R: Exact Maximum Pseudolikelihood Estimate for Stationary...

exactMPLEstrauss {spatstat.core} | R Documentation

## Exact Maximum Pseudolikelihood Estimate for Stationary Strauss Process

### Description

Computes, to very high accuracy, the Maximum Pseudolikelihood Estimates of the parameters of a stationary Strauss point process.

### Usage

      exactMPLEstrauss(X, R, ngrid = 2048, plotit = FALSE, project=TRUE)

### Arguments

`X` | Data to which the Strauss process will be fitted. A point pattern dataset (object of class `"ppp"`).
`R` | Interaction radius of the Strauss process. A non-negative number.
`ngrid` | Grid size for calculation of integrals. An integer, giving the number of grid points in the x and y directions.
`plotit` | Logical. If `TRUE`, the log pseudolikelihood is plotted on the current device.
`project` | Logical. If `TRUE` (the default), the parameter gamma is constrained to lie in the interval [0,1]. If `FALSE`, this constraint is not applied.

### Details

This function is intended mainly for technical investigation of algorithm performance. Its practical use is quite limited.

It fits the stationary Strauss point process model to the point pattern dataset `X` by maximum pseudolikelihood (with the border edge correction) using an algorithm with very high accuracy. This algorithm is more accurate than the default behaviour of the model-fitting function `ppm` because the discretisation is much finer.

Ripley (1988) and Baddeley and Turner (2000) derived the log pseudolikelihood for the stationary Strauss process, and eliminated the parameter beta, obtaining an exact formula for the partial log pseudolikelihood as a function of the interaction parameter gamma only. The algorithm evaluates this expression to a high degree of accuracy, using numerical integration on a `ngrid * ngrid` lattice, uses `optim` to maximise the log pseudolikelihood with respect to gamma, and finally recovers beta.

The result is a vector of length 2, containing the fitted coefficients log(beta) and log(gamma). These values correspond to the entries that would be obtained with `coef(ppm(X, ~1, Strauss(R)))`. The fitted coefficients are typically accurate to within 10^(-6) as shown in Baddeley and Turner (2013).

Note however that (by default) `exactMPLEstrauss` constrains the parameter gamma to lie in the interval [0,1] in which the point process is well defined (Kelly and Ripley, 1976) whereas `ppm` does not constrain the value of gamma (by default). This behaviour is controlled by the argument `project` to `ppm` and `exactMPLEstrauss`. The default for `ppm` is `project=FALSE`, while the default for `exactMPLEstrauss` is `project=TRUE`.

### Value

Vector of length 2.

### Author(s)

Adrian Baddeley Adrian.Baddeley@curtin.edu.au

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

### References

Baddeley, A. and Turner, R. (2000) Practical maximum pseudolikelihood for spatial point patterns. Australian and New Zealand Journal of Statistics 42, 283–322.

Baddeley, A. and Turner, R. (2013) Bias correction for parameter estimates of spatial point process models. Journal of Statistical Computation and Simulation 2012. doi: 10.1080/00949655.2012.755976

Kelly, F.P. and Ripley, B.D. (1976) On Strauss's model for clustering. Biometrika 63, 357–360.

Ripley, B.D. (1988) Statistical inference for spatial processes. Cambridge University Press.

### See Also

`ppm`

### Examples

     if(interactive()) {
       rc <- 0.09
       exactMPLEstrauss(cells, rc, plotit=TRUE)
       coef(ppm(cells ~1, Strauss(rc)))
       coef(ppm(cells ~1, Strauss(rc), nd=128))
       rr <- 0.04
       exactMPLEstrauss(redwood, rr)
       exactMPLEstrauss(redwood, rr, project=FALSE)
       coef(ppm(redwood ~1, Strauss(rr)))
     } else {
       rc <- 0.09
       exactMPLEstrauss(cells, rc, ngrid=64, plotit=TRUE)
       exactMPLEstrauss(cells, rc, ngrid=64, project=FALSE)
     }

[Package spatstat.core version 1.65-5 Index]