pcf.fv(spatstat)
pcf.fv()所属R语言包：spatstat
Pair Correlation Function obtained from K Function
对相关函数获得从K功能
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, given an estimate of the K function.
估计的点模式，对相关函数的K函数的估计。
用法----------Usage----------
## S3 method for class 'fv'
pcf(X, ..., method="c")
参数----------Arguments----------
参数：X
An estimate of the K function or one of its variants. An object of class "fv".
估计K函数或它的变种。对象的类"fv"。
参数：...
Arguments controlling the smoothing spline function smooth.spline.
参数控制的平滑样条函数smooth.spline。
参数：method
Letter "a", "b", "c" or "d" indicating the method for deriving the pair correlation function from the K function.
字母"a"，"b"，"c"或"d"表明K功能获得对相关功能的方法。
Details
详细信息----------Details----------
The pair correlation function of a stationary point process is
是一个固定的点过程中对相关函数
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). 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.
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。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。
We also apply the same definition to other variants of the classical K function, such as the multitype K functions (see Kcross, Kdot) and the inhomogeneous K function (see Kinhom). For all these variants, the benchmark value of K(r) = pi * r^2 corresponds to g(r) = 1.
我们也适用相同的定义，其他变种古典K函数，如多类型K函数（参见Kcross，Kdot）和不均匀K 函数（见Kinhom）。对于所有这些变种，K(r) = pi * r^2对应的基准值到g(r) = 1。
This routine computes an estimate of g(r) from an estimate of K(r) or its variants, using smoothing splines to approximate the derivative. It is a method for the generic function pcf for the class "fv".
该程序计算的估计g(r)的估计K(r)或它的变种，使用平滑样条近似的导数。它是一种通用功能pcf类"fv"。
The argument X should be an estimated K function, given as a function value table (object of class "fv", see fv.object). This object should be the value returned by Kest, Kcross, Kmulti or Kinhom.
参数X应该是估计K功能，给出的函数值表（对象类"fv"，看到fv.object）。这个对象应该是的返回值Kest，Kcross，Kmulti或Kinhom。
The smoothing spline operations are performed by smooth.spline and predict.smooth.spline from the modreg library. Four numerical methods are available:
样条函数操作都是由smooth.spline和predict.smooth.splinemodreg库。四个数值方法可供选择：
"a" apply smoothing to K(r), estimate its derivative, and plug in to the formula above;
“”适用于平滑度K(r)，估计它的衍生物，插在上面的公式;
"b" apply smoothing to Y(r) = K(r)/(2 * pi * r) constraining Y(0) = 0, estimate the derivative of Y, and solve;
“B”适用于平滑Y(r) = K(r)/(2 * pi * r)制约Y(0) = 0，估计Y衍生，和解决;
"c" apply smoothing to Y(r) = K(r)/(pi * r^2) constraining Z(0)=1, estimate its derivative, and solve.
“C”申请平滑Y(r) = K(r)/(pi * r^2)制约Z(0)=1，估计及其衍生物，和解决的问题。
"d" apply smoothing to V(r) = sqrt(K(r)), estimate its derivative, and solve.
“d”的申请平滑V(r) = sqrt(K(r))，估计及其衍生物，并解决。
Method "c" seems to be the best at suppressing variability for small values of r. However it effectively constrains g(0) = 1. If the point pattern seems to have inhibition at small distances, you may wish to experiment with method "b" which effectively constrains g(0)=0. Method "a" seems comparatively unreliable.
方法"c"似乎是最好的抑制变异小值r。然而，它有效地限制了g(0) = 1。如果点模式似乎有抑制作用小的距离，你不妨试验，与方法"b"这有效地限制了g(0)=0。方法"a"似乎比较不可靠的。
Useful arguments to control the splines include the smoothing tradeoff parameter spar and the degrees of freedom df. See smooth.spline for details.
有用的参数，来控制花键包括平滑权衡参数spar和度的自由df。见smooth.spline的详细信息。
值----------Value----------
A function value table (object of class "fv", see fv.object) representing a pair correlation function.
函数值表（对象类"fv"，看到fv.object）一对相关功能。
Essentially a data frame containing (at least) the variables
本质上是一个数据框包含的变量（至少）
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：pcf
vector of values of g(r)
矢量值g(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----------
Stochastic geometry and its applications. 2nd edition. Springer Verlag.
Fractals, random shapes and point fields: methods of geometrical statistics. John Wiley and Sons.
参见----------See Also----------
pcf, pcf.ppp, Kest, Kinhom, Kcross, Kdot, Kmulti, alltypes, smooth.spline, predict.smooth.spline
pcf，pcf.ppp，Kest，Kinhom，Kcross，Kdot，Kmulti，alltypes，smooth.spline，predict.smooth.spline
实例----------Examples----------
# univariate point pattern[单变量点模式]
data(simdat)
K <- Kest(simdat)
p <- pcf.fv(K, spar=0.5, method="b")
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：
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 13:53:51
