# Quick Simulation from an Endemic-Only `twinstim`

`twinstim_simEndemicEvents.Rd`

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.

## 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)`

).

## 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)
}
```