Hi everyone,
I'm recently struggling with exploring a model to forecast the
schistosomiasis risk. Let me introduce the data I have first.
Parasitological data, including number of infected individuals and
population at risk, are available in a province from 1997 to 2010 at the
county level (i.e.,
Hi everyone,
I have a simple problem, which I know will have a simple solution, but I
just can't tackle it.
I have a shapefile with regions, including an attribute containing a
regional id. I used the following R script to create a neighbours list:
anhui <- readShapePoly("endemic.shp")
zzz<- po
Thanks, Corey. That's really helpful. By the way, have u got the R codes from
Edzer? :)
Yi
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Thanks, Corey, that's really helpful. By the way, do u get the R codes from
Edzer? :)
Yes, you can choose a zero inflated poisson or negative binomial likelihood.
Also, I had asked about a similar question a while back and Edzer replied
with this:
http://r-sig-geo.2731867.n2.nabble.com/Questio
Thanks Corey. Yeah, what you mentioned is indeed the problem. However, does
INLA can deal with the problem of many "zero" values if a count data model
is assumed?
>You need to be careful with the incidence data if the Gaussian model isn't
appropriate, I assume that many >counties have very low p
Thanks, Roger. I will try.
It's my fault to mistakenly write the spautolm function as a package.
what do mean by saying "if your space-time data are balanced". My data is
multiple years of prevalence data of schistosomiasis at the county level,
combined with data of environmental determinants.
Hi everyone,
It seems that there is no specific R package to implement the space-time SAR
model. I happend to find an article entitled "Spatio-temporal regression
models for deforestation in the Brazilian Amazon" (literature information:
STDM 2011, The International Symposium on Spatial-Temporal A