R: Residual Diagnostics for Multiple Fitted Models

compareFit {spatstat.core} | R Documentation

## Residual Diagnostics for Multiple Fitted Models

### Description

Compares several fitted point process models using the same residual diagnostic.

### Usage

    compareFit(object, Fun, r = NULL, breaks = NULL, ...,
             trend = ~1, interaction = Poisson(), rbord = NULL,
             modelnames = NULL, same = NULL, different = NULL)

### Arguments

`object` | Object or objects to be analysed. Either a fitted point process model (object of class `"ppm"`), a point pattern (object of class `"ppp"`), or a list of these objects.
`Fun` | Diagnostic function to be computed for each model. One of the functions `Kcom`, `Kres`, `Gcom`, `Gres`, `psst`, `psstA` or `psstG` or a string containing one of these names.
`r` | Optional. Vector of values of the argument r at which the diagnostic should be computed. This argument is usually not specified. There is a sensible default.
`breaks` | Optional alternative to `r` for advanced use.
`...` | Extra arguments passed to `Fun`.
`trend,interaction,rbord` | Optional. Arguments passed to `ppm` to fit a point process model to the data, if `object` is a point pattern or list of point patterns. See `ppm` for details. Each of these arguments can be a list, specifying different `trend`, `interaction` and/or `rbord` values to be used to generate different fitted models.
`modelnames` | Character vector. Short descriptive names for the different models.
`same,different` | Character strings or character vectors passed to `collapse.fv` to determine the format of the output.

### Details

This is a convenient way to collect diagnostic information for several different point process models fitted to the same point pattern dataset, or for point process models of the same form fitted to several different datasets, etc.

The first argument, `object`, is usually a list of fitted point process models (objects of class `"ppm"`), obtained from the model-fitting function `ppm`.

For convenience, `object` can also be a list of point patterns (objects of class `"ppp"`). In that case, point process models will be fitted to each of the point pattern datasets, by calling `ppm` using the arguments `trend` (for the first order trend), `interaction` (for the interpoint interaction) and `rbord` (for the erosion distance in the border correction for the pseudolikelihood). See `ppm` for details of these arguments.

Alternatively `object` can be a single point pattern (object of class `"ppp"`) and one or more of the arguments `trend`, `interaction` or `rbord` can be a list. In this case, point process models will be fitted to the same point pattern dataset, using each of the model specifications listed.

The diagnostic function `Fun` will be applied to each of the point process models. The results will be collected into a single function value table. The `modelnames` are used to label the results from each fitted model.

### Value

Function value table (object of class `"fv"`).

### Author(s)

Ege Rubak rubak@math.aau.dk, Adrian Baddeley Adrian.Baddeley@curtin.edu.au and Jesper Moller.

### See Also

`ppm`, `Kcom`, `Kres`, `Gcom`, `Gres`, `psst`, `psstA`, `psstG`, `collapse.fv`

### Examples

       nd <- 40

       ilist <- list(Poisson(), Geyer(7, 2), Strauss(7))
       iname <- c("Poisson", "Geyer", "Strauss")

       K <- compareFit(swedishpines, Kcom, interaction=ilist, rbord=9,
                correction="translate",
                same="trans", different="tcom", modelnames=iname, nd=nd)
       K

[Package spatstat.core version 2.3-1 Index]