| thomas.estK {spatstat} | R Documentation |
Fits the Thomas point process to a point pattern dataset by the Method of Minimum Contrast.
thomas.estK(X, startpar=c(kappa=1,sigma2=1), lambda=NULL,
q = 1/4, p = 2, rmin = NULL, rmax = NULL)
X |
Data to which the Thomas model will be fitted. Either a point pattern or a summary statistic. See Details. |
startpar |
Vector of starting values for the parameters of the Thomas process. |
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. |
This algorithm fits the Thomas point process model to a point pattern dataset by the Method of Minimum Contrast, using the K function.
The argument X can be either
"ppp"
representing a point pattern dataset.
The K function of the point pattern will be computed
using Kest, and the method of minimum contrast
will be applied to this.
"fv" containing
the values of a summary statistic, computed for a point pattern
dataset. The summary statistic should be the K function,
and this object should have been obtained by a call to
Kest or one of its relatives.
The algorithm fits the Thomas point process to X,
by finding the parameters of the Thomas model
which give the closest match between the
theoretical K function of the Thomas process
and the observed K function.
For a more detailed explanation of the Method of Minimum Contrast,
see mincontrast.
The Thomas point process is described in Moller and Waagepetersen (2003, pp. 61–62). It is a cluster process formed by taking a pattern of parent points, generated according to a Poisson process with intensity kappa, and around each parent point, generating a random number of offspring points, such that the number of offspring of each parent is a Poisson random variable with mean mu, and the locations of the offspring points of one parent are independent and isotropically Normally distributed around the parent point with standard deviation sigma.
The theoretical K-function of the Thomas process is
K(r) = pi r^2 + (1 - exp(-r^2/(4 sigma^2)))/kappa.
The theoretical intensity of the Thomas process is lambda=kappa* mu.
In this algorithm, the Method of Minimum Contrast is first used to find optimal values of the parameters kappa and sigma^2. 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 Thomas process can be simulated, using rThomas.
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.
|
Rasmus Waagepetersen rw@math.auc.dk Adapted for spatstat by Adrian Baddeley adrian@maths.uwa.edu.au http://www.maths.uwa.edu.au/~adrian/
Moller, J. and Waagepetersen, R. (2003). Statistical Inference and Simulation for Spatial Point Processes. Chapman and Hall/CRC, Boca Raton.
Waagepetersen, R. (2006). An estimation function approach to inference for inhomogeneous Neyman-Scott processes. Submitted.
lgcp.estK,
matclust.estK,
mincontrast,
Kest,
rThomas to simulate the fitted model.
data(redwood)
u <- thomas.estK(redwood, c(kappa=10, sigma2=0.1))
u
plot(u)