Triplets(spatstat)
Triplets()所属R语言包：spatstat
The Triplet Point Process Model
三重点过程模型
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Creates an instance of Geyer's triplet interaction point process model which can then be fitted to point pattern data.
盖尔的三重互动点过程模型，然后可以配点模式的数据创建一个实例。
用法----------Usage----------
Triplets(r)
参数----------Arguments----------
参数：r
The interaction radius of the Triplets process
三胞胎过程的相互作用半径
Details
详细信息----------Details----------
The (stationary) Geyer triplet process (Geyer, 1999) with interaction radius r and parameters beta and gamma is the point process in which each point contributes a factor beta to the probability density of the point pattern, and each triplet of close points contributes a factor gamma to the density. A triplet of close points is a group of 3 points, each pair of which is closer than r units apart.
（固定）盖尔的三重工艺（赫耶尔1999）互动半径r和参数beta和gamma是点的过程中，每一个点的贡献的一个因素beta的概率密度的点的图案，和接近点每个三重贡献的因素gamma密度。接近点是一组3个点，这是每对接近r单位除了一个三元组。
Thus the probability density is
因此，概率密度是
where x[1],…,x[n] represent the points of the pattern, n(x) is the number of points in the pattern, s(x) is the number of unordered triples of points that are closer than r units apart, and alpha is the normalising constant.
x[1],…,x[n]代表的模式，n(x)是模式中的点的数量，s(x)是无序的三倍，接近r单位的点的数量外，和alpha是的标准化不变。
The interaction parameter gamma must be less than or equal to 1 so that this model describes an “ordered” or “inhibitive” pattern.
相互作用参数gamma必须小于或等于1，因此，该模型描述了一种“有序”或“抑制”的图案。
The nonstationary Triplets process is similar except that the contribution of each individual point x[i] is a function beta(x[i]) of location, rather than a constant beta.
非平稳的三胞胎过程是类似的，除了的贡献，每个点x[i]的功能beta(x[i])的位置，而不是一个常数测试。
The function ppm(), which fits point process models to point pattern data, requires an argument of class "interact" describing the interpoint interaction structure of the model to be fitted. The appropriate description of the Triplets process pairwise interaction is yielded by the function Triplets(). See the examples below.
的功能ppm()，适合点模式数据点过程模型，需要一个参数的类"interact"描述INTERPOINT互动结构的模型被安装。适当的描述三胞胎过程中对相互作用产生的功能Triplets()。请参见下面的例子。
Note the only argument is the interaction radius r. When r is fixed, the model becomes an exponential family. The canonical parameters log(beta) and log(gamma) are estimated by ppm(), not fixed in Triplets().
注意：唯一的参数是互动半径r。当r是固定的，的模型成为指数的家庭。规范参数log(beta)和log(gamma)估计ppm()，而不是固定在Triplets()。
值----------Value----------
An object of class "interact" describing the interpoint interaction structure of the Triplets process with interaction radius r.
类的一个对象"interact"描述INTERPOINT互动结构的的三胞胎过程的互动半径r。
（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----------
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----------
ppm, triplet.family, ppm.object
ppm，triplet.family，ppm.object
实例----------Examples----------
Triplets(r=0.1)
# prints a sensible description of itself[打印本身就是一个明智的描述]
## Not run: [＃不运行：]
ppm(cells, ~1, Triplets(r=0.1))
# fit the stationary Triplets process to `cells'[适合固定三胞胎过程中的单元]
## End(Not run)[＃（不执行）]
ppm(cells, ~polynom(x,y,3), Triplets(r=0.1))
# fit a nonstationary Triplets process with log-cubic polynomial trend[适合非平稳的三胞胎过程与数三次多项式趋势]
转载请注明:出自 生物统计家园网(http://www.biostatistic.net)。
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。
注2：由于是机器人自动翻译，难免有不准确之处，使用时仔细对照中、英文内容进行反复理解，可以帮助R语言的学习。
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发表于 2012-9-30 14:18:28
