Hi BUGS users,
I have data consisting of a bivariate response (an overdispersed
Poisson variable and an overdispersed Binomial variable), together with
6 explanatory variables, measured at 90 irregularly spaced points in a
geographical region. There's no problem setting up an appropriate
hierarchical generalized linear model for each response separately, with
error terms {e_i, i = 1, ..., 90} to reflect spatial dependence.
However, I have the following two questions:
(1) The nature of the problem is such that a geostatistical
covariance structure, e.g.,
corr(e_i, e_j) = exp{ -gamma * dist(i, j) }
seems more appropriate than a conditional autoregressive model.
However, I've read that such models can take a VERY long time to run in
BUGS. Has anyone had experience with a problem of this size?
(2) Since the response is bivariate, I should really model both
components simultaneously. Has someone had experience modeling
bivariate spatially distributed responses in BUGS, with either
autoregressive or explicitly defined covariance structures?
Thanks,
Sam Oman
--
Professor Samuel D. Oman
Department of Statistics
Hebrew University of Jerusalem
Mount Scopus, Jerusalem
91905 Israel
telephone: +972 2 5883 442
facsimile: +972 2 5883 549
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