spatstat (version 1.20-2)

lgcp.estK: Fit a Log-Gaussian Cox Point Process by Minimum Contrast

Description

Fits a log-Gaussian Cox point process model (with exponential covariance function) to a point pattern dataset by the Method of Minimum Contrast.

Usage

lgcp.estK(X, startpar=c(sigma2=1,alpha=1), lambda=NULL,
q = 1/4, p = 2, rmin = NULL, rmax = NULL, ...)

Arguments

X

Data to which the model will be fitted. Either a point pattern or a summary statistic. See Details.

startpar

Vector of starting values for the parameters of the log-Gaussian Cox process model.

lambda

Optional. An estimate of the intensity of the point process.

q,p

Optional. Exponents for the contrast criterion.

rmin, rmax

Optional. The interval of $r$ values for the contrast criterion.

...

Optional arguments passed to optim to control the optimisation algorithm. See Details.

Value

• An object of class "minconfit". There are methods for printing and plotting this object. It contains the following main components:
• par
  Vector of fitted parameter values.
• fit
  Function value table (object of class "fv") containing the observed values of the summary statistic (observed) and the theoretical values of the summary statistic computed from the fitted model parameters.

Details

This algorithm fits a log-Gaussian Cox point process model to a point pattern dataset by the Method of Minimum Contrast, using the K function.

The argument X can be either [object Object],[object Object] The algorithm fits a log-Gaussian Cox point process (LGCP) model to X, by finding the parameters of the LGCP model which give the closest match between the theoretical $K$ function of the LGCP model and the observed $K$ function. For a more detailed explanation of the Method of Minimum Contrast, see mincontrast.

The model fitted is a stationary, isotropic log-Gaussian Cox process with exponential covariance (Moller and Waagepetersen, 2003, pp. 72-76). To define this process we start with a stationary Gaussian random field $Z$ in the two-dimensional plane, with constant mean $\mu$ and covariance function $$c(r) = \sigma^2 e^{-r/\alpha}$$ where $\sigma^2$ and $\alpha$ are parameters. Given $Z$, we generate a Poisson point process $Y$ with intensity function $\lambda(u) = \exp(Z(u))$ at location $u$. Then $Y$ is a log-Gaussian Cox process.

The theoretical $K$-function of the LGCP is $$K(r) = \int_0^r 2\pi s \exp(\sigma^2 \exp(-s/\alpha)) \, {\rm d}s.$$ The theoretical intensity of the LGCP is $$\lambda = \exp(\mu + \frac{\sigma^2}{2}).$$ In this algorithm, the Method of Minimum Contrast is first used to find optimal values of the parameters $\sigma^2$ and $\alpha$. Then the remaining parameter $\mu$ is inferred from the estimated intensity $\lambda$.

If the argument lambda is provided, then this is used as the value of $\lambda$. Otherwise, if X is a point pattern, then $\lambda$ will be estimated from X. If X is a summary statistic and lambda is missing, then the intensity $\lambda$ cannot be estimated, and the parameter $\mu$ will be returned as NA.

The remaining arguments rmin,rmax,q,p control the method of minimum contrast; see mincontrast.

The optimisation algorithm can be controlled through the additional arguments "..." which are passed to the optimisation function optim. For example, to constrain the parameter values to a certain range, use the argument method="L-BFGS-B" to select an optimisation algorithm that respects box constraints, and use the arguments lower and upper to specify (vectors of) minimum and maximum values for each parameter.

References

Moller, J. and Waagepetersen, R. (2003). Statistical Inference and Simulation for Spatial Point Processes. Chapman and Hall/CRC, Boca Raton.

Waagepetersen, R. (2007) An estimating function approach to inference for inhomogeneous Neyman-Scott processes. Biometrics 63, 252--258.

See Also

lgcp.estK, matclust.estK, mincontrast, Kest

Examples

data(redwood)
    u <- lgcp.estK(redwood, c(sigma2=0.1, alpha=1))
    u
    plot(u)