| diseasemapping-package {diseasemapping} | R Documentation |
Functions for calculating observed and expected counts by region, and manipulating posterior samples from Bayesian models produced by glmmBUGS.
Patrick Brown
# creating SMR's
data(popdata)
data(casedata)
model = getRates(casedata, popdata, ~age*sex, breaks=seq(0, 90, by=10) )
ontario = getSMR(popdata,model, casedata)
## Not run:
spplot(ontario, 'SMR')
## End(Not run)
# an example of a spatial model with glmmBUGS
## Not run:
# run the model
library(spdep)
popDataAdjMat = poly2nb(ontario, ontario[["CSDUID"]])
library(glmmBUGS)
forBugs = glmmBUGS(formula=observed + logExpected ~ 1,
effects="CSDUID", family="poisson", spatial=popDataAdjMat,
data=ontario@data)
startingValues = forBugs$startingValues
source("getInits.R")
library(R2WinBUGS)
ontarioResult = bugs(forBugs$ragged, getInits, parameters.to.save = names(getInits()),
model.file="model.bug", n.chain=3, n.iter=100, n.burnin=10, n.thin=2,
program="winbugs", debug=TRUE)
data(ontarioResult)
ontarioParams = restoreParams(ontarioResult, forBugs$ragged)
ontarioSummary = summaryChain(ontarioParams)
# merge results back in to popdata
ontario = mergeBugsData(ontario, ontarioSummary)
## End(Not run)
# running the same thing with INLA