R: Check Whether Point Process Model is Valid

valid.ppm {spatstat} | R Documentation

## Check Whether Point Process Model is Valid

### Description

Determines whether a fitted point process model satisfies the integrability conditions for existence of the point process.

### Usage

      ## S3 method for class 'ppm'
    valid(object, warn=TRUE, ...)

### Arguments

`object` | Fitted point process model (object of class `"ppm"`).
`warn` | Logical value indicating whether to issue a warning if the validity of the model cannot be checked (due to unavailability of the required code).
`...` | Ignored.

### Details

This is a method for the generic function `valid` for Poisson and Gibbs point process models (class `"ppm"`).

The model-fitting function `ppm` fits Gibbs point process models to point pattern data. By default, `ppm` does not check whether the fitted model actually exists as a point process. This checking is done by `valid.ppm`.

Unlike a regression model, which is well-defined for any values of the fitted regression coefficients, a Gibbs point process model is only well-defined if the fitted interaction parameters satisfy some constraints. A famous example is the Strauss process (see `Strauss`) which exists only when the interaction parameter gamma is less than or equal to 1. For values gamma > 1, the probability density is not integrable and the process does not exist (and cannot be simulated).

By default, `ppm` does not enforce the constraint that a fitted Strauss process (for example) must satisfy gamma <= 1. This is because a fitted parameter value of gamma > 1 could be useful information for data analysis, as it indicates that the Strauss model is not appropriate, and suggests a clustered model should be fitted.

The function `valid.ppm` checks whether the fitted model `object` specifies a well-defined point process. It returns `TRUE` if the model is well-defined.

Another possible reason for invalid models is that the data may not be adequate for estimation of the model parameters. In this case, some of the fitted coefficients could be `NA` or infinite values. If this happens then `valid.ppm` returns `FALSE`.

Use the function `project.ppm` to force the fitted model to be valid.

### Value

A logical value, or `NA`.

### Author(s)

Adrian Baddeley Adrian.Baddeley@curtin.edu.au

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

### See Also

`ppm`, `project.ppm`

### Examples

       fit1 <- ppm(cells, ~1, Strauss(0.1))
       valid(fit1)
       fit2 <- ppm(redwood, ~1, Strauss(0.1))
       valid(fit2)

[Package spatstat version 1.63-3 Index]