# Plotting Intensities of Infection over Time or Space

`twinstim_intensity.Rd`

`intensityplot`

method to plot the evolution of the total infection
intensity, its epidemic proportion or its endemic proportion over time
or space (integrated over the other dimension) of fitted
`twinstim`

models (or `simEpidataCS`

).
The `"simEpidataCS"`

-method is just a wrapper around
`intensityplot.twinstim`

by making the `"simEpidataCS"`

object
`"twinstim"`

-compatible, i.e. enriching it by the
required model components and environment.

The `intensity.twinstim`

auxiliary function returns functions which
calculate the endemic or epidemic intensity at a specific time point or
location (integrated over the other dimension).

## Usage

```
# S3 method for twinstim
intensityplot(x,
which = c("epidemic proportion", "endemic proportion", "total intensity"),
aggregate = c("time", "space"), types = 1:nrow(x$qmatrix),
tiles, tiles.idcol = NULL, plot = TRUE, add = FALSE,
tgrid = 101, rug.opts = list(),
sgrid = 128, polygons.args = list(), points.args = list(),
cex.fun = sqrt, ...)
# S3 method for simEpidataCS
intensityplot(x, ...)
intensity.twinstim(x,
aggregate = c("time", "space"), types = 1:nrow(x$qmatrix),
tiles, tiles.idcol = NULL)
```

## Arguments

- x
an object of class

`"twinstim"`

or`"simEpidataCS"`

, respectively.- which
`"epidemic proportion"`

,`"endemic proportion"`

, or`"total intensity"`

. Partial matching is applied. Determines whether to plot the path of the total intensity or its epidemic or endemic proportions over time or space (`which`

) aggregated over the other dimension and`types`

.- aggregate
One of

`"time"`

or`"space"`

. The former results in a plot of the evolution of`which`

as a function of time (integrated over the observation region \(\bold{W}\)), whereas the latter produces a`spplot`

of`which`

over \(\bold{W}\) (spanned by`tiles`

). In both cases,`which`

is evaluated on a grid of values, given by`tgrid`

or`sgrid`

, respectively.- types
event types to aggregate. By default, all types of events are aggregated, but one could also be interested in only one specific type or a subset of event types.

- tiles
object of class

`SpatialPolygons`

representing the decomposition of \(\bold{W}\) into different regions (as used in the corresponding`stgrid`

of the`"epidataCS"`

. This is only needed for`aggregate = "space"`

.- tiles.idcol
either a column index for

`tiles@data`

(if`tiles`

is a`SpatialPolygonsDataFrame`

), or`NULL`

(default), which refers to the`"ID"`

slot of the polygons, i.e.,`row.names(tiles)`

. The ID's must correspond to the factor levels of`stgrid$tile`

of the`"epidataCS"`

on which`x`

was fitted.- plot
logical indicating if a plot is desired, which defaults to

`TRUE`

. Otherwise, a function will be returned, which takes a vector of time points (if`aggregate = "time"`

) or a matrix of coordinates (if`aggregate = "space"`

), and returns`which`

on this grid.- add
logical. If

`TRUE`

and`aggregate = "time"`

, paths are added to the current plot, using`lines`

. This does not work for`aggregate = "space"`

.- tgrid
either a numeric vector of time points when to evaluate

`which`

, or a scalar representing the desired number of evaluation points in the observation interval \([t_0, T]\). This argument is unused for`aggregate = "space"`

.- rug.opts
if a list, its elements are passed as arguments to the function

`rug`

, which will mark the time points of the events if`aggregate = "time"`

(it is unused in the spatial case); otherwise (e.g.,`NULL`

), no`rug`

will be produced. By default, the`rug`

argument`ticksize`

is set to 0.02 and`quiet`

is set to`TRUE`

. Note that the argument`x`

of the`rug`

function, which contains the locations for the`rug`

is fixed internally and can not be modified.- sgrid
either an object of class

`"SpatialPixels"`

(or coercible to that class) representing the locations where to evaluate`which`

, or a scalar representing the approximate number of points of a grid constructed on the bounding box of`tiles`

.`sgrid`

is internally subsetted to contain only points inside`tiles`

. This argument is unused for`aggregate = "time"`

.- polygons.args
if a list, its elements are passed as arguments to

`sp.polygons`

, which will add`tiles`

to the plot if`aggregate = "space"`

(it is unused for the temporal plot). By default, the fill`col`

our of the tiles is set to`"darkgrey"`

.- points.args
if a list, its elements are passed as arguments to

`sp.points`

, which will add the event locations to the plot if`aggregate = "space"`

(it is unused for the temporal plot). By default, the plot symbol is set to`pch=1`

. The sizes of the points are determined as the product of the argument`cex`

(default: 0.5) of this list and the sizes obtained from the function`cex.fun`

which accounts for multiple events at the same location.- cex.fun
function which takes a vector of counts of events at each unique location and returns a (vector of)

`cex`

value(s) for the sizes of the points at the event locations used in`points.args`

. Defaults to the`sqrt()`

function, which for the default circular`pch=1`

means that the area of each point is proportional to the number of events at its location.- ...
further arguments passed to

`plot`

or`lines`

(if`aggregate = "time"`

), or to`spplot`

(if`aggregate = "space"`

).

For`intensityplot.simEpidataCS`

, arguments passed to`intensityplot.twinstim`

.

## Value

If `plot = FALSE`

or `aggregate = "time"`

,
a function is returned, which takes a vector of
time points (if `aggregate = "time"`

) or a matrix of coordinates
(if `aggregate = "space"`

), and returns `which`

on this grid.

`intensity.twinstim`

returns a list containing such functions for
the endemic and epidemic intensity (but these are not vectorized).

If `plot = TRUE`

and `aggregate = "space"`

, the

`trellis.object`

of the spatial plot is returned.

## See also

`plot.twinstim`

, which calls `intensityplot.twinstim`

.

## Examples

```
data("imdepi", "imdepifit")
# for the intensityplot we need the model environment, which can be
# easily added by the intelligent update method (no need to refit the model)
imdepifit <- update(imdepifit, model=TRUE)
## path of the total intensity
opar <- par(mfrow=c(2,1))
intensityplot(imdepifit, which="total intensity",
aggregate="time", tgrid=500)
plot(imdepi, "time", breaks=100)
par(opar)
## time course of the epidemic proportion by event
intensityplot(imdepifit, which="epidemic proportion",
aggregate="time", tgrid=500, types=1)
intensityplot(imdepifit, which="epidemic proportion",
aggregate="time", tgrid=500, types=2, add=TRUE, col=2)
legend("topright", legend=levels(imdepi$events$type), lty=1, col=1:2,
title = "event type")
## endemic and total intensity in one plot
intensity_endprop <- intensityplot(imdepifit, which="endemic proportion",
aggregate="time", plot=FALSE)
intensity_total <- intensityplot(imdepifit, which="total intensity",
aggregate="time", tgrid=501, lwd=2)
curve(intensity_endprop(x) * intensity_total(x), add=TRUE, col=2, lwd=2, n=501)
text(2500, 0.36, labels="total", col=1, pos=2, font=2)
text(2500, 0.08, labels="endemic", col=2, pos=2, font=2)
## spatial shape of the intensity (aggregated over time)
if (surveillance.options("allExamples")) {
## load borders of Germany's districts
load(system.file("shapes", "districtsD.RData", package="surveillance"))
# total intensity (using a rather sparse 'sgrid' for speed)
intensityplot(imdepifit, which="total intensity",
aggregate="space", tiles=districtsD, sgrid=500,
col.regions=rev(heat.colors(100)))
# epidemic proportion by type
maps_epiprop <- lapply(1:2, function (type) {
intensityplot(imdepifit, which="epidemic", aggregate="space",
types=type, tiles=districtsD, sgrid=1000,
main=rownames(imdepifit$qmatrix)[type],
scales=list(draw=FALSE), at=seq(0,1,by=0.1),
col.regions=rev(hcl.colors(10,"YlOrRd")),
colorkey=list(title=list("Epidemic proportion", cex=1)))
})
plot(maps_epiprop[[1]], split=c(1,1,2,1), more=TRUE)
plot(maps_epiprop[[2]], split=c(2,1,2,1))
}
```