Hi R-list,

I am constructing a Bayesian model in openBugs, and am using glmmBUGS to
generate initials for the simulations.

The dependent variable is the presence and absence of a species (i.e., 0 or
1).

The independent variables include

(1) continuous variables: Ele (elevation), Slp (channel Slope), VFW (valley
floor width);

(2) categorical variables: presence and absence of nitrogen-fixing bacteria
(i.e., Cyan: 1, 2), channel type (chanType:  1, 2, 3), and presence and
absence of riparian vegetation (ripVeg: 1,2).

(3) Spatial autocorrelation between points. There are 675 points. All the
dependent/independent variables are at point level (observational level,
675 points in total). One observation for each point. I assigned ID for
each point, called "dummy", which is from 1 to 675. "poly_yr1_nb" gives the
neighbor matrix, generated from shapefile using poly2nb. The name of the
dataset is "y1". The codes are below:


I tried this:

forBugs_sp=glmmBUGS(sp ~ Ele+Slp+VFW,reparam=c("chanType","Cyan","ripVeg"),

                effects="dummy",family="bernoulli",spatial=poly_yr1_nb,

                spatialEffect="dummy",

                data=y1)


It was able to run 10 interations, and gave me the result with Warning
message:

In spatialBugs.list(map, raggedArray, effect, prefix, regionID) :

 some random effects don't appear to be regions


However, the result looks absolutely wrong. The coefficients for Ele, Slp,
and VFW are at the magnitude of 10^15. i have results from openBUGS already
without spatial autocorrelation, and am sure glmmBUGS are giving me
incorrect results. What I want to get is the estimate for the spatial
random effect for each point, so that I can use that for initial values in
the CAR.NORMAL model in openBugs.


Any help is greatly appreciated!


Thank you,

Xiaoli

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