Dear List,
I have been using geoRglm to perform spatial prediction in the binomial-logit
spatial model.
I have been able to produce maps of some predicted volumetric quantity
(prevalence) over a wide geographic area (2385km * 1375km in 131,175 5km cells)
, using covariates in the fixed part of the model. Using the function
binom.krige.bayes produces output which includes an estimate of the predictive
median for each cell in the map. This is specified when setting
'inference=TRUE' in the control function called 'output.glm.control' which is
called by binom.krige.bayes.
However the median is only one map of the posterior distribution of the
estimated quantity. I want to look at some of the other maps, including the
mean map of the predicted. In order to do this I need to get the posterior
prediction of the prevalence for each map cell for each iteration of the
simulation. Then for each cell I can calculate the mean etc. In order to do
this there is a TRUE/FALSE switch called 'sim.predict' in the control function
'output.glm.control'. When set to false you simply return the predictive
median. When set to TRUE you should return the values of the predictied
quantity for each iteration of the model. When I set this to true RGui crashes
with no warning or error messages (even when verbose mode is turned on). R is
not using it vector or stack heaps, and I am only using 8GB out of 16GB of
system ram. Also the table which should be dimensioned to hold the predictions
is well inside the limits placed by R (i.e. a table with less than 2^31-1
elements!
). I am running 150,000 iterations of a model , predicting over 131,175 map
cells, and I have thinning set to 100 (to minimise autocorrelation between
simulations), so I should be recording 1500 simulation results (150,000 / 100).
Has this problem happened to anybody else and does anybody know of another way
to record the predictive posterior draws of the simulations?
Much obliged and sorry if I have not used appropriate jargon in places - I am
relatively new to this.
Cheers,
Simon
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