R: Loglikelihood of Spatial Logistic Regression

logLik.slrm {spatstat} | R Documentation

## Loglikelihood of Spatial Logistic Regression

### Description

Computes the (maximised) loglikelihood of a fitted Spatial Logistic Regression model.

### Usage

      ## S3 method for class 'slrm'
    logLik(object, ..., adjust = TRUE)

### Arguments

`object` | a fitted spatial logistic regression model. An object of class `"slrm"`.
`...` | Ignored.
`adjust` | Logical value indicating whether to adjust the loglikelihood of the model to make it comparable with a point process likelihood. See Details.

### Details

This is a method for `logLik` for fitted spatial logistic regression models (objects of class `"slrm"`, usually obtained from the function `slrm`). It computes the log-likelihood of a fitted spatial logistic regression model.

If `adjust=FALSE`, the loglikelihood is computed using the standard formula for the loglikelihood of a logistic regression model for a finite set of (pixel) observations.

If `adjust=TRUE` then the loglikelihood is adjusted so that it is approximately comparable with the likelihood of a point process in continuous space, by subtracting the value n * log(a) where n is the number of points in the original point pattern dataset, and a is the area of one pixel.

### Value

A numerical value.

### Author(s)

Adrian Baddeley Adrian.Baddeley@curtin.edu.au adrian@maths.uwa.edu.au

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

### See Also

`slrm`

### Examples

      X <- rpoispp(42)
      fit <- slrm(X ~ x+y)
      logLik(fit)
      logLik(fit, adjust=FALSE)

[Package spatstat version 1.63-3 Index]