In endemic-only twinstim models, the conditional intensity is a piecewise constant function independent from the history of the process. This allows for a much more efficient simulation algorithm than via Ogata's modified thinning as in the general simulate.twinstim method.

## Usage

simEndemicEvents(object, tiles)

## Arguments

object

an object of class "twinstim" (with the model component retained; otherwise try object <- update(object, model = TRUE)).

tiles

an object inheriting from "SpatialPolygons", which represents the tiles of the original data's stgrid (see, e.g., levels(environment(object)$gridTiles)). ## Value a SpatialPointsDataFrame ## Author Sebastian Meyer ## See also the general simulation method simulate.twinstim ## Examples data("imdepi", "imdepifit") load(system.file("shapes", "districtsD.RData", package="surveillance")) ## Fit an endemic-only twinstim() m_noepi <- update(imdepifit, epidemic = ~0, siaf = NULL, model = TRUE, T = 120) # using a restricted time range, for speed ## Simulate events from the above endemic model set.seed(1) s1 <- simEndemicEvents(m_noepi, tiles = districtsD) class(s1) # just a "SpatialPointsDataFrame" summary(s1@data) plot(imdepi$W, lwd = 2, asp = 1)
plot(s1, col = s1\$type, cex = 0.5, add = TRUE)

if (surveillance.options("allExamples")) {
## the general simulation method takes longer
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)
}