data("imdepi", "imdepifit")
load(system.file("shapes", "districtsD.RData", package="surveillance"))
## Fit an endemic-only twinstim()
m_noepi <- update(imdepifit, epidemic = ~0, siaf = NULL, model = TRUE,
T = 120) # using a restricted time range, for speed
## Simulate events from the above endemic model
set.seed(1)
s1 <- simEndemicEvents(m_noepi, tiles = districtsD)
class(s1) # just a "SpatialPointsDataFrame"
summary(s1@data)
plot(imdepi$W, lwd = 2, asp = 1)
plot(s1, col = s1$type, cex = 0.5, add = TRUE)
## Compare with the generic simulation method (slower)
s0 <- simulate(m_noepi, seed = 1, data = imdepi, tiles = districtsD)
class(s0) # gives a full "simEpidataCS" with several methods applicable
methods(class = "epidataCS")
plot(s0, "time")
plot(s0, "space", points.args = list(pch = 3), lwd = 2)
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