pcf.ppp(spatstat)
pcf.ppp()所属R语言包：spatstat
Pair Correlation Function of Point Pattern
对相关函数点格局
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Estimates the pair correlation function of a point pattern using kernel methods.
估计对相关函数的一个点模式，使用内核的方法。
用法----------Usage----------
## S3 method for class 'ppp'
pcf(X, ..., r = NULL, kernel="epanechnikov", bw=NULL, stoyan=0.15,
correction=c("translate", "Ripley"))
参数----------Arguments----------
参数：X
A point pattern (object of class "ppp").
点模式（类的对象"ppp"）。
参数：r
Vector of values for the argument r at which g(r) should be evaluated. There is a sensible default.
向量参数的值r，g(r)应该进行评估。有一个合理的默认。
参数：kernel
Choice of smoothing kernel, passed to density.
选择平滑的内核，传递给density。
参数：bw
Bandwidth for smoothing kernel, passed to density.
带宽为平滑的内核，传递给density。
参数：...
Other arguments passed to the kernel density estimation function density.
其他参数传递给内核密度估计函数density。
参数：stoyan
Bandwidth coefficient; see Details.
带宽系数;详细。
参数：correction
Choice of edge correction.
选择边缘校正。
Details
详细信息----------Details----------
The pair correlation function g(r) is a summary of the dependence between points in a spatial point process. The best intuitive interpretation is the following: the probability p(r) of finding two points at locations x and y separated by a distance r is equal to
对相关函数g(r)是一个总结的空间点过程中的一个点之间的依赖关系。最好的直观解释是下列：的概率p(r)位置处找到两点x和y分离的距离r是等于
where lambda is the intensity of the point process. For a completely random (uniform Poisson) process, p(r) = lambda^2 so g(r) = 1.
lambda点过程的强度。对于一个完全随机的（统一的泊松分布）的过程中，p(r) = lambda^2所以g(r) = 1。
Formally, the pair correlation function of a stationary point process is defined by
从形式上看，对相关函数被定义为一个固定的点过程
where K'(r) is the derivative of K(r), the reduced second moment function (aka “Ripley's K function”) of the point process. See Kest for information about K(r).
K'(r) K(r)衍生，减少二阶矩（又名“里普利K函数”）的点处理功能。见Kest：信息K(r)的。
For a stationary Poisson process, the pair correlation function is identically equal to 1. Values g(r) < 1 suggest inhibition between points; values greater than 1 suggest clustering.
对于一个固定的泊松过程，对相关函数是相同的等于1。值g(r) < 1建议点与点之间的抑制;值大于1，表明聚类。
This routine computes an estimate of g(r) by the kernel smoothing method (Stoyan and Stoyan (1994), pages 284–285). By default, their recommendations are followed exactly.
此例程计算估计g(r)由内核平滑方法（斯托扬和斯托扬（1994），第284-285页）。默认情况下，完全按照他们的建议。
If correction="translate" then the translation correction is used. The estimate is given in equation (15.15), page 284 of Stoyan and Stoyan (1994).
如果correction="translate"然后翻译校正。该估计中给出的式（15.15），斯托扬和斯托扬（1994）的第284页。
If correction="Ripley" then Ripley's isotropic edge correction is used; the estimate is given in equation (15.18), page 285 of Stoyan and Stoyan (1994).
如果correction="Ripley"然后雷普利的各向同性的边缘校正;估计方程（15.18），斯托扬和斯托扬（1994）第285页。
If correction=c("translate", "Ripley") then both estimates will be computed.
如果correction=c("translate", "Ripley")然后将计算两个估计。
The choice of smoothing kernel is controlled by the argument kernel which is passed to density. The default is the Epanechnikov kernel, recommended by Stoyan and Stoyan (1994, page 285).
图像平滑用核的选择控制由参数kernel这是传递给density。默认值是叶帕涅奇尼科夫内核，建议，由斯托扬和斯托扬（1994年，第285页）。
The bandwidth of the smoothing kernel can be controlled by the argument bw. Its precise interpretation is explained in the documentation for density. For the Epanechnikov kernel, the argument bw is equivalent to h/sqrt(5).
可控制的参数bw的平滑核的带宽。其精确的解释中说明的文档density。对于叶帕涅奇尼科夫内核中，参数bw是相当于h/sqrt(5)。
Stoyan and Stoyan (1994, page 285) recommend using the Epanechnikov kernel with support [-h,h] chosen by the rule of thumn h = c/sqrt(lambda), where lambda is the (estimated) intensity of the point process, and c is a constant in the range from 0.1 to 0.2. See equation (15.16). If bw is missing, then this rule of thumb will be applied. The argument stoyan determines the value of c.
斯托扬和斯托扬（1994年，第285页）建议使用叶帕涅奇尼科夫内核的支持[-h,h]的规则选择的thumn h = c/sqrt(lambda)，其中lambda是（估计值）的点过程的强度，和c在从0.1至0.2的范围内是一个常数。见公式（15.16）。 bw如果丢失了，那么这个经验法则将被应用。的参数stoyan确定的价值c。
The argument r is the vector of values for the distance r at which g(r) should be evaluated. There is a sensible default. If it is specified, r must be a vector of increasing numbers starting from r[1] = 0, and max(r) must not exceed half the diameter of the window.
参数r是向量的值的距离r，g(r)应该进行评估。有一个合理的默认。如果它被指定，r必须是一个向量，越来越多的从r[1] = 0和max(r)不能超过窗口的直径的一半。
To compute a confidence band for the true value of the pair correlation function, use lohboot.
要计算置信带的真正价值，对相关功能，使用lohboot。
值----------Value----------
A function value table (object of class "fv"). Essentially a data frame containing the variables
函数值表（类的对象"fv"）。本质上是一个数据框包含的变量
参数：r
the vector of values of the argument r at which the pair correlation function g(r) has been estimated
矢量参数的值r对相关函数g(r)已经估计
参数：theo
vector of values equal to 1, the theoretical value of g(r) for the Poisson process
矢量的值等于1，为泊松过程的理论价值g(r)
参数：trans
vector of values of g(r) estimated by translation correction
向量的值g(r)估计翻译校正
参数：iso
vector of values of g(r) estimated by Ripley isotropic correction
g(r)估计里普利各向同性修正值向量
as required.
根据需要。
（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----------
Fractals, random shapes and point fields: methods of geometrical statistics. John Wiley and Sons.
参见----------See Also----------
Kest, pcf, density, lohboot.
Kest，pcf，density，lohboot。
实例----------Examples----------
data(simdat)
p <- pcf(simdat)
plot(p, main="pair correlation function for simdat")
# indicates inhibition at distances r < 0.3[指示禁止在距离r <0.3]
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 13:53:57
