Hi Ken,
I've also been using geoRglm for similar analyses, and found the prediction
to be a bit fickle when it comes to the prediction grids!
I was having similar problems to yours and the only thing that I could do to
make it work was to alter the way I made my prediction grids. How are you
creating yours?
What I was doing initially was creating the prediction points in ArcMap,
adding the covariate data to it and then exporting and importing to R which
didn't work.
I ended up changing my method, so I'd create the prediction points in R
using the pred_grid function, then export that and import the points to
ArcMap. Then I'd attach all the covariate data to the prediction points and
then export from ArcMap and import to R as a covariate object (and also use
the prediction points created using pred_grid as my prediction locations).
I hope this helps you a bit, but perhaps your problem is not the same as
mine was!
Nicola
Date: Wed, 04 Mar 2009 11:07:16 -0800
From: Ken Nussear <knuss...@mac.com>
Subject: [R-sig-Geo] imaging geoRglm binomial krig
To: r-sig-geo@stat.math.ethz.ch
Message-ID: <1898d59a-04af-4625-b19c-e8daca88a...@mac.com>
Content-Type: text/plain; charset=US-ASCII; format=flowed; delsp=yes
Hi
Wondering if anyone has had success imaging a krige produced using
geoRglm?
The dataset has 3467 locations....
Here is the code used to build the krige and attempted image.
itdsglm1 <- trend.spatial(~TRAN_LNTH + Maxent + HWYS_Dist2 + MDEP +
pop, WMgeo.Sign)
itlsglm1 <- trend.spatial(~TRAN_LNTH + Maxent + HWYS_Dist2 + MDEP +
pop, WMgeo.kriglocs)
kmod <- model.glm.control(trend.d = itdsglm1, trend.l = itlsglm1,
cov.model = "exponential")
kprior <- prior.glm.control(beta.prior = "flat", sigmasq.prior =
"fixed", sigmasq= 0.0307, phi.prior = "fixed", phi = 7748,
phi.discrete = NULL, tausq.rel = 5.276)
bkcontrl <- mcmc.control(S.scale = .07, thin=40, phi.start= 7748,
phi.scale = .2, burn.in=10)
kgout <- output.glm.control(sim.posterior=T, sim.predict=T,
keep.mcmc.sim=T, inference=T, messages=T, quantile=c(0.25,0.5,0.975))
bkb <- binom.krige.bayes(WMgeo.Sign, locations = WMgeo.kriglocs
$coords, model= kmod, mcmc.input= bkcontrl, prior=kprior)
image(bkb, locations=WMgeo.kriglocs$coords,
values.to.plot='simulation', number.col=1, messates=T)
image.glm.krige.bayes(bkb, locations=WMgeo.kriglocs$coords,
values.to.plot=c("median"), number.col=1, coords.data=WMgeo.kriglocs
$coords, x.leg=c(410312,561312), y.leg=c(3822651,3920651), messages=T)
I get the following errors and I can't figure out what it wants....
mapping the medians of the predictive distribution
Error in image.default(x = c(410311.919949, 411311.919949,
412311.919949, :
dimensions of z are not length(x)(-1) times length(y)(-1)
In addition: Warning messages:
1: In if (coords.data) coords.data <- eval(attr(x, "data.locations")) :
the condition has length > 1 and only the first element will be used
2: In matrix(values.loc, ncol = ny) :
data length [3467] is not a sub-multiple or multiple of the number
of rows [36]
Ken
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Scotland, with registration number SC005336.
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