r.anis.MatClust.lpp: Simulate anisotropic Matern cluster process on linear...

In Moradii/anisotropylpp: methods for (an)isotropic point patterns on linear networks

Description

Simulate anisotropic Matern cluster process on linear networks based on given interaction distance and angle

Usage

r.anis.MatClust.lpp(
  pint = 10,
  r = 0.2,
  cint = 10,
  L = L,
  a = pi/3,
  t = 10,
  model = c("Poisson", "Uniform"),
  relaxed = FALSE,
  prob = 0.5,
  nsim = 1,
  check = FALSE
)

Arguments

pint	intensity function of the parents if model is Poisson, otherwise the number of parrents
r	iteraction distance
cint	intensity function of offsprings if model is Poisson, otherwise the number of offsprings per cluster
L	a linear network
a	angle of interaction
t	pi/t will be considered as a tolerance for angle of interaction
model	model to be considered for simulating parents and offsprings, currently only Poisson and Uniform. Default is Poisson.
relaxed	logical. if TRUE the function keeps some points which do not fall within angle condition according to a given probability
prob	if relaxed=TRUE, then the simulation also includes points, which do not fall within an angle a with respect to their parent, with probability prob
nsim	number of simulations
check	logical

Details

this function generates realizations from (an)isotropic Matern Cluster point processes over linear networks. the strength of anisotropy can be controlled by relaxed and prob

Value

if nsim=1 a single realization, otherwise a list of realizations of lpp objects

Author(s)

Mehdi Moradi [e-mail]

References

Moradi, M., Mateu, J,. and Comas, C. (2020) Directional analysis for point patterns on linear networks. Stat.

See Also

rMatClust

Examples

# generate random realizations
X <- r.anis.MatClust.lpp(pint=0.01,r=300,cint=0.2,L=branchnet,t=50)
plot(X,main="",cols=2,col=4,pch=20)
Y <- r.anis.MatClust.lpp(pint=0.01,r=300,cint=0.2,L=branchnet,t=50,a=pi/2)
plot(Y,main="",cols=2,col=4,pch=20)

Moradii/anisotropylpp documentation built on Oct. 22, 2020, 7:05 a.m.