rmhmodel.list(spatstat)
rmhmodel.list()所属R语言包：spatstat
Define Point Process Model for Metropolis-Hastings Simulation.
定义点过程模型大都市黑斯廷斯的模拟。
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Given a list of parameters, builds a description of a point process model for use in simulating the model by the Metropolis-Hastings algorithm.
给定一个参数列表中的一个点过程模型用于模拟模型的Metropolis-Hastings算法，建立了一个描述。
用法----------Usage----------
## S3 method for class 'list'
rmhmodel(model, ...)
参数----------Arguments----------
参数：model
A list of parameters. See Details.
的参数列表。查看详细信息。
参数：...
Optional list of additional named parameters.
额外的命名参数的可选列表。
Details
详细信息----------Details----------
The generic function rmhmodel takes a description of a point process model in some format, and converts it into an object of class "rmhmodel" so that simulations of the model can be generated using the Metropolis-Hastings algorithm rmh.
通用功能rmhmodel某种格式的点过程模型的描述，并将其转换成一个类的对象"rmhmodel"，使模拟的模型可以使用的Metropolis-Hastings算法生成rmh。
This function rmhmodel.list is the method for lists. The argument model should be a named list of parameters of the form
此功能rmhmodel.list列表的方法。参数model应该是一个命名的参数列表的形式
list(cif, par, w, trend, types)
list(cif, par, w, trend, types)
where cif and par are required and the others are optional. For details about these components, see rmhmodel.default.
cif和par是必需的，其他都是可选的。有关这些组件的详细信息，请参阅rmhmodel.default。
The subsequent arguments ... (if any) may also have these names, and they will take precedence over elements of the list model.
后面的参数...（如果有的话）可能也有这些名字，他们将优先于元素的列表model。
值----------Value----------
An object of class "rmhmodel", which is essentially a validated list of parameter values for the model.
一个目的类"rmhmodel"，这基本上是一个有效的参数值列表为模型。
There is a print method for this class, which prints a sensible description of the model chosen.
有一个print这个类的方法，它会输出一个明智的描述所选择的模式。
（作者）----------Author(s)----------
Adrian Baddeley
<a href="mailto:Adrian.Baddeley@csiro.au">Adrian.Baddeley@csiro.au</a>
<a href="http://www.maths.uwa.edu.au/~adrian/">http://www.maths.uwa.edu.au/~adrian/</a>
and Rolf Turner
<a href="mailto:r.turner@auckland.ac.nz">r.turner@auckland.ac.nz</a>
参考文献----------References----------
Patterns (2nd ed.) Arnold, London.
Monte Carlo methods of inference for implicit statistical models. Journal of the Royal Statistical Society, series B 46, 193 – 212.
A nonparametric estimator for pairwise-interaction point processes. Biometrika 74, 763 – 770. Scandinavian Journal of Statistics 21, 359–373.
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.
参见----------See Also----------
rmhmodel, rmhmodel.default, rmhmodel.ppm, rmh, rmhcontrol, rmhstart, ppm, Strauss, Softcore, StraussHard, MultiStrauss, MultiStraussHard, DiggleGratton, PairPiece
rmhmodel，rmhmodel.default，rmhmodel.ppm，rmh，rmhcontrol，rmhstart，ppm，Strauss，<所述>，Softcore，StraussHard，MultiStrauss，MultiStraussHard，DiggleGratton
实例----------Examples----------
# Strauss process:[施特劳斯的过程：]
mod01 <- list(cif="strauss",par=list(beta=2,gamma=0.2,r=0.7),
w=c(0,10,0,10))
mod01 <- rmhmodel(mod01)
# Strauss with hardcore:[斯特劳斯的铁杆：]
mod04 <- list(cif="straush",par=list(beta=2,gamma=0.2,r=0.7,hc=0.3),
w=owin(c(0,10),c(0,5)))
mod04 <- rmhmodel(mod04)
# Soft core:[软核：]
w <- square(10)
mod07 <- list(cif="sftcr",
par=list(beta=0.8,sigma=0.1,kappa=0.5),
w=w)
mod07 <- rmhmodel(mod07)
# Multitype Strauss:[多类型施特劳斯：]
beta <- c(0.027,0.008)
gmma <- matrix(c(0.43,0.98,0.98,0.36),2,2)
r <- matrix(c(45,45,45,45),2,2)
mod08 <- list(cif="straussm",
par=list(beta=beta,gamma=gmma,radii=r),
w=square(250))
mod08 <- rmhmodel(mod08)
# specify types[指定类型]
mod09 <- rmhmodel(list(cif="straussm",
par=list(beta=beta,gamma=gmma,radii=r),
w=square(250),
types=c("A", "B")))
# Multitype Strauss hardcore with trends for each type:[多类型，每一种类型的趋势施特劳斯铁杆：]
beta <- c(0.27,0.08)
ri <- matrix(c(45,45,45,45),2,2)
rhc <- matrix(c(9.1,5.0,5.0,2.5),2,2)
tr3 <- function(x,y){x <- x/250; y <- y/250;
exp((6*x + 5*y - 18*x^2 + 12*x*y - 9*y^2)/6)
}
# log quadratic trend[登录二次趋势]
tr4 <- function(x,y){x <- x/250; y <- y/250;
exp(-0.6*x+0.5*y)}
# log linear trend[对数线性趋势]
mod10 <- list(cif="straushm",par=list(beta=beta,gamma=gmma,
iradii=ri,hradii=rhc),w=c(0,250,0,250),
trend=list(tr3,tr4))
mod10 <- rmhmodel(mod10)
# Lookup (interaction function h_2 from page 76, Diggle (2003)):[查找（第76页，Diggle（2003年）的交互功能H_2）：]
r <- seq(from=0,to=0.2,length=101)[-1] # Drop 0.[删除0。]
h <- 20*(r-0.05)
h[r<0.05] <- 0
h[r>0.10] <- 1
mod17 <- list(cif="lookup",par=list(beta=4000,h=h,r=r),w=c(0,1,0,1))
mod17 <- rmhmodel(mod17)
转载请注明:出自 生物统计家园网(http://www.biostatistic.net)。
注：
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发表于 2012-9-30 14:08:33
