sim.cppm {NScluster} | R Documentation |
Simulation for Neyman-Scott Cluster Point Process Models and Their Extensions
Description
Simulation for the Thomas and Inverse-power type models, and the extended Thomas models of type A, B, and C.
Usage
sim.cppm(model = "Thomas", pars, seed = NULL)
## S3 method for class 'sim.cpp'
print(x, ...)
## S3 method for class 'sim.cpp'
plot(x, parents.distinct = FALSE, ...)
Arguments
model |
a character string indicating each cluster point process model:
|
pars |
a named vector giving the values of each parameter. See 'Details'. |
seed |
arbitrary positive integer to generate a sequence of uniform random numbers. The default seed is based on the current time. |
x |
an object of class |
parents.distinct |
logical. If |
... |
further graphical parameters from |
Details
We consider the five cluster point process models: the Thomas and Inverse-power type models, and the extended Thomas models of type A, B, and C.
"Thomas"
(Thomas model)The parameters of the model are as follows:
-
mu
: the intensity of parent points. -
nu
: the expectation of a random number of descendant points of each parent point. -
sigma
: the parameter set of the dispersal kernel.
Let a random variable
U
be independently and uniformly distributed in [0,1].Consider
U = \int_0^r q_\sigma(t)dt = 1 - \exp \left( -\frac{r^2}{2\sigma^2} \right),
where
r
is the random variable of the distance between each parent point and the descendant points associated with the given parent. The distance is distributed independently and identically according to the dispersal kernel.We have
r = \sigma \sqrt{-2 \log(1-U)}.
Let
(x_i^p, y_i^p), i=1,2,\dots, I,
be a coordinate of each parent point where the integerI
is generated from the Poisson random variablePoisson(\mu)
with mean\mu
from now on. Then, for eachi
, the number of offspringJ_i
is generated by the random variablePoisson(\nu)
with mean\nu
. Then, using series of different uniform random numbers\{U\}
for differenti
andj
, each of the offspring coordinates(x_j^i, y_j^i), j=1,2,\dots,J_i
is given byx_j^i = x_i^p + r \cos(2 \pi U),
y_j^i = y_i^p + r \sin(2 \pi U),
owing to the isotropy condition of the distribution.
Given a positive number
\nu
and let a sequence of a random variable\{U_k\}
be independently and uniformly distributed in [0,1], the Poisson random numberM
is the smallest integer such that\sum_{k=1}^{M+1} - \log U_k > \nu,
where
\log
represents natural logarithm.-
"IP"
(Inverse-power type model)The parameters of the model are as follows:
-
mu
: the intensity of parent points. -
nu
: the expectation of a random number of descendant points of each parent point. -
p
,c
: the set of parameters of the dispersal kernel, wherep
> 1 andc
> 0.
Let
U
be as above.For all
r \ge 0
,Q_{p,c}(r) := \int_0^r q_{p,c}(t)dt
= c^{p-1}(p-1) \frac{(r+c)^{1-p} - c^{1-p}}{1-p}
= 1 - c^{p-1} (r+c)^{1-p}.
Here, we put
Q_{p,c}(r) = U
. From this, we haver = c\{(1-U)^{1/(1-p)} - 1\}.
The parent points and their descendant points are generated the same as the Thomas model.
-
"TypeA"
(Type A model)The parameters of the model are as follows:
-
mu
: the intensity of parent points. -
nu
: the expectation of a random number of descendant points of each parent point. -
a
,sigma1
,sigma2
: the set of parameters of the dispersal kernel, where wherea
is a mixture ratio parameter with 0 <a
< 1.
Let each random variable
U_k, k=1,2
, be independently and uniformly distributed in [0,1].Then
r
satisfies as follows:r = \sigma_1 \sqrt{-2 \log(1-U_1)}, \quad U_2 \le a ,
r = \sigma_2 \sqrt{-2 \log(1-U_1)}, \quad \mathrm{otherwise.}
The parent points and their descendant points are generated the same as the Thomas model.
-
"TypeB"
(Type B model)The TypeB is a superposed Thomas model. The parameters of the model are as follows:
-
mu1
,mu2
: the corresponding intensity of parent points of each Thomas model. -
nu
: the expectation of a random number of descendant points of each parent point. -
sigma1
,sigma2
: the corresponding set of parameters of the dispersal kernel of each Thomas model.
Consider the two types of the Thomas model with parameters
(\mu_1, \nu, \sigma_1)
and(\mu_2, \nu, \sigma_2)
. Parents' configuration and numbers of the descendant cluster sizes are generated by the two types of uniformly distributed parents(x_i^k, y_i^k)
withi=1,2,\dots,Poisson(\mu_k)
fork=1,2
, respectively.Then, using series of different uniform random numbers
\{U\}
for differenti
andj
, each of the descendant coordinates(x_j^{k,i}, y_j^{k,i})
of the parents(x_i^k, y_i^k)
,k=1,2
,j=1,2,\dots,Poisson(\nu)
, is given byx_j^{k,i} = x_i^k + r_k \cos (2 \pi U),
y_j^{k,i} = y_i^k + r_k \sin (2 \pi U),
where
r_k = \sigma_k \sqrt{-2 \log (1-U_k)}, \quad k = 1, 2,
with different random numbers
\{U_k, U\}
for differentk, i
, andj
.-
"TypeC"
(Type C model)The TypeC is a superposed Thomas model. The parameters of the model are as follows:
-
mu1
,mu2
: the corresponding intensity of parent points of each Thomas model. -
nu1
,nu2
: the corresponding expectation of a random number of descendant points of each Thomas model. -
sigma1
,sigma2
: the corresponding set of parameters of the dispersal kernel of each Thomas model.
The parent points and their descendant points are generated the same as the Type B model.
-
Value
sim.cppm
returns an object of class "sim.cpp"
containing the
following components which has print
and plot
methods.
parents |
a list containing two components named " |
offspring |
a list containing two components named " |
References
Tanaka, U., Ogata, Y. and Katsura, K. (2008) Simulation and estimation of the Neyman-Scott type spatial cluster models. Computer Science Monographs 34, 1-44. The Institute of Statistical Mathematics, Tokyo. https://www.ism.ac.jp/editsec/csm/.
Examples
## Thomas Model
pars <- c(mu = 50.0, nu = 30.0, sigma = 0.03)
t.sim <- sim.cppm("Thomas", pars, seed = 117)
t.sim
plot(t.sim)
## Inverse-Power Type Model
pars <- c(mu = 50.0, nu = 30.0, p = 1.5, c = 0.005)
ip.sim <- sim.cppm("IP", pars, seed = 353)
ip.sim
plot(ip.sim)
## Type A Model
pars <- c(mu = 50.0, nu = 30.0, a = 0.3, sigma1 = 0.005, sigma2 = 0.1)
a.sim <- sim.cppm("TypeA", pars, seed = 575)
a.sim
plot(a.sim)
## Type B Model
pars <- c(mu1 = 10.0, mu2 = 40.0, nu = 30.0, sigma1 = 0.01, sigma2 = 0.03)
b.sim <- sim.cppm("TypeB", pars, seed = 257)
b.sim
plot(b.sim, parents.distinct = TRUE)
## Type C Model
pars <- c(mu1 = 5.0, mu2 = 9.0, nu1 = 30.0, nu2 = 150.0,
sigma1 = 0.01, sigma2 = 0.05)
c.sim <- sim.cppm("TypeC", pars, seed = 555)
c.sim
plot(c.sim, parents.distinct = FALSE)