Interpret Fitted Model for Metropolis-Hastings Simulation.
Converts a fitted point process model into a format that can be used to simulate the model by the Metropolis-Hastings algorithm.
## S3 method for class 'ppm': rmhmodel(model, win, ..., verbose=TRUE, project=TRUE, control=rmhcontrol())
- Fitted point process model (object of class
- Optional. Window in which the simulations should be generated.
- Logical flag indicating whether to print progress reports while the model is being converted.
- Logical flag indicating what to do if the fitted model does not correspond to a valid point process. See Details.
- Parameters determining the iterative behaviour of the simulation
algorithm. Passed to
The generic function
rmhmodel takes a
description of a point process model in some format, and
converts it into an object of class
so that simulations of the model can be generated using
the Metropolis-Hastings algorithm
rmhmodel.ppm is the method for
"ppm" of fitted point process models.
model should be a fitted point process model
(object of class
"ppm") typically obtained from the
This will be converted into an object of class
The optional argument
win specifies the window
in which the pattern is to be generated. If specified, it must be in
a form which can be coerced to an object of class
It is also possible that a fitted point process model
ppm may not correspond to a valid
point process. For example a fitted model with the
interpoint interaction may have any value of the interaction parameter
$\gamma$; however the Strauss
process is not well-defined for
$\gamma > 1$ (Kelly and Ripley, 1976).
project determines what to do in such cases.
project=FALSE, a fatal error will occur.
project=TRUE, the fitted model parameters will be
adjusted to the nearest values which do correspond to a valid
point process. For example a Strauss process with $\gamma >
1$ will be projected to a Strauss process with
$\gamma = 1$, equivalent to a Poisson process.
- An object of class
"rmhmodel", which is essentially a list of parameter values for the model. There is a
Diggle, P. J. (2003) Statistical Analysis of Spatial Point Patterns (2nd ed.) Arnold, London.
Diggle, P.J. and Gratton, R.J. (1984) Monte Carlo methods of inference for implicit statistical models. Journal of the Royal Statistical Society, series B 46, 193 -- 212.
Geyer, C.J. (1999) Likelihood Inference for Spatial Point Processes. Chapter 3 in O.E. Barndorff-Nielsen, W.S. Kendall and M.N.M. Van Lieshout (eds) Stochastic Geometry: Likelihood and Computation, Chapman and Hall / CRC, Monographs on Statistics and Applied Probability, number 80. Pages 79--140.
Kelly, F.P. and Ripley, B.D. (1976) On Strauss's model for clustering. Biometrika 63, 357--360.
data(cells) fit <- ppm(cells, ~1, Strauss(0.07)) mod1 <- rmhmodel(fit) fit2 <- ppm(cells, ~x, Geyer(0.07, 2)) mod2 <- rmhmodel(fit2) # Then rmh(mod1), etc