rmhcontrol

0th

Percentile

Set Control Parameters for Metropolis-Hastings Algorithm.

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

Keywords
spatial, datagen
Usage
rmhcontrol(...)

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

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
Simulation window or expansion rule. Either a window (object of class "owin") or a numerical expansion factor, specifying that simulations are to be performed in a domain other than the original data window, then clipped to th
periodic
Logical value (or NULL) indicating whether to simulate ``periodically'', i.e. identifying opposite edges of the rectangular simulation window. A NULL value means ``undecided.''
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
nsave,nburn
If these values are specified, then intermediate states of the simulation algorithm will be saved every nsave iterations, after an initial burn-in period of nburn iterations.
track
Logical flag indicating whether to save the transition history of the simulations.
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],[object Object],[object Object]

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.

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, rmhexpand, spatstat.options

Aliases
  • rmhcontrol
  • rmhcontrol.default
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)
Documentation reproduced from package spatstat, version 1.42-2, License: GPL (>= 2)

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