# Update method for `"epidataCS"`

`epidataCS_update.Rd`

The `update`

method for the `"epidataCS"`

class
may be used to modify the hyperparameters \(\epsilon\) (`eps.t`

)
and \(\delta\) (`eps.s`

), the indicator matrix `qmatrix`

determining
possible transmission between the event types, the numerical
accuracy `nCircle2Poly`

of the polygonal approximation, and
the endemic covariates from `stgrid`

(including the time intervals).
The update method will also update the auxiliary information contained
in an `"epidataCS"`

object accordingly, e.g., the vector of potential
sources of each event, the influence regions, or the endemic covariates
copied from the new `stgrid`

.

## Usage

```
# S3 method for class 'epidataCS'
update(object, eps.t, eps.s, qmatrix, nCircle2Poly, stgrid, ...)
```

## Arguments

- object
an object of class

`"epidataCS"`

.- eps.t
numeric vector of length 1 or corresponding to the number of events in

`object$events`

. The event data column`eps.t`

specifies the maximum temporal influence radius (e.g., length of infectious period, time to culling, etc.) of the events.- eps.s
numeric vector of length 1 or corresponding to the number of events in

`object$events`

. The event data column`eps.s`

specifies the maximum spatial influence radius of the events.- qmatrix
square indicator matrix (0/1 or TRUE/FALSE) for possible transmission between the event types.

- nCircle2Poly
accuracy (number of edges) of the polygonal approximation of a circle.

- stgrid
a new

`data.frame`

with endemic covariates, possibly transformed from or adding to the original`object$stgrid`

. The grid must cover the same regions as the original, i.e.,`levels(object$stgrid$tile)`

must remain identical. See`epidataCS`

for a detailed description of the required format.- ...
unused (argument of the generic).

## See also

class `"epidataCS"`

.

## Examples

```
data("imdepi")
## assume different interaction ranges and simplify polygons
imdepi2 <- update(imdepi, eps.t = 20, eps.s = Inf, nCircle2Poly = 16)
(s <- summary(imdepi))
(s2 <- summary(imdepi2))
## The update reduced the number of infectives (along time)
## because the length of the infectious periods is reduced. It also
## changed the set of potential sources of transmission for each
## event, since the interaction is shorter in time but wider in space
## (eps.s=Inf means interaction over the whole observation region).
## use a time-constant grid
imdepi3 <- update(imdepi, stgrid = subset(imdepi$stgrid, BLOCK == 1, -stop))
(s3 <- summary(imdepi3)) # "1 time block"
```