R: Update a Fitted Point Process Model

update.ppm {spatstat} | R Documentation

## Update a Fitted Point Process Model

### Description

`update` method for class `"ppm"`.

### Usage

      ## S3 method for class 'ppm'
    update(object, ..., fixdummy=TRUE, use.internal=NULL,
                                          envir=environment(terms(object)))

### 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`).
`envir` | Environment in which to re-evaluate the call to `ppm`.

### 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` (or as specifying a change to the trend formula);

- 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`.

The `trend` argument can be a formula that specifies a change to the current trend formula. For example, the formula `~ . + Z` specifies that the additional covariate `Z` will be added to the right hand side of the trend formula in the existing `object`.

The argument `fixdummy=TRUE` ensures comparability of the objects before and after updating. 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"`).

### Author(s)

Adrian Baddeley Adrian.Baddeley@curtin.edu.au

and Rolf Turner r.turner@auckland.ac.nz

### 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)

      # change the trend formula: add another term to the trend
      fitPxy <- update(fitP, ~ . + y)
      # change the trend formula: remove the x variable
      fitPy <- update(fitPxy, ~ . - x)

      # fit a stationary Strauss process
      fitS <- update(fit, interaction=Strauss(13))
      fitS <- update(fit, Strauss(13))

      # refit using a different edge correction
      fitS <- update(fitS, correction="isotropic")

      # 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)

[Package spatstat version 1.54-0 Index]