spatstat (version 1.23-1)

rmhcontrol: Set Control Parameters for Metropolis-Hastings Algorithm.

Description

Sets up a list of parameters controlling the iterative behaviour of the Metropolis-Hastings algorithm.

Usage

rmhcontrol(...)
   ## S3 method for class 'default':
rmhcontrol(\dots, p=0.9, q=0.5, nrep=5e5,
                      expand=NULL, periodic=FALSE, ptypes=NULL,
                      x.cond=NULL, fixall=FALSE, nverb=0)

Arguments

...

Arguments passed to methods.

p

Probability of proposing a shift (as against a birth/death)

q

Conditional probability of proposing a death given that a birth or death will be proposed

nrep

Total number of steps (proposals) of Metropolis-Hastings algorithm that should be run

expand

Either a numerical expansion factor, or a window (object of class "owin"), specifying that simulations are to be performed in a domain larger than the original data window, then clipped to the original data window.

periodic

(Logical) whether to simulate ``periodically'', i.e. on a torus formed by identifying opposite edges of a rectangle.

ptypes

For multitype point processes, the distribution of the mark attached to a new random point (when a birth is proposed)

x.cond

Conditioning points for conditional simulation.

fixall

(Logical) for multitype point processes, whether to fix the number of points of each type.

nverb

Progress reports will be printed every nverb iterations

Value

• An object of class "rmhcontrol", which is essentially a list of parameter values for the algorithm. There is a print method for this class, which prints a sensible description of the parameters chosen.

Details

The Metropolis-Hastings algorithm, implemented as rmh, generates simulated realisations of point process models. The function rmhcontrol sets up a list of parameters which control the iterative behaviour and termination of the Metropolis-Hastings algorithm, for use in a subsequent call to rmh. It also checks that the parameters are valid.

(A separate function rmhstart determines the initial state of the algorithm, and rmhmodel determines the model to be simulated.)

The parameters are as follows: [object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

References

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.

See Also

rmh, rmhmodel, rmhstart, expand.owin, spatstat.options

Examples

# parameters given as named arguments
   c1 <- rmhcontrol(p=0.3,periodic=TRUE,nrep=1e6,nverb=1e5)

   # parameters given as a list
   liz <- list(p=0.9, nrep=1e4)
   c2 <- rmhcontrol(liz)

   # parameters given in rmhcontrol object
   c3 <- rmhcontrol(c1)