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) spplot(ontario, 'SMR') # 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