rmhmodel.default(spatstat)
rmhmodel.default()所属R语言包：spatstat
Build Point Process Model for Metropolis-Hastings Simulation.
建立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----------
Builds a description of a point process model for use in simulating the model by the Metropolis-Hastings algorithm.
建立一个点过程模型用于模拟模型的Metropolis-Hastings算法的描述。
用法----------Usage----------
## Default S3 method:
rmhmodel(...,
cif=NULL, par=NULL, w=NULL, trend=NULL, types=NULL)
参数----------Arguments----------
参数：...
Ignored.
忽略。
参数：cif
Character string specifying the choice of model
字符串指定模式的选择
参数：par
Parameters of the model
模型的参数
参数：w
Spatial window in which to simulate
空间窗口，在该窗口中，以模拟
参数：trend
Specification of the trend in the model
参数：types
A vector of factor levels defining the possible marks, for a multitype process.
一个向量的定义可能的标记因子水平，为多类型的过程。
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.default is the default method. It builds a description of the point process model from the simple arguments listed.
此功能rmhmodel.default是默认的方法。它建立了一个描述的点过程模型，从简单的参数。
The argument cif is a character string specifying the choice of interpoint interaction for the point process. The current options are
参数cif INTERPOINT互动的点过程中指定的选择是一个字符串。当前选项
'areaint' Area-interaction process.
'areaint'区域互动过程。
'badgey' Baddeley-Geyer (hybrid Geyer) process.
'badgey'巴德利 - 盖尔（混合盖尔）的过程。
'dgs' Diggle, Gates and Stibbard (1987) process
'dgs'Diggle，盖茨和Stibbard（1987年）的过程
'diggra' Diggle and Gratton (1984) process
'diggra'Diggle和格拉顿（1984）的过程
'fiksel' Fiksel double exponential process (Fiksel, 1984).
'fiksel'的Fiksel双指数的过程（Fiksel，1984年）。
'geyer' Saturation process (Geyer, 1999).
'geyer'饱和过程（盖尔，1999年）。
'hardcore' Hard core process
'hardcore'硬核过程
'lennard' Lennard-Jones process
'lennard'的Lennard-Jones过程
'lookup' General isotropic pairwise interaction process,
'lookup'的各向同性对相互作用过程，
'multihard' Multitype hardcore process
'multihard'的多类型铁杆过程
'strauss' The Strauss process
'strauss'施特劳斯过程
'straush' The Strauss process with hard core
'straush'施特劳斯与硬核
'sftcr' The Softcore process
'sftcr'的情色过程
'straussm' The multitype Strauss process
'straussm'多类型施特劳斯过程
'straushm' Multitype Strauss process with hard core
'straushm'的多类型施特劳斯过程与硬核
'triplets' Triplets process (Geyer, 1999).
'triplets'的三胞胎过程（盖尔，1999年）。
The argument par supplies parameter values appropriate to the conditional intensity function being invoked. These are:
参数par提供适当的条件强度函数的参数值被调用。这些是：
(Area-interaction process.) A named list with components beta,eta,r which are respectively the “base” intensity, the scaled interaction parameter and the interaction radius.
（区相互作用的过程。）命名的组件列表beta,eta,r分别是“碱基”的力度，规模相互作用参数和互动半径。
(Baddeley-Geyer process.) A named list with components beta (the “base” intensity), gamma (a vector of non-negative interaction parameters), r (a vector of interaction radii, of the same length as gamma, in increasing order), and sat (the saturation parameter(s); this may be a scalar, or a vector of the same length as gamma and r; all values should be at least 1). Note that because of the presence of “saturation” the gamma values are permitted to be larger than 1.
（巴德利Geyer的过程）命名的组件列表beta（以下简称“碱基”强度），gamma（向量非负的相互作用参数），r（矢量。的相互作用半径，具有相同的长度作为gamma，以递增顺序），和sat（饱和度参数（s），这可能是一个标量，或者具有相同的长度的矢量为gamma和r;所有的值至少应为1）。请注意，因为“饱和”的存在下，gamma的值允许大于1。
(Diggle, Gates, and Stibbard process. See Diggle, Gates, and Stibbard (1987)) A named list with components beta and rho. This process has pairwise interaction function equal to
（Diggle，盖茨和Stibbard的过程。Diggle，盖茨和Stibbard的（1987））命名的组件beta和rho。这个过程有两两交互功能，等于
for t < rho, and equal to 1 for t >= rho.
t < rho，和等于1 t >= rho的。
(Diggle-Gratton process. See Diggle and Gratton (1984) and Diggle, Gates and Stibbard (1987).) A named list with components beta, kappa, delta and rho. This process has pairwise interaction function e(t) equal to 0 for t < delta, equal to
（Diggle格拉顿的过程。Diggle和格拉顿（1984年）Diggle，盖茨和Stibbard，等（1987）。）命名的组件列表beta，kappa，delta和<X >。这个过程有两两交互功能rho至0为e(t)中，等于
for delta <= t < rho, and equal to 1 for t >= rho. Note that here we use the symbol kappa where Diggle, Gates, and Stibbard use beta since we reserve the symbol beta for an intensity parameter.
delta <= t < rho，和等于1 t >= rho的。请注意，我们在这里使用的符号kappa其中Diggle，盖茨和Stibbard的的使用beta，因为我们保留的象征beta的强度参数。
(Fiksel double exponential process, see Fiksel (1984)) A named list with components beta, r, hc, kappa and a. This process has pairwise interaction function e(t) equal to 0 for t < hc, equal to
（Fiksel双指数过程中，看到Fiksel）命名名单（1984年）的组件beta，r，hc，kappa和a。这个过程有两两交互功能e(t)至0为t < hc中，等于
for hc <= t < r, and equal to 1 for t >= r.
hc <= t < r，和等于1 t >= r的。
(Geyer's saturation process. See Geyer (1999).) A named list with components beta, gamma, r, and sat. The components beta, gamma, r are as for the Strauss model, and sat is the “saturation” parameter. The model is Geyer's “saturation” point process model, a modification of the Strauss process in which we effectively impose an upper limit (sat) on the number of neighbours which will be counted as close to a given point.
（盖尔的饱和过程。盖尔（1999）。）命名的组件列表beta，gamma，r和sat。的组成部分beta，gamma，r是施特劳斯的模型，和sat是“饱和度”参数。该模型是赫耶尔的“饱和”点的过程模型，施特劳斯过程的变形例，其中，有效地施加一个上限（sat）将被算作靠近到一个给定的点的数量的邻居。
Explicitly, a saturation point process with interaction radius r, saturation threshold s, and parameters beta and gamma, is the point process in which each point x[i] in the pattern X contributes a factor
明确地说，一个饱和点过程中的相互作用半径r，饱和阈值s，和参数beta和gamma，点过程中，每个点x[i]在模式X贡献的一个因素
to the probability density of the point pattern, where t(x[i],X) denotes the number of “r-close neighbours” of x[i] in the pattern X.
的概率密度的点模式，其中t(x[i],X)表示的“r - x[i]的模式X近邻”。
If the saturation threshold s is infinite, the Geyer process reduces to a Strauss process with interaction parameter gamma^2 rather than gamma.
如果饱和阈值s是无穷的，Geyer的过程中减少施特劳斯过程相互作用参数gamma^2而不是gamma。
(Hard core process.) A named list with components beta and hc where beta is the base intensity and hc is the hard core distance. This process has pairwise interaction function e(t) equal to 1 if t > hc and 0 if t <= hc.
（硬核的过程。）命名的组件列表beta和hc其中beta是基础的强度和hc是硬核的距离。这个过程有两两交互功能e(t)等于1，如果t > hc如果t <= hc和0。
(Lennard-Jones process.) A named list with components sigma and epsilon, where sigma is the characteristic diameter and epsilon is the well depth. See LennardJones for explanation.
（Lennard-Jones势的过程。）命名的组件列表sigma和epsilon，其中sigma是特征直径和epsilon是井深。见LennardJones解释。
(Multitype hard core process.) A named list with components beta and hradii, where beta is a vector of base intensities for each type of point, and hradii is a matrix of hard core radii between each pair of types.
（多类型，硬核）命名的组件列表。beta和hradii，其中beta是一个向量，基础强度为每个类型的点，hradii是一个矩阵的每个类型对之间的硬核半径。
(Strauss process.) A named list with components beta,gamma,r which are respectively the “base” intensity, the pairwise interaction parameter and the interaction radius. Note that gamma must be less than or equal to 1. (Note that there is also an algorithm for perfect simulation of the Strauss process, rStrauss)
（施特劳斯的过程。）命名的组件列表beta,gamma,r：“这是”碱基“的力度，对相互作用参数和互动半径分别。请注意，gamma必须小于或等于1。 （需要注意的是施特劳斯完美的模拟算法，rStrauss）
(Strauss process with hardcore.) A named list with entries beta,gamma,r,hc where beta, gamma, and r are as for the Strauss process, and hc is the hardcore radius. Of course hc must be less than r.
（施特劳斯与铁杆的过程。）命名的条目列表beta,gamma,r,hc其中beta，gamma和r是施特劳斯的过程中，和hc是的铁杆半径。当然hc必须小于r。
(Softcore process.) A named list with components beta,sigma,kappa. Again beta is a “base” intensity. The pairwise interaction between two points u != v is
（情色过程。）命名的组件列表beta,sigma,kappa。 beta是“碱基”的强度。两个点之间的两两互动u != v是
Note that it is necessary that 0 < kappa <1.
注意，这是必要的，0 < kappa <1。
(Multitype Strauss process.) A named list with components
（多类型施特劳斯的过程。）命名的组件列表
beta: A vector of “base” intensities, one for each possible type.
beta：“碱基”的强度，对于每个可能的类型之一的向量。
gamma: A symmetric matrix of interaction parameters, with gamma_ij pertaining to the interaction between type i and type j.
gamma：对称矩阵的相互作用参数，用gamma_ij类型i和类型j之间的相互作用有关。
radii: A symmetric matrix of interaction radii, with entries r_ij pertaining to the interaction between type i and type j.
radii：对称矩阵的互动半径的条目r_ij类型i和类型j之间的相互作用有关。
(Multitype Strauss process with hardcore.) A named list with components beta and gamma as for straussm and two “radii” components:
（多类型施特劳斯与铁杆的过程。）命名的组件列表beta和gamma的straussm和两个“半径”组件：
iradii: the interaction radii
iradii：互动半径
hradii: the hardcore radii
hradii：铁杆半径
which are both symmetric matrices of nonnegative numbers. The entries of hradii must be less than the corresponding entries of iradii.
这是两个对称矩阵的非负数。 hradii的条目必须小于相应的条目，iradii。
(Triplets process.) A named list with components beta,gamma,r which are respectively the “base” intensity, the triplet interaction parameter and the interaction radius. Note that gamma must be less than or equal to 1.
（三联的过程。）命名的组件列表beta,gamma,r分别是“碱基”的力度，三重相互作用参数和互动半径。请注意，gamma必须小于或等于1。
(Arbitrary pairwise interaction process with isotropic interaction.) A named list with components beta, r, and h, or just with components beta and h.
（任意两两相互作用过程与各向同性的互动。）命名的组件列表beta，r和h，或只是与组件beta和h。
This model is the pairwise interaction process with an isotropic interaction given by any chosen function H. Each pair of points x[i], x[j] in the point pattern contributes a factor H(d(x[i],x[j])) to the probability density, where d denotes distance and H is the pair interaction function.
这种模式是对相互作用过程的任何功能选择，H各向同性的互动。每对点x[i], x[j]在点模式作出贡献的一个因素H(d(x[i],x[j]))的概率密度，其中d表示距离和H是对交互功能。
The component beta is a (positive) scalar which determines the “base” intensity of the process.
组件beta（正）是一个标量，它决定了“碱基”的过程中强度。
In this implementation, H must be a step function. It is specified by the user in one of two ways.
在此实现中，H必须是一个阶跃函数。在以下两种方法之一，它是由用户指定的。
as a vector of values: If r is present, then r is assumed to give the locations of jumps in the function H, while the vector h gives the corresponding values of the function.
作为一个向量的值：如果r是本，然后r被假定，得到的位置的跳转函数H，而矢量h给出了相应的值的功能。
Specifically, the interaction function H(t) takes the value h[1] for distances t in the interval [0, r[1]); takes the value h[i] for distances t in the interval [r[i-1], r[i]) where i = 2, ..., n; and takes the value 1 for t >= r[n]. Here n denotes the length of r.
具体来说，交互功能H(t)的价值h[1]距离t的间隔[0, r[1]);的价值h[i]的距离t的时间间隔[r[i-1], r[i])其中i = 2, ..., n;以值1 t >= r[n]。这是n表示的长度r。
The components r and h must be numeric vectors of equal length. The r values must be strictly positive, and sorted in increasing order.
的组成部分r和h必须是数字向量的长度相等。 r值必须是严格正的，和递增的顺序排序。
The entries of h must be non-negative. If any entry of h is greater than 1, then the entry h[1] must be 0 (otherwise the specified process is non-existent).
h的条目必须为非负数。如果任何进入h是大于1的，然后进入h[1]必须是0（否则指定的进程是不存在的）。
Greatest efficiency is achieved if the values of r are equally spaced.
的值r是等距，实现最大的效率。
[Note: The usage of r and h has changed from the previous usage in spatstat versions 1.4-7 to 1.5-1, in which ascending order was not required, and in which the first entry of r had to be 0.]
[注：使用r和h已经改变，从以前使用spatstat版本1.4-7 1.5-1，其中升序不是必需的，并在其中第一个条目的r必须为0。]
as a stepfun object: If r is absent, then h must be an object of class "stepfun" specifying a step function. Such objects are created by stepfun.
作为一个stepfun对象：如果r是不存在的，那么h必须是一个对象类"stepfun"指定一个阶梯函数。这些对象创建的stepfun。
The stepfun object h must be right-continuous (which is the default using stepfun.)
的stepfun对象h必须是正确的，连续的（这是默认使用stepfun。）
The values of the step function must all be nonnegative. The values must all be less than 1 unless the function is identically zero on some initial interval [0,r). The rightmost value (the value of h(t) for large t) must be equal to 1.
阶跃函数的值都必须是非负的。的值都必须小于1，除非函数恒等于零一些初步的间隔[0,r)。最右边的值（该值h(t)大t的）必须等于1。
Greatest efficiency is achieved if the jumps (the “knots” of the step function) are equally spaced.
实现最大的效率，如果跳跃（的“节”的阶跃函数）是等间隔的。
The optional argument trend determines the spatial trend in the model, if it has one. It should be a function or image (or a list of such, if the model is multitype) to provide the value of the trend at an arbitrary point.
可选参数trend决定了空间的趋势模型中，如果有一个。它应是一个函数或图像（或这样的列表，如果模型是多类型的），以提供在任意点处的值的趋势。
trend given as a function: A trend function may be a function of any number of arguments, but the first two must be the x,y coordinates of a point. Auxiliary arguments may be passed to the trend function at the time of simulation, via the ... argument to rmh.
趋势的函数：可能是一个趋势函数的函数任意数量的参数，但前两个因素必须是x,y一个点的坐标。辅助参数可以传递给trend的功能在模拟的时间，通过...rmh参数。
The function must be vectorized. That is, it must be capable of accepting vector valued x and y arguments. Put another way, it must be capable of calculating the trend value at a number of points, simultaneously, and should return the vector of corresponding trend values.
该功能必须量化。也就是说，它必须能够接受x和y参数向量。换句话说，它必须是能够计算的趋势值在数量的点，同时，应该返回相应的趋势值的矢量。
An image (see im.object) provides the trend values at a grid of points in the observation window and determines the trend value at other points as the value at the nearest grid point.
一种图像（参见im.object）提供的观察窗中的网格点的趋势值，并确定在其他点作为最近的网格点处的值的趋势值。
Note that the trend or trends must be non-negative; no checking is done for this.
需要注意的是必须的趋势或趋势非负;此没有进行检查。
The optional argument w specifies the window in which the pattern is to be generated. If specified, it must be in a form which can be coerced to an object of class owin by as.owin.
可选参数w指定要生成模式的窗口中。如果指定的话，它必须是可以强制转换为一个对象类owinas.owin的形式。
The optional argument types specifies the possible types in a multitype point process. If the model being simulated is multitype, and types is not specified, then this vector defaults to 1:ntypes where ntypes is the number of types.
可选参数types指定一个多类型的点过程中可能的类型。如果被模拟的模型是多类型，types未指定，那么这个矢量默认1:ntypes其中ntypes类型的数量。
值----------Value----------
An object of class "rmhmodel", which is essentially a list of parameter values for the model.
类"rmhmodel"，它的一个目的是基本上是一个为模型的参数值列表。
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。
警告尊重的“查找”----------Warnings in Respect of “lookup”----------
For the lookup cif, the entries of the r component of par must be strictly positive and sorted into ascending order.
对于lookup到岸价格rpar组成部分，项目必须严格为正，按升序排列。
Note that if you specify the lookup pairwise interaction function via stepfun() the arguments x and y which are passed to stepfun() are slightly different from r and h: length(y) is equal to 1+length(x); the final entry of y must be equal to 1 — i.e. this value is explicitly supplied by the user rather than getting tacked on internally.
需要注意的是，如果你指定了lookup两两交互功能通过stepfun()参数x和y传递给stepfun()略有不同r 和h：length(y)是等于1+length(x)，y的最后一项必须等于1 - 也就是这个值是明确由用户提供的，而不是让上涨了内部。
The step function returned by stepfun() must be right continuous (this is the default behaviour of stepfun()) otherwise an error is given.
的步骤函数返回stepfun()必须是右连续的（这是默认的stepfun()），否则给出错误的行为。
（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.
Estimation of parameterized pair potentials of marked and non-marked Gibbsian point processes. Electronische Informationsverabeitung und Kybernetika 20, 270–278.
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----------
rmh, rmhcontrol, rmhstart, ppm, AreaInter, BadGey, DiggleGatesStibbard, DiggleGratton, Fiksel, Geyer, Hardcore, LennardJones, MultiHard, MultiStrauss, MultiStraussHard, PairPiece, Poisson, Softcore, Strauss, StraussHard, Triplets
rmh，rmhcontrol，rmhstart，ppm，AreaInter，BadGey，DiggleGatesStibbard，DiggleGratton，Fiksel，Geyer，Hardcore，LennardJones，MultiHard，MultiStrauss，MultiStraussHard，PairPiece，Poisson ，Softcore，Strauss，StraussHard，Triplets
实例----------Examples----------
# Strauss process:[施特劳斯的过程：]
mod01 <- rmhmodel(cif="strauss",par=list(beta=2,gamma=0.2,r=0.7),
w=c(0,10,0,10))
# The above could also be simulated using 'rStrauss'[以上也可以模拟使用rStrauss]
# Strauss with hardcore:[斯特劳斯的铁杆：]
mod04 <- rmhmodel(cif="straush",par=list(beta=2,gamma=0.2,r=0.7,hc=0.3),
w=owin(c(0,10),c(0,5)))
# Hard core:[硬核：]
mod05 <- rmhmodel(cif="hardcore",par=list(beta=2,hc=0.3),
w=square(5))
# Soft core:[软核：]
w <- square(10)
mod07 <- rmhmodel(cif="sftcr",
par=list(beta=0.8,sigma=0.1,kappa=0.5),
w=w)
# Area-interaction process:[区域互动的过程：]
mod42 <- rmhmodel(cif="areaint",par=list(beta=2,eta=1.6,r=0.7),
w=c(0,10,0,10))
# Baddeley-Geyer process:[巴德利Geyer的过程：]
mod99 <- rmhmodel(cif="badgey",par=list(beta=0.3,
gamma=c(0.2,1.8,2.4),r=c(0.035,0.07,0.14),sat=5),
w=unit.square())
# 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 <- rmhmodel(cif="straussm",
par=list(beta=beta,gamma=gmma,radii=r),
w=square(250))
# specify types[指定类型]
mod09 <- rmhmodel(cif="straussm",
par=list(beta=beta,gamma=gmma,radii=r),
w=square(250),
types=c("A", "B"))
# Multitype Hardcore:[多类型性交：]
rhc <- matrix(c(9.1,5.0,5.0,2.5),2,2)
mod08hard <- rmhmodel(cif="multihard",
par=list(beta=beta,hradii=rhc),
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 <- rmhmodel(cif="straushm",par=list(beta=beta,gamma=gmma,
iradii=ri,hradii=rhc),w=c(0,250,0,250),
trend=list(tr3,tr4))
# Triplets process:[三胞胎处理：]
mod11 <- rmhmodel(cif="triplets",par=list(beta=2,gamma=0.2,r=0.7),
w=c(0,10,0,10))
# 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 <- rmhmodel(cif="lookup",par=list(beta=4000,h=h,r=r),w=c(0,1,0,1))
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注：
注1：为了方便大家学习，本文档为生物统计家园网机器人LoveR翻译而成，仅供个人R语言学习参考使用，生物统计家园保留版权。
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发表于 2012-9-30 14:08:25
