014 13:38, schrieb David March Morla:
> Dear all,
>
> Currently, I am considering analysing a highly skewed (zero-inflated) 
> count georeferenced-data by combining two published approaches that 
> made use of boosted regression trees. The first one (Leathwick et al. 
> 2008 ), follows a delta-log-normal approach, whereas the second (Crase 
> et al. 2007) introduces the concept of residual autocovariate (RAC) on 
> binary data. I would like to incorporate the RAC approach with a 
> delta-poisson approach. However, although a previous paper (Dormann et 
> al. 2007) suggests that autocovariate regression could also be 
> extended to any type of data (eg. Poisson or Gaussian errors), the 
> same authors alert about its correctness.
>
> Then, my main thoughts are:
> -    Is there any potential problem for extending the RAC approach to 
> Poisson or Gaussian distributions?
> -    If there is no major drawback, what should be the best way to 
> implement the RAC into a delta approach that consists in a binary 
> model (BM) for presence/absence and a poisson model (PM) for only 
> positive counts? My first idea is to calculate the residuals from the 
> delta (BMxPM), estimate the autocovariate (RAC), and include this new 
> term to both the BM and PM models. However, I am not sure if I should 
> need to calculate different autocovariates for the BM (ie. RAC from 
> the BM) and PM (RAC from BMxPM) respectively.
>
> Any comments or suggestion about these issues would be more than welcome.
>
> Kind regards,
> David March
>
> References:
> Crase et al 2012. A new method for dealing with residual spatial 
> autocorrelation in species distribution models. Ecography, 35: 879-888
> Dormann et al. 2007. Methods to account for spatial autocorrelation in 
> the analysis of species distributional data:  a review. Ecography, 30: 
> 609-628.
> Leathwick et al. 2008. Novel methods for the design and evaluation of 
> marine protected in offshore waters. Conservation Letters, 1: 91-102
>

-- 
--------------------------------------------------------------------------------------------
Dr. Ingolf Kühn
Head of Macroecology Group
Dept. Community Ecology

Helmholtz Centre for Environmental Research GmbH - UFZ	
Theodor-Lieser-Str. 4 / 06120 Halle / Germany
Phone +49 345 558 5311 / Fax +49 341 235 455311
ingolf.kuehn@ufz.de / www.ufz.de


The following is required by German law - we love this ;-)

Sitz der Gesellschaft: Leipzig
Registergericht: Amtsgericht Leipzig, Handelsregister Nr. B 4703
Vorsitzender des Aufsichtsrats: MinDirig Wilfried Kraus
Wissenschaftlicher Geschäftsführer: Prof. Dr. Georg Teutsch
Administrativer Geschäftsführer: Dr. Heike Graßmann
						
--------------------------------------------------------------------------------------------
NeoBiota Journal: http://www.pensoft.net/journals/neobiota
Home: http://www.ufz.de/index.php?en=821
--------------------------------------------------------------------------------------------


	[[alternative HTML version deleted]]

