Emark(spatstat)
Emark()所属R语言包：spatstat
Diagnostics for random marking
随机标记的诊断
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Estimate the summary functions E(r) and V(r) for a marked point pattern, proposed by Schlather et al (2004) as diagnostics for dependence between the points and the marks.
估计的汇总函数E(r)和V(r)显着的点格局，由“Schlather等（2004）提出的诊断和标记点之间的依赖关系。
用法----------Usage----------
Emark(X, r=NULL,
correction=c("isotropic", "Ripley", "translate"),
method="density", ..., normalise=FALSE)
Vmark(X, r=NULL,
correction=c("isotropic", "Ripley", "translate"),
method="density", ..., normalise=FALSE)
参数----------Arguments----------
参数：X
The observed point pattern. An object of class "ppp" or something acceptable to as.ppp. The pattern should have numeric marks.
观测点的模式。类的一个对象"ppp"或接受的as.ppp的东西。这种模式应该有数字的标记。
参数：r
Optional. Numeric vector. The values of the argument r at which the function E(r) or V(r) should be evaluated. There is a sensible default.
可选。数字矢量。的参数的值r功能E(r)或V(r)应该进行评估。有一个合理的默认。
参数：correction
A character vector containing any selection of the options "isotropic", "Ripley" or "translate". It specifies the edge correction(s) to be applied.
字符向量含有任何选择的选项"isotropic"，"Ripley"或"translate"。指定，边缘校正（S）。
参数：method
A character vector indicating the user's choice of density estimation technique to be used. Options are "density", "loess", "sm" and "smrep".
要使用的字符向量表示用户选择的密度估计技术。选项"density"，"loess"，"sm"和"smrep"。
参数：...
Arguments passed to the density estimation routine (density, loess or sm.density) selected by method.
传递参数的密度估计程序（density，loess或sm.density）选择method。
参数：normalise
IfTRUE, normalise the estimate of E(r) or V(r) so that it would have value equal to 1 if the marks are independent of the points.
如果TRUE，规范化的估计E(r)或V(r)因此，这将有值等于1，如果该商标是独立的点。
Details
详细信息----------Details----------
For a marked point process, Schlather et al (2004) defined the functions E(r) and V(r) to be the conditional mean and conditional variance of the mark attached to a typical random point, given that there exists another random point at a distance r away from it.
一个标记点的过程中，Schlather等人（2004）定义的功能E(r)和V(r)是一个典型的随机点的标记附加的条件均值和条件方差，由于还存在另一种随机点在距离r远离它。
More formally,
更确切地讲，
and
和
where E[0u] denotes the conditional expectation given that there are points of the process at the locations 0 and u separated by a distance r, and where M(0) denotes the mark attached to the point 0.
其中E[0u]表示给定的条件的期望的位置处的过程中，还有点0和u的距离r分离，以及其中M(0)表示标记附着点0。
These functions may serve as diagnostics for dependence between the points and the marks. If the points and marks are independent, then E(r) and V(r) should be constant (not depending on r). See Schlather et al (2004).
这些功能可作为诊断和标记点之间的依赖关系。如果点和标志是独立的，那么E(r)和V(r)应该是恒定的（不依赖于r）。见Schlather等人（2004）。
The argument X must be a point pattern (object of class "ppp") or any data that are acceptable to as.ppp. It must be a marked point pattern with numeric marks.
参数X必须是点模式（类的对象"ppp"）或任何数据到as.ppp是可以接受的。它必须是一个显着的点模式，用数字标记。
The argument r is the vector of values for the distance r at which k[f](r) is estimated.
参数r是矢量的距离r，k[f](r)估计值。
This algorithm assumes that X can be treated as a realisation of a stationary (spatially homogeneous) random spatial point process in the plane, observed through a bounded window. The window (which is specified in X as X$window) may have arbitrary shape.
此算法假定X可以被视为一个实现了一个固定的（的空间均匀）随机空间点在飞机上，观察到有界的窗口。窗口（中指定XX$window的）可以有任意形状的。
Biases due to edge effects are treated in the same manner as in Kest. The edge corrections implemented here are
边缘效应产生的偏差的处理中相同的方式，当在Kest。这里实现的边缘修正
isotropic/Ripley Ripley's isotropic correction (see Ripley, 1988; Ohser, 1983). This is implemented only for rectangular and polygonal windows (not for binary masks).
各向同性/ Ripley旅游Ripley的各向同性修正（见里普利，1988; Ohser，1983年）。实现此方法仅适用于矩形和多边形窗口（而不是二进制口罩）。
translate Translation correction (Ohser, 1983). Implemented for all window geometries, but slow for complex windows.
翻译的翻译的校正（Ohser，1983）。实现所有窗口的几何形状，但速度缓慢复杂的Windows。
Note that the estimator assumes the process is stationary (spatially homogeneous).
请注意，估计假设的过程是平稳的（空间均匀）。
The numerator and denominator of the mark correlation function (in the expression above) are estimated using density estimation techniques. The user can choose between
标记相关函数（在上面的表达式）的分子和分母的估计使用密度估计技术。用户可以选择
which uses the standard kernel density estimation routine density, and works only for evenly-spaced r values;
它使用标准的内核密度估计程序density，仅适用于均匀分布的r值;
which uses the function loess in the package modreg;
使用功能loess包中的modreg;
which uses the function sm.density in the package sm and is extremely slow;
使用功能sm.density包中的sm和极其缓慢;
which uses the function sm.density in the package sm and is relatively fast, but may require manual control of the smoothing parameter hmult.
使用该函数sm.density，软件包中sm和是比较快的，但可能需要手动控制的平滑参数hmult。
值----------Value----------
An object of class "fv" (see fv.object).
类的一个对象"fv"（见fv.object）。
Essentially a data frame containing numeric columns
本质上是一个数据框包含数字的列
参数：r
the values of the argument r at which the function E(r) or V(r) has been estimated
的参数的值r功能E(r)或V(r)已经估计
参数：theo
the theoretical, constant value of E(r) or V(r) when the marks attached to different points are independent
E(r)或V(r)连接到不同的点时，就标志着独立的理论，恒定值，
together with a column or columns named "iso" and/or "trans", according to the selected edge corrections. These columns contain estimates of the function E(r) or V(r) obtained by the edge corrections named.
连同一列或多列名为"iso"和/或"trans"，根据选定的边修正。这些列包含的功能估计E(r)或V(r)得到命名的边缘改正。
（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----------
Detecting dependence between marks and locations of marked point processes. Journal of the Royal Statistical Society, series B 66 (2004) 79-83.
参见----------See Also----------
Mark correlation markcorr, mark variogram markvario for numeric marks.
马克相关markcorr，标志变异函数markvario数字标记。
Mark connection function markconnect and multitype K-functions Kcross, Kdot for factor-valued marks.
马克连接功能markconnect和多类型K-函数Kcross，Kdot因子值的标记。
实例----------Examples----------
data(spruces)
plot(Emark(spruces))
E <- Emark(spruces, method="density", kernel="epanechnikov")
plot(Vmark(spruces))
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。
注3：如遇到不准确之处，请在本贴的后面进行回帖，我们会逐渐进行修订。
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发表于 2012-9-30 13:25:17
