spatstat (version 1.64-1)

rmh.ppm: Simulate from a Fitted Point Process Model


Given a point process model fitted to data, generate a random simulation of the model, using the Metropolis-Hastings algorithm.


# S3 method for ppm
rmh(model, start=NULL,
                    control=default.rmhcontrol(model, w=w),
                    w = NULL, 
                    nsim=1, drop=TRUE, saveinfo=TRUE,
                    verbose=TRUE, new.coef=NULL)



A fitted point process model (object of class "ppm", see ppm.object) which it is desired to simulate. This fitted model is usually the result of a call to ppm. See Details below.


Data determining the initial state of the Metropolis-Hastings algorithm. See rmhstart for description of these arguments. Defaults to list(x.start=data.ppm(model))


Data controlling the iterative behaviour of the Metropolis-Hastings algorithm. See rmhcontrol for description of these arguments.

Further arguments passed to rmhcontrol, or to rmh.default, or to covariate functions in the model.


Optional. Window in which the simulations should be generated. Default is the window of the original data.


Logical flag indicating what to do if the fitted model is invalid (in the sense that the values of the fitted coefficients do not specify a valid point process). If project=TRUE the closest valid model will be simulated; if project=FALSE an error will occur.


Number of simulated point patterns that should be generated.


Logical. If nsim=1 and drop=TRUE (the default), the result will be a point pattern, rather than a list containing a single point pattern.


Logical value indicating whether to save auxiliary information.


Logical flag indicating whether to print progress reports.


New values for the canonical parameters of the model. A numeric vector of the same length as coef(model).


A point pattern (an object of class "ppp"; see ppp.object) or a list of point patterns.


See Warnings in rmh.default.


This function generates simulated realisations from a point process model that has been fitted to point pattern data. It is a method for the generic function rmh for the class "ppm" of fitted point process models. To simulate other kinds of point process models, see rmh or rmh.default.

The argument model describes the fitted model. It must be an object of class "ppm" (see ppm.object), and will typically be the result of a call to the point process model fitting function ppm.

The current implementation enables simulation from any fitted model involving the interactions AreaInter, DiggleGratton, DiggleGatesStibbard, Geyer, Hardcore, MultiStrauss, MultiStraussHard, PairPiece, Poisson, Strauss, StraussHard and Softcore, including nonstationary models. See the examples.

It is also possible to simulate hybrids of several such models. See Hybrid and the examples.

It is possible that the fitted coefficients of a point process model may be ``illegal'', i.e. that there may not exist a mathematically well-defined point process with the given parameter values. For example, a Strauss process with interaction parameter \(\gamma > 1\) does not exist, but the model-fitting procedure used in ppm will sometimes produce values of \(\gamma\) greater than 1. In such cases, if project=FALSE then an error will occur, while if project=TRUE then rmh.ppm will find the nearest legal model and simulate this model instead. (The nearest legal model is obtained by projecting the vector of coefficients onto the set of valid coefficient vectors. The result is usually the Poisson process with the same fitted intensity.)

The arguments start and control are lists of parameters determining the initial state and the iterative behaviour, respectively, of the Metropolis-Hastings algorithm.

The argument start is passed directly to rmhstart. See rmhstart for details of the parameters of the initial state, and their default values.

The argument control is first passed to rmhcontrol. Then if any additional arguments are given, update.rmhcontrol is called to update the parameter values. See rmhcontrol for details of the iterative behaviour parameters, and default.rmhcontrol for their default values.

Note that if you specify expansion of the simulation window using the parameter expand (so that the model will be simulated on a window larger than the original data window) then the model must be capable of extrapolation to this larger window. This is usually not possible for models which depend on external covariates, because the domain of a covariate image is usually the same as the domain of the fitted model.

After extracting the relevant information from the fitted model object model, rmh.ppm invokes the default rmh algorithm rmh.default, unless the model is Poisson. If the model is Poisson then the Metropolis-Hastings algorithm is not needed, and the model is simulated directly, using one of rpoispp, rmpoispp, rpoint or rmpoint.

See rmh.default for further information about the implementation, or about the Metropolis-Hastings algorithm.

See Also

simulate.ppm, rmh, rmhmodel, rmhcontrol, default.rmhcontrol, update.rmhcontrol, rmhstart, rmh.default, ppp.object, ppm,

Interactions: AreaInter, DiggleGratton, DiggleGatesStibbard, Geyer, Hardcore, Hybrid, MultiStrauss, MultiStraussHard, PairPiece, Poisson, Strauss, StraussHard, Softcore


Run this code
   live <- interactive()
   op <- spatstat.options()
   Nrep <- 1e5

   X <- swedishpines
   if(live) plot(X, main="Swedish Pines data")

   # Poisson process
   fit <- ppm(X, ~1, Poisson())
   Xsim <- rmh(fit)
   if(live) plot(Xsim, main="simulation from fitted Poisson model")

   # Strauss process   
   fit <- ppm(X, ~1, Strauss(r=7))
   Xsim <- rmh(fit)
   if(live) plot(Xsim, main="simulation from fitted Strauss model")

# }
     # Strauss process simulated on a larger window
     # then clipped to original window
     Xsim <- rmh(fit, control=list(nrep=Nrep, expand=1.1, periodic=TRUE))
     Xsim <- rmh(fit, nrep=Nrep, expand=2, periodic=TRUE)
# }
# }
     X <- rSSI(0.05, 100)
     # piecewise-constant pairwise interaction function
     fit <- ppm(X, ~1, PairPiece(seq(0.02, 0.1, by=0.01)))
     Xsim <- rmh(fit)
# }
    # marked point pattern
    Y <- amacrine

# }
     # marked Poisson models
     fit <- ppm(Y)
     fit <- ppm(Y,~marks)
     fit <- ppm(Y,~polynom(x,2))
     fit <- ppm(Y,~marks+polynom(x,2))
     fit <- ppm(Y,~marks*polynom(x,y,2))
     Ysim <- rmh(fit)
# }
   # multitype Strauss models
   MS <- MultiStrauss(radii=matrix(0.07, ncol=2, nrow=2),
                      types = levels(Y$marks))
# }
    fit <- ppm(Y ~marks, MS)
    Ysim <- rmh(fit)
# }
   fit <- ppm(Y ~ marks*polynom(x,y,2), MS)
   Ysim <- rmh(fit)
   if(live) plot(Ysim, main="simulation from fitted inhomogeneous Multitype Strauss")


# }
    # Hybrid model
    fit <- ppm(redwood, ~1, Hybrid(A=Strauss(0.02), B=Geyer(0.1, 2)))
    Y <- rmh(fit)
# }

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