# update.ppm

##### Update a Fitted Point Process Model

`update`

method for class `"ppm"`

.

##### Usage

```
## S3 method for class 'ppm':
update(object, \dots, fixdummy=TRUE, use.internal=NULL)
```

##### Arguments

- object
- An existing fitted point process model,
typically produced by
`ppm`

. - ...
- Arguments to be updated in the new call to
`ppm`

. - fixdummy
- Logical flag indicating whether the quadrature scheme for the
call to
`ppm`

should use the same set of dummy points as that in the original call. - use.internal
- Optional. Logical flag indicating whether the model should be
refitted using the internally saved data (
`use.internal=TRUE`

) or by re-evaluating these data in the current frame (`use.internal=FALSE`

).

##### Details

This is a method for the generic function `update`

for the class `"ppm"`

. An object of class `"ppm"`

describes a fitted point process model. See `ppm.object`

)
for details of this class.

`update.ppm`

will modify the point process model
specified by `object`

according to the new arguments given,
then re-fit it.
The actual re-fitting is performed by the model-fitting
function `ppm`

.

If you are comparing several model fits to the same data,
or fits of the same model to different data, it is
strongly advisable to use `update.ppm`

rather than trying to fit them by hand.
This is because `update.ppm`

re-fits the model
in a way which is comparable to the original fit.

The arguments `...`

are matched to the formal arguments
of `ppm`

as follows.

First, all the *named* arguments in `...`

are matched
with the formal arguments of `ppm`

.
Use `name=NULL`

to remove the argument `name`

from the
call.

Second, any *unnamed* arguments in `...`

are
matched with formal arguments of `ppm`

if the matching
is obvious from the class of the object. Thus `...`

may contain

- exactly one argument of class
`"ppp"`

or`"quad"`

, which will be interpreted as the named argument`Q`

; - exactly one argument of class
`"formula"`

, which will be interpreted as the named argument`trend`

; - exactly one argument of class
`"interact"`

, which will be interpreted as the named argument`interaction`

; - exactly one argument of class
`"data.frame"`

, which will be interpreted as the named argument`covariates`

.

When `fixdummy=FALSE`

, calling `update.ppm`

is exactly the same as calling `ppm`

with the updated
arguments. However, the original and updated models
are not strictly comparable (for example, their pseudolikelihoods
are not strictly comparable) unless they used the same set of dummy
points for the quadrature scheme. Setting `fixdummy=TRUE`

ensures that the re-fitting will be performed using the same set
of dummy points. This is highly recommended.

The value of `use.internal`

determines where to find data
to re-evaluate the model (data for the arguments mentioned in
the original call to `ppm`

that are not overwritten by
arguments to `update.ppm`

).
If `use.internal=FALSE`

, then arguments
to `ppm`

are *re-evaluated* in the frame where you
call `update.ppm`

. This is like the behaviour of the
other methods for `update`

. This means that if you have changed
any of the objects referred to in the call, these changes will be
taken into account. Also if the original call to `ppm`

included
any calls to random number generators, these calls will be recomputed,
so that you will get a different outcome of the random numbers.

If `use.internal=TRUE`

, then arguments to `ppm`

are extracted
from internal data stored inside the current fitted
model `object`

. This is useful if you don't want to
re-evaluate anything. It is also necessary if
if `object`

has been restored from a dump file
using `load`

or `source`

. In such cases,
we have lost the environment in which `object`

was fitted,
and data cannot be re-evaluated.

By default, if `use.internal`

is missing, `update.ppm`

will
re-evaluate the arguments if this is possible, and use internal data
if not.

##### Value

- Another fitted point process model (object of class
`"ppm"`

).

##### Examples

```
data(nztrees)
data(cells)
# fit the stationary Poisson process
fit <- ppm(nztrees, ~ 1)
# fit a nonstationary Poisson process
fitP <- update(fit, trend=~x)
fitP <- update(fit, ~x)
# fit a stationary Strauss process
fitS <- update(fit, interaction=Strauss(13))
fitS <- update(fit, Strauss(13))
# oops, forgot the edge correction
fitS <- update(fitS, rbord=13)
# re-fit the model to a subset
# of the original point pattern
nzw <- owin(c(0,148),c(0,95))
nzsub <- nztrees[,nzw]
fut <- update(fitS, Q=nzsub)
fut <- update(fitS, nzsub)
# WARNING: the point pattern argument is called 'Q'
ranfit <- ppm(rpoispp(42), ~1, Poisson())
ranfit
# different random data!
update(ranfit)
# the original data
update(ranfit, use.internal=TRUE)
```

*Documentation reproduced from package spatstat, version 1.10-3, License: GPL version 2 or newer*