Skip to content
Prev 2854 / 20628 Next
Original Raw

zero inflated and spatial autocorrelation

Alexandre VILLERS 
(sorry if the message was double posted...)

Good afternoon,

I currently try to estimate the relative contribution of habitat quality 
and a proxy of conspecific attraction for a bird species (3 years of 
data: 2000, 2004 and 2008) with a model of the form

Abundance (year t) ~ Abundance (year t-1) + Habitat_quality + Epsi

I would like being able to compare what happened between 2000-2004 to 
2004-2008
and would need some advices.

Problems are
1/ that the distribution of abundance (integer values) is zero inflated, 
with 80% of my grid cells "tagged" with 0
2/ I need to account for spatial autocorrelation (which is detectable 
below 10 km, my grid being 3 x 3 km in order to account for between 
years short dispersal).

I lack some skills in statistics to fully handle this on my own (even if 
I try to make up for lost time)
Shall I run straight ahead with a gamm model which is the only one, to 
my knowledge, that can account for both the spatial autocorrelation 
structure (with for example correlation=corSpher(form=~(X+Y))) and the 
zero inflated distribution (with family= negbin) ?
Or is there a way to relax some constraints on the distribution in order 
to handle overdispersion (induced by the ZI distribution) and thus trust 
the run of a glmmPQL with quasipoisson ?
 >From what I read, glmm and gamm are at the edge of statistics and need 
to be examined cautiously... so if I can avoid inserting irrelevant 
information in it...

Thanks for any help or link
Best regards


Alex


Tiebreaker: my observation window has an irregular shape, meaning that 
cells at the edge are truncated. If it is the correct solution to manage 
this, how do I properly specify weights=...  with the area of cells as 
argument.
Do I need to transform the value of areas ?
Or would it also be correct to exclude those piece of cells (which are 
mainly zeros --> 0 would then represent "only" 68% of observations, 
392/577 )?