# update.ppm

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##### Update a Fitted Point Process Model

update method for class "ppm".

Keywords
models, methods, spatial
##### 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 argumentQ;
• exactly one argument of class"formula", which will be interpreted as the named argumenttrend;
• exactly one argument of class"interact", which will be interpreted as the named argumentinteraction;
• exactly one argument of class"data.frame", which will be interpreted as the named argumentcovariates.

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").

• update.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

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